├── .gitignore ├── README.md ├── Swin-Transformer-Classification ├── .circleci │ └── config.yml ├── .dev_scripts │ └── benchmark_regression │ │ ├── 1-benchmark_valid.py │ │ ├── 2-benchmark_test.py │ │ ├── 3-benchmark_train.py │ │ └── bench_train.yml ├── .gitattributes ├── .github │ ├── ISSUE_TEMPLATE │ │ ├── 1_bug-report.yml │ │ ├── 2_feature-request.yml │ │ ├── 3_bug-report_zh.yml │ │ ├── 4_feature-request_zh.yml │ │ └── config.yml │ ├── pull_request_template.md │ └── workflows │ │ ├── build.yml │ │ ├── deploy.yml │ │ ├── lint.yml │ │ └── test-mim.yml ├── .gitignore ├── .pre-commit-config.yaml ├── .readthedocs.yml ├── CITATION.cff ├── CONTRIBUTING.md ├── LICENSE ├── MANIFEST.in ├── README.md ├── README_zh-CN.md ├── configs │ ├── _base_ │ │ ├── datasets │ │ │ ├── cifar100_bs16.py │ │ │ ├── cifar10_bs16.py │ │ │ ├── cub_bs8_384.py │ │ │ ├── cub_bs8_448.py │ │ │ ├── imagenet21k_bs128.py │ │ │ ├── imagenet_bs128_poolformer_medium_224.py │ │ │ ├── imagenet_bs128_poolformer_small_224.py │ │ │ ├── imagenet_bs256_rsb_a12.py │ │ │ ├── imagenet_bs256_rsb_a3.py │ │ │ ├── imagenet_bs32.py │ │ │ ├── imagenet_bs32_pil_bicubic.py │ │ │ ├── imagenet_bs32_pil_resize.py │ │ │ ├── imagenet_bs64.py │ │ │ ├── imagenet_bs64_autoaug.py │ │ │ ├── imagenet_bs64_convmixer_224.py │ │ │ ├── imagenet_bs64_mixer_224.py │ │ │ ├── imagenet_bs64_pil_resize.py │ │ │ ├── imagenet_bs64_pil_resize_autoaug.py │ │ │ ├── imagenet_bs64_swin_224.py │ │ │ ├── imagenet_bs64_swin_256.py │ │ │ ├── imagenet_bs64_swin_384.py │ │ │ ├── imagenet_bs64_t2t_224.py │ │ │ ├── pipelines │ │ │ │ ├── auto_aug.py │ │ │ │ └── rand_aug.py │ │ │ ├── stanford_cars_bs8_448.py │ │ │ └── voc_bs16.py │ │ ├── default_runtime.py │ │ ├── models │ │ │ ├── conformer │ │ │ │ ├── base-p16.py │ │ │ │ ├── small-p16.py │ │ │ │ ├── small-p32.py │ │ │ │ └── tiny-p16.py │ │ │ ├── convmixer │ │ │ │ ├── convmixer-1024-20.py │ │ │ │ ├── convmixer-1536-20.py │ │ │ │ └── convmixer-768-32.py │ │ │ ├── convnext │ │ │ │ ├── convnext-base.py │ │ │ │ ├── convnext-large.py │ │ │ │ ├── convnext-small.py │ │ │ │ ├── convnext-tiny.py │ │ │ │ └── convnext-xlarge.py │ │ │ ├── densenet │ │ │ │ ├── densenet121.py │ │ │ │ ├── densenet161.py │ │ │ │ ├── densenet169.py │ │ │ │ └── densenet201.py │ │ │ ├── efficientnet_b0.py │ │ │ ├── efficientnet_b1.py │ │ │ ├── efficientnet_b2.py │ │ │ ├── efficientnet_b3.py │ │ │ ├── efficientnet_b4.py │ │ │ ├── efficientnet_b5.py │ │ │ ├── efficientnet_b6.py │ │ │ ├── efficientnet_b7.py │ │ │ ├── efficientnet_b8.py │ │ │ ├── efficientnet_em.py │ │ │ ├── efficientnet_es.py │ │ │ ├── hornet │ │ │ │ ├── hornet-base-gf.py │ │ │ │ ├── hornet-base.py │ │ │ │ ├── hornet-large-gf.py │ │ │ │ ├── hornet-large-gf384.py │ │ │ │ ├── hornet-large.py │ │ │ │ ├── hornet-small-gf.py │ │ │ │ ├── hornet-small.py │ │ │ │ ├── hornet-tiny-gf.py │ │ │ │ └── hornet-tiny.py │ │ │ ├── hrnet │ │ │ │ ├── hrnet-w18.py │ │ │ │ ├── hrnet-w30.py │ │ │ │ ├── hrnet-w32.py │ │ │ │ ├── hrnet-w40.py │ │ │ │ ├── hrnet-w44.py │ │ │ │ ├── hrnet-w48.py │ │ │ │ └── hrnet-w64.py │ │ │ ├── mlp_mixer_base_patch16.py │ │ │ ├── mlp_mixer_large_patch16.py │ │ │ ├── mobilenet_v2_1x.py │ │ │ ├── mobilenet_v3_large_imagenet.py │ │ │ ├── mobilenet_v3_small_cifar.py │ │ │ ├── mobilenet_v3_small_imagenet.py │ │ │ ├── mvit │ │ │ │ ├── mvitv2-base.py │ │ │ │ ├── mvitv2-large.py │ │ │ │ ├── mvitv2-small.py │ │ │ │ └── mvitv2-tiny.py │ │ │ ├── poolformer │ │ │ │ ├── poolformer_m36.py │ │ │ │ ├── poolformer_m48.py │ │ │ │ ├── poolformer_s12.py │ │ │ │ ├── poolformer_s24.py │ │ │ │ └── poolformer_s36.py │ │ │ ├── regnet │ │ │ │ ├── regnetx_1.6gf.py │ │ │ │ ├── regnetx_12gf.py │ │ │ │ ├── regnetx_3.2gf.py │ │ │ │ ├── regnetx_4.0gf.py │ │ │ │ ├── regnetx_400mf.py │ │ │ │ ├── regnetx_6.4gf.py │ │ │ │ ├── regnetx_8.0gf.py │ │ │ │ └── regnetx_800mf.py │ │ │ ├── repmlp-base_224.py │ │ │ ├── repvgg-A0_in1k.py │ │ │ ├── repvgg-B3_lbs-mixup_in1k.py │ │ │ ├── res2net101-w26-s4.py │ │ │ ├── res2net50-w14-s8.py │ │ │ ├── res2net50-w26-s4.py │ │ │ ├── res2net50-w26-s6.py │ │ │ ├── res2net50-w26-s8.py │ │ │ ├── res2net50-w48-s2.py │ │ │ ├── resnest101.py │ │ │ ├── resnest200.py │ │ │ ├── resnest269.py │ │ │ ├── resnest50.py │ │ │ ├── resnet101.py │ │ │ ├── resnet101_cifar.py │ │ │ ├── resnet152.py │ │ │ ├── resnet152_cifar.py │ │ │ ├── resnet18.py │ │ │ ├── resnet18_cifar.py │ │ │ ├── resnet34.py │ │ │ ├── resnet34_cifar.py │ │ │ ├── resnet34_gem.py │ │ │ ├── resnet50.py │ │ │ ├── resnet50_cifar.py │ │ │ ├── resnet50_cifar_cutmix.py │ │ │ ├── resnet50_cifar_mixup.py │ │ │ ├── resnet50_cutmix.py │ │ │ ├── resnet50_label_smooth.py │ │ │ ├── resnet50_mixup.py │ │ │ ├── resnetv1c50.py │ │ │ ├── resnetv1d101.py │ │ │ ├── resnetv1d152.py │ │ │ ├── resnetv1d50.py │ │ │ ├── resnext101_32x4d.py │ │ │ ├── resnext101_32x8d.py │ │ │ ├── resnext152_32x4d.py │ │ │ ├── resnext50_32x4d.py │ │ │ ├── seresnet101.py │ │ │ ├── seresnet50.py │ │ │ ├── seresnext101_32x4d.py │ │ │ ├── seresnext50_32x4d.py │ │ │ ├── shufflenet_v1_1x.py │ │ │ ├── shufflenet_v2_1x.py │ │ │ ├── swin_transformer │ │ │ │ ├── base_224.py │ │ │ │ ├── base_384.py │ │ │ │ ├── large_224.py │ │ │ │ ├── large_384.py │ │ │ │ ├── small_224.py │ │ │ │ └── tiny_224.py │ │ │ ├── swin_transformer_v2 │ │ │ │ ├── base_256.py │ │ │ │ ├── base_384.py │ │ │ │ ├── large_256.py │ │ │ │ ├── large_384.py │ │ │ │ ├── small_256.py │ │ │ │ └── tiny_256.py │ │ │ ├── t2t-vit-t-14.py │ │ │ ├── t2t-vit-t-19.py │ │ │ ├── t2t-vit-t-24.py │ │ │ ├── tnt_s_patch16_224.py │ │ │ ├── twins_pcpvt_base.py │ │ │ ├── twins_svt_base.py │ │ │ ├── van │ │ │ │ ├── van_b0.py │ │ │ │ ├── van_b1.py │ │ │ │ ├── van_b2.py │ │ │ │ ├── van_b3.py │ │ │ │ ├── van_b4.py │ │ │ │ ├── van_b5.py │ │ │ │ ├── van_b6.py │ │ │ │ ├── van_base.py │ │ │ │ ├── van_large.py │ │ │ │ ├── van_small.py │ │ │ │ └── van_tiny.py │ │ │ ├── vgg11.py │ │ │ ├── vgg11bn.py │ │ │ ├── vgg13.py │ │ │ ├── vgg13bn.py │ │ │ ├── vgg16.py │ │ │ ├── vgg16bn.py │ │ │ ├── vgg19.py │ │ │ ├── vgg19bn.py │ │ │ ├── vit-base-p16.py │ │ │ ├── vit-base-p32.py │ │ │ ├── vit-large-p16.py │ │ │ ├── vit-large-p32.py │ │ │ └── wide-resnet50.py │ │ └── schedules │ │ │ ├── cifar10_bs128.py │ │ │ ├── cub_bs64.py │ │ │ ├── imagenet_bs1024_adamw_conformer.py │ │ │ ├── imagenet_bs1024_adamw_swin.py │ │ │ ├── imagenet_bs1024_coslr.py │ │ │ ├── imagenet_bs1024_linearlr_bn_nowd.py │ │ │ ├── imagenet_bs2048.py │ │ │ ├── imagenet_bs2048_AdamW.py │ │ │ ├── imagenet_bs2048_coslr.py │ │ │ ├── imagenet_bs2048_rsb.py │ │ │ ├── imagenet_bs256.py │ │ │ ├── imagenet_bs256_140e.py │ │ │ ├── imagenet_bs256_200e_coslr_warmup.py │ │ │ ├── imagenet_bs256_coslr.py │ │ │ ├── imagenet_bs256_epochstep.py │ │ │ ├── imagenet_bs4096_AdamW.py │ │ │ └── stanford_cars_bs8.py │ ├── conformer │ │ ├── README.md │ │ ├── conformer-base-p16_8xb128_in1k.py │ │ ├── conformer-small-p16_8xb128_in1k.py │ │ ├── conformer-small-p32_8xb128_in1k.py │ │ ├── conformer-tiny-p16_8xb128_in1k.py │ │ └── metafile.yml │ ├── convmixer │ │ ├── README.md │ │ ├── convmixer-1024-20_10xb64_in1k.py │ │ ├── convmixer-1536-20_10xb64_in1k.py │ │ ├── convmixer-768-32_10xb64_in1k.py │ │ └── metafile.yml │ ├── convnext │ │ ├── README.md │ │ ├── convnext-base_32xb128_in1k.py │ │ ├── convnext-large_64xb64_in1k.py │ │ ├── convnext-small_32xb128_in1k.py │ │ ├── convnext-tiny_32xb128_in1k.py │ │ ├── convnext-xlarge_64xb64_in1k.py │ │ └── metafile.yml │ ├── cspnet │ │ ├── README.md │ │ ├── cspdarknet50_8xb32_in1k.py │ │ ├── cspresnet50_8xb32_in1k.py │ │ ├── cspresnext50_8xb32_in1k.py │ │ └── metafile.yml │ ├── csra │ │ ├── README.md │ │ ├── metafile.yml │ │ └── resnet101-csra_1xb16_voc07-448px.py │ ├── deit │ │ ├── README.md │ │ ├── deit-base-distilled_ft-16xb32_in1k-384px.py │ │ ├── deit-base-distilled_pt-16xb64_in1k.py │ │ ├── deit-base_ft-16xb32_in1k-384px.py │ │ ├── deit-base_pt-16xb64_in1k.py │ │ ├── deit-small-distilled_pt-4xb256_in1k.py │ │ ├── deit-small_pt-4xb256_in1k.py │ │ ├── deit-tiny-distilled_pt-4xb256_in1k.py │ │ ├── deit-tiny_pt-4xb256_in1k.py │ │ └── metafile.yml │ ├── densenet │ │ ├── README.md │ │ ├── densenet121_4xb256_in1k.py │ │ ├── densenet161_4xb256_in1k.py │ │ ├── densenet169_4xb256_in1k.py │ │ ├── densenet201_4xb256_in1k.py │ │ └── metafile.yml │ ├── efficientformer │ │ ├── README.md │ │ ├── efficientformer-l1_8xb128_in1k.py │ │ ├── efficientformer-l3_8xb128_in1k.py │ │ ├── efficientformer-l7_8xb128_in1k.py │ │ └── metafile.yml │ ├── efficientnet │ │ ├── README.md │ │ ├── efficientnet-b0_8xb32-01norm_in1k.py │ │ ├── efficientnet-b0_8xb32_in1k.py │ │ ├── efficientnet-b1_8xb32-01norm_in1k.py │ │ ├── efficientnet-b1_8xb32_in1k.py │ │ ├── efficientnet-b2_8xb32-01norm_in1k.py │ │ ├── efficientnet-b2_8xb32_in1k.py │ │ ├── efficientnet-b3_8xb32-01norm_in1k.py │ │ ├── efficientnet-b3_8xb32_in1k.py │ │ ├── efficientnet-b4_8xb32-01norm_in1k.py │ │ ├── efficientnet-b4_8xb32_in1k.py │ │ ├── efficientnet-b5_8xb32-01norm_in1k.py │ │ ├── efficientnet-b5_8xb32_in1k.py │ │ ├── efficientnet-b6_8xb32-01norm_in1k.py │ │ ├── efficientnet-b6_8xb32_in1k.py │ │ ├── efficientnet-b7_8xb32-01norm_in1k.py │ │ ├── efficientnet-b7_8xb32_in1k.py │ │ ├── efficientnet-b8_8xb32-01norm_in1k.py │ │ ├── efficientnet-b8_8xb32_in1k.py │ │ ├── efficientnet-em_8xb32-01norm_in1k.py │ │ ├── efficientnet-es_8xb32-01norm_in1k.py │ │ └── metafile.yml │ ├── fp16 │ │ ├── resnet50_b32x8_fp16_dynamic_imagenet.py │ │ └── resnet50_b32x8_fp16_imagenet.py │ ├── hornet │ │ ├── README.md │ │ ├── hornet-base-gf_8xb64_in1k.py │ │ ├── hornet-base_8xb64_in1k.py │ │ ├── hornet-small-gf_8xb64_in1k.py │ │ ├── hornet-small_8xb64_in1k.py │ │ ├── hornet-tiny-gf_8xb128_in1k.py │ │ ├── hornet-tiny_8xb128_in1k.py │ │ └── metafile.yml │ ├── hrnet │ │ ├── README.md │ │ ├── hrnet-w18_4xb32_in1k.py │ │ ├── hrnet-w30_4xb32_in1k.py │ │ ├── hrnet-w32_4xb32_in1k.py │ │ ├── hrnet-w40_4xb32_in1k.py │ │ ├── hrnet-w44_4xb32_in1k.py │ │ ├── hrnet-w48_4xb32_in1k.py │ │ ├── hrnet-w64_4xb32_in1k.py │ │ └── metafile.yml │ ├── lenet │ │ ├── README.md │ │ └── lenet5_mnist.py │ ├── mlp_mixer │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── mlp-mixer-base-p16_64xb64_in1k.py │ │ └── mlp-mixer-large-p16_64xb64_in1k.py │ ├── mobilenet_v2 │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── mobilenet-v2_8xb32_in1k.py │ │ └── mobilenet_v2_b32x8_imagenet.py │ ├── mobilenet_v3 │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── mobilenet-v3-large_8xb32_in1k.py │ │ ├── mobilenet-v3-small_8xb16_cifar10.py │ │ ├── mobilenet-v3-small_8xb32_in1k.py │ │ ├── mobilenet_v3_large_imagenet.py │ │ ├── mobilenet_v3_small_cifar.py │ │ └── mobilenet_v3_small_imagenet.py │ ├── mvit │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── mvitv2-base_8xb256_in1k.py │ │ ├── mvitv2-large_8xb256_in1k.py │ │ ├── mvitv2-small_8xb256_in1k.py │ │ └── mvitv2-tiny_8xb256_in1k.py │ ├── poolformer │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── poolformer-m36_32xb128_in1k.py │ │ ├── poolformer-m48_32xb128_in1k.py │ │ ├── poolformer-s12_32xb128_in1k.py │ │ ├── poolformer-s24_32xb128_in1k.py │ │ └── poolformer-s36_32xb128_in1k.py │ ├── regnet │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── regnetx-1.6gf_8xb128_in1k.py │ │ ├── regnetx-12gf_8xb64_in1k.py │ │ ├── regnetx-3.2gf_8xb64_in1k.py │ │ ├── regnetx-4.0gf_8xb64_in1k.py │ │ ├── regnetx-400mf_8xb128_in1k.py │ │ ├── regnetx-6.4gf_8xb64_in1k.py │ │ ├── regnetx-8.0gf_8xb64_in1k.py │ │ └── regnetx-800mf_8xb128_in1k.py │ ├── repmlp │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── repmlp-base_8xb64_in1k-256px.py │ │ ├── repmlp-base_8xb64_in1k.py │ │ ├── repmlp-base_delopy_8xb64_in1k.py │ │ └── repmlp-base_deploy_8xb64_in1k-256px.py │ ├── repvgg │ │ ├── README.md │ │ ├── deploy │ │ │ ├── repvgg-A0_deploy_4xb64-coslr-120e_in1k.py │ │ │ ├── repvgg-A1_deploy_4xb64-coslr-120e_in1k.py │ │ │ ├── repvgg-A2_deploy_4xb64-coslr-120e_in1k.py │ │ │ ├── repvgg-B0_deploy_4xb64-coslr-120e_in1k.py │ │ │ ├── repvgg-B1_deploy_4xb64-coslr-120e_in1k.py │ │ │ ├── repvgg-B1g2_deploy_4xb64-coslr-120e_in1k.py │ │ │ ├── repvgg-B1g4_deploy_4xb64-coslr-120e_in1k.py │ │ │ ├── repvgg-B2_deploy_4xb64-coslr-120e_in1k.py │ │ │ ├── repvgg-B2g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py │ │ │ ├── repvgg-B3_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py │ │ │ ├── repvgg-B3g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py │ │ │ └── repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py │ │ ├── metafile.yml │ │ ├── repvgg-A0_4xb64-coslr-120e_in1k.py │ │ ├── repvgg-A1_4xb64-coslr-120e_in1k.py │ │ ├── repvgg-A2_4xb64-coslr-120e_in1k.py │ │ ├── repvgg-B0_4xb64-coslr-120e_in1k.py │ │ ├── repvgg-B1_4xb64-coslr-120e_in1k.py │ │ ├── repvgg-B1g2_4xb64-coslr-120e_in1k.py │ │ ├── repvgg-B1g4_4xb64-coslr-120e_in1k.py │ │ ├── repvgg-B2_4xb64-coslr-120e_in1k.py │ │ ├── repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py │ │ ├── repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py │ │ ├── repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py │ │ └── repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py │ ├── res2net │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── res2net101-w26-s4_8xb32_in1k.py │ │ ├── res2net50-w14-s8_8xb32_in1k.py │ │ └── res2net50-w26-s8_8xb32_in1k.py │ ├── resnest │ │ ├── README.md │ │ ├── resnest101_32xb64_in1k.py │ │ ├── resnest101_b64x32_imagenet.py │ │ ├── resnest200_64xb32_in1k.py │ │ ├── resnest200_b32x64_imagenet.py │ │ ├── resnest269_64xb32_in1k.py │ │ ├── resnest269_b32x64_imagenet.py │ │ ├── resnest50_32xb64_in1k.py │ │ └── resnest50_b64x32_imagenet.py │ ├── resnet │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── resnet101_8xb16_cifar10.py │ │ ├── resnet101_8xb32_in1k.py │ │ ├── resnet101_b16x8_cifar10.py │ │ ├── resnet101_b32x8_imagenet.py │ │ ├── resnet152_8xb16_cifar10.py │ │ ├── resnet152_8xb32_in1k.py │ │ ├── resnet152_b16x8_cifar10.py │ │ ├── resnet152_b32x8_imagenet.py │ │ ├── resnet18_8xb16_cifar10.py │ │ ├── resnet18_8xb32_in1k.py │ │ ├── resnet18_b16x8_cifar10.py │ │ ├── resnet18_b32x8_imagenet.py │ │ ├── resnet34_8xb16_cifar10.py │ │ ├── resnet34_8xb32_in1k.py │ │ ├── resnet34_b16x8_cifar10.py │ │ ├── resnet34_b32x8_imagenet.py │ │ ├── resnet50_32xb64-warmup-coslr_in1k.py │ │ ├── resnet50_32xb64-warmup-lbs_in1k.py │ │ ├── resnet50_32xb64-warmup_in1k.py │ │ ├── resnet50_8xb128_coslr-90e_in21k.py │ │ ├── resnet50_8xb16-mixup_cifar10.py │ │ ├── resnet50_8xb16_cifar10.py │ │ ├── resnet50_8xb16_cifar100.py │ │ ├── resnet50_8xb256-rsb-a1-600e_in1k.py │ │ ├── resnet50_8xb256-rsb-a2-300e_in1k.py │ │ ├── resnet50_8xb256-rsb-a3-100e_in1k.py │ │ ├── resnet50_8xb32-coslr-preciseBN_in1k.py │ │ ├── resnet50_8xb32-coslr_in1k.py │ │ ├── resnet50_8xb32-cutmix_in1k.py │ │ ├── resnet50_8xb32-fp16-dynamic_in1k.py │ │ ├── resnet50_8xb32-fp16_in1k.py │ │ ├── resnet50_8xb32-lbs_in1k.py │ │ ├── resnet50_8xb32-mixup_in1k.py │ │ ├── resnet50_8xb32_in1k.py │ │ ├── resnet50_8xb8_cars.py │ │ ├── resnet50_8xb8_cub.py │ │ ├── resnet50_b16x8_cifar10.py │ │ ├── resnet50_b16x8_cifar100.py │ │ ├── resnet50_b16x8_cifar10_mixup.py │ │ ├── resnet50_b32x8_coslr_imagenet.py │ │ ├── resnet50_b32x8_cutmix_imagenet.py │ │ ├── resnet50_b32x8_imagenet.py │ │ ├── resnet50_b32x8_label_smooth_imagenet.py │ │ ├── resnet50_b32x8_mixup_imagenet.py │ │ ├── resnet50_b64x32_warmup_coslr_imagenet.py │ │ ├── resnet50_b64x32_warmup_imagenet.py │ │ ├── resnet50_b64x32_warmup_label_smooth_imagenet.py │ │ ├── resnetv1c101_8xb32_in1k.py │ │ ├── resnetv1c152_8xb32_in1k.py │ │ ├── resnetv1c50_8xb32_in1k.py │ │ ├── resnetv1d101_8xb32_in1k.py │ │ ├── resnetv1d101_b32x8_imagenet.py │ │ ├── resnetv1d152_8xb32_in1k.py │ │ ├── resnetv1d152_b32x8_imagenet.py │ │ ├── resnetv1d50_8xb32_in1k.py │ │ └── resnetv1d50_b32x8_imagenet.py │ ├── resnext │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── resnext101-32x4d_8xb32_in1k.py │ │ ├── resnext101-32x8d_8xb32_in1k.py │ │ ├── resnext101_32x4d_b32x8_imagenet.py │ │ ├── resnext101_32x8d_b32x8_imagenet.py │ │ ├── resnext152-32x4d_8xb32_in1k.py │ │ ├── resnext152_32x4d_b32x8_imagenet.py │ │ ├── resnext50-32x4d_8xb32_in1k.py │ │ └── resnext50_32x4d_b32x8_imagenet.py │ ├── seresnet │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── seresnet101_8xb32_in1k.py │ │ ├── seresnet101_b32x8_imagenet.py │ │ ├── seresnet50_8xb32_in1k.py │ │ ├── seresnet50_b32x8_imagenet.py │ │ ├── seresnext101-32x4d_8xb32_in1k.py │ │ ├── seresnext101_32x4d_b32x8_imagenet.py │ │ ├── seresnext50-32x4d_8xb32_in1k.py │ │ └── seresnext50_32x4d_b32x8_imagenet.py │ ├── shufflenet_v1 │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── shufflenet-v1-1x_16xb64_in1k.py │ │ └── shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet.py │ ├── shufflenet_v2 │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── shufflenet-v2-1x_16xb64_in1k.py │ │ └── shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py │ ├── swin_transformer │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── swin-base_16xb64_in1k-384px.py │ │ ├── swin-base_16xb64_in1k.py │ │ ├── swin-large_16xb64_in1k-384px.py │ │ ├── swin-large_16xb64_in1k.py │ │ ├── swin-large_8xb8_cub_384px.py │ │ ├── swin-small_16xb64_in1k.py │ │ ├── swin-tiny_16xb64_in1k.py │ │ ├── swin_base_224_b16x64_300e_imagenet.py │ │ ├── swin_base_384_evalonly_imagenet.py │ │ ├── swin_large_224_evalonly_imagenet.py │ │ ├── swin_large_384_evalonly_imagenet.py │ │ ├── swin_small_224_b16x64_300e_imagenet.py │ │ └── swin_tiny_224_b16x64_300e_imagenet.py │ ├── swin_transformer_v2 │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── swinv2-base-w16_16xb64_in1k-256px.py │ │ ├── swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py │ │ ├── swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py │ │ ├── swinv2-base-w8_16xb64_in1k-256px.py │ │ ├── swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py │ │ ├── swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py │ │ ├── swinv2-small-w16_16xb64_in1k-256px.py │ │ ├── swinv2-small-w8_16xb64_in1k-256px.py │ │ ├── swinv2-tiny-w16_16xb64_in1k-256px.py │ │ └── swinv2-tiny-w8_16xb64_in1k-256px.py │ ├── t2t_vit │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── t2t-vit-t-14_8xb64_in1k.py │ │ ├── t2t-vit-t-19_8xb64_in1k.py │ │ └── t2t-vit-t-24_8xb64_in1k.py │ ├── tnt │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── tnt-s-p16_16xb64_in1k.py │ │ └── tnt_s_patch16_224_evalonly_imagenet.py │ ├── twins │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── twins-pcpvt-base_8xb128_in1k.py │ │ ├── twins-pcpvt-large_16xb64_in1k.py │ │ ├── twins-pcpvt-small_8xb128_in1k.py │ │ ├── twins-svt-base_8xb128_in1k.py │ │ ├── twins-svt-large_16xb64_in1k.py │ │ └── twins-svt-small_8xb128_in1k.py │ ├── van │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── van-b0_8xb128_in1k.py │ │ ├── van-b1_8xb128_in1k.py │ │ ├── van-b2_8xb128_in1k.py │ │ ├── van-b3_8xb128_in1k.py │ │ ├── van-b4_8xb128_in1k.py │ │ ├── van-base_8xb128_in1k.py │ │ ├── van-large_8xb128_in1k.py │ │ ├── van-small_8xb128_in1k.py │ │ └── van-tiny_8xb128_in1k.py │ ├── vgg │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── vgg11_8xb32_in1k.py │ │ ├── vgg11_b32x8_imagenet.py │ │ ├── vgg11bn_8xb32_in1k.py │ │ ├── vgg11bn_b32x8_imagenet.py │ │ ├── vgg13_8xb32_in1k.py │ │ ├── vgg13_b32x8_imagenet.py │ │ ├── vgg13bn_8xb32_in1k.py │ │ ├── vgg13bn_b32x8_imagenet.py │ │ ├── vgg16_8xb16_voc.py │ │ ├── vgg16_8xb32_in1k.py │ │ ├── vgg16_b16x8_voc.py │ │ ├── vgg16_b32x8_imagenet.py │ │ ├── vgg16bn_8xb32_in1k.py │ │ ├── vgg16bn_b32x8_imagenet.py │ │ ├── vgg19_8xb32_in1k.py │ │ ├── vgg19_b32x8_imagenet.py │ │ ├── vgg19bn_8xb32_in1k.py │ │ └── vgg19bn_b32x8_imagenet.py │ ├── vision_transformer │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── vit-base-p16_ft-4xb544-ipu_in1k.py │ │ ├── vit-base-p16_ft-64xb64_in1k-384.py │ │ ├── vit-base-p16_pt-64xb64_in1k-224.py │ │ ├── vit-base-p32_ft-64xb64_in1k-384.py │ │ ├── vit-base-p32_pt-64xb64_in1k-224.py │ │ ├── vit-large-p16_ft-64xb64_in1k-384.py │ │ ├── vit-large-p16_pt-64xb64_in1k-224.py │ │ ├── vit-large-p32_ft-64xb64_in1k-384.py │ │ └── vit-large-p32_pt-64xb64_in1k-224.py │ └── wrn │ │ ├── README.md │ │ ├── metafile.yml │ │ ├── wide-resnet101_8xb32_in1k.py │ │ ├── wide-resnet50_8xb32_in1k.py │ │ └── wide-resnet50_timm_8xb32_in1k.py ├── demo │ ├── bird.JPEG │ ├── cat-dog.png │ ├── demo.JPEG │ ├── dog.jpg │ ├── image_demo.py │ └── ipu_train_example.sh ├── docker │ ├── Dockerfile │ └── serve │ │ ├── Dockerfile │ │ ├── config.properties │ │ └── entrypoint.sh ├── docs │ ├── en │ │ ├── Makefile │ │ ├── _static │ │ │ ├── css │ │ │ │ └── readthedocs.css │ │ │ ├── image │ │ │ │ ├── mmcls-logo.png │ │ │ │ └── tools │ │ │ │ │ ├── analysis │ │ │ │ │ └── analyze_log.jpg │ │ │ │ │ └── visualization │ │ │ │ │ ├── lr_schedule1.png │ │ │ │ │ └── lr_schedule2.png │ │ │ └── js │ │ │ │ └── custom.js │ │ ├── _templates │ │ │ └── classtemplate.rst │ │ ├── api │ │ │ ├── apis.rst │ │ │ ├── core.rst │ │ │ ├── datasets.rst │ │ │ ├── models.rst │ │ │ ├── models.utils.augment.rst │ │ │ ├── models.utils.rst │ │ │ ├── transforms.rst │ │ │ └── utils.rst │ │ ├── changelog.md │ │ ├── community │ │ │ └── CONTRIBUTING.md │ │ ├── compatibility.md │ │ ├── conf.py │ │ ├── device │ │ │ └── npu.md │ │ ├── docutils.conf │ │ ├── faq.md │ │ ├── getting_started.md │ │ ├── index.rst │ │ ├── install.md │ │ ├── model_zoo.md │ │ ├── stat.py │ │ ├── tools │ │ │ ├── analysis.md │ │ │ ├── miscellaneous.md │ │ │ ├── model_serving.md │ │ │ ├── onnx2tensorrt.md │ │ │ ├── pytorch2onnx.md │ │ │ ├── pytorch2torchscript.md │ │ │ └── visualization.md │ │ └── tutorials │ │ │ ├── MMClassification_python.ipynb │ │ │ ├── MMClassification_tools.ipynb │ │ │ ├── config.md │ │ │ ├── data_pipeline.md │ │ │ ├── finetune.md │ │ │ ├── new_dataset.md │ │ │ ├── new_modules.md │ │ │ ├── runtime.md │ │ │ └── schedule.md │ └── zh_CN │ │ ├── Makefile │ │ ├── _static │ │ ├── css │ │ │ └── readthedocs.css │ │ ├── image │ │ │ ├── mmcls-logo.png │ │ │ └── tools │ │ │ │ ├── analysis │ │ │ │ └── analyze_log.jpg │ │ │ │ └── visualization │ │ │ │ ├── lr_schedule1.png │ │ │ │ └── lr_schedule2.png │ │ └── js │ │ │ └── custom.js │ │ ├── api │ │ ├── apis.rst │ │ ├── core.rst │ │ ├── datasets.rst │ │ ├── models.rst │ │ ├── models.utils.augment.rst │ │ ├── models.utils.rst │ │ ├── transforms.rst │ │ └── utils.rst │ │ ├── changelog.md │ │ ├── community │ │ └── CONTRIBUTING.md │ │ ├── compatibility.md │ │ ├── conf.py │ │ ├── device │ │ └── npu.md │ │ ├── docutils.conf │ │ ├── faq.md │ │ ├── getting_started.md │ │ ├── imgs │ │ ├── qq_group_qrcode.jpg │ │ └── zhihu_qrcode.jpg │ │ ├── index.rst │ │ ├── install.md │ │ ├── model_zoo.md │ │ ├── stat.py │ │ ├── tools │ │ ├── analysis.md │ │ ├── miscellaneous.md │ │ ├── model_serving.md │ │ ├── onnx2tensorrt.md │ │ ├── pytorch2onnx.md │ │ ├── pytorch2torchscript.md │ │ └── visualization.md │ │ └── tutorials │ │ ├── MMClassification_python_cn.ipynb │ │ ├── MMClassification_tools_cn.ipynb │ │ ├── config.md │ │ ├── data_pipeline.md │ │ ├── finetune.md │ │ ├── new_dataset.md │ │ ├── new_modules.md │ │ ├── runtime.md │ │ └── schedule.md ├── mmcls │ ├── __init__.py │ ├── apis │ │ ├── __init__.py │ │ ├── inference.py │ │ ├── test.py │ │ └── train.py │ ├── core │ │ ├── __init__.py │ │ ├── evaluation │ │ │ ├── __init__.py │ │ │ ├── eval_hooks.py │ │ │ ├── eval_metrics.py │ │ │ ├── mean_ap.py │ │ │ └── multilabel_eval_metrics.py │ │ ├── export │ │ │ ├── __init__.py │ │ │ └── test.py │ │ ├── hook │ │ │ ├── __init__.py │ │ │ ├── class_num_check_hook.py │ │ │ ├── lr_updater.py │ │ │ ├── precise_bn_hook.py │ │ │ └── wandblogger_hook.py │ │ ├── optimizers │ │ │ ├── __init__.py │ │ │ └── lamb.py │ │ ├── utils │ │ │ ├── __init__.py │ │ │ ├── dist_utils.py │ │ │ └── misc.py │ │ └── visualization │ │ │ ├── __init__.py │ │ │ └── image.py │ ├── datasets │ │ ├── __init__.py │ │ ├── base_dataset.py │ │ ├── builder.py │ │ ├── cifar.py │ │ ├── cub.py │ │ ├── custom.py │ │ ├── dataset_wrappers.py │ │ ├── eurosat_clip.py │ │ ├── imagenet.py │ │ ├── imagenet21k.py │ │ ├── mnist.py │ │ ├── multi_label.py │ │ ├── oxford_flower_mona.py │ │ ├── oxford_pet_mona.py │ │ ├── pipelines │ │ │ ├── __init__.py │ │ │ ├── auto_augment.py │ │ │ ├── compose.py │ │ │ ├── formatting.py │ │ │ ├── loading.py │ │ │ └── transforms.py │ │ ├── samplers │ │ │ ├── __init__.py │ │ │ ├── distributed_sampler.py │ │ │ └── repeat_aug.py │ │ ├── stanford_cars.py │ │ ├── utils.py │ │ ├── voc.py │ │ └── voc_mona.py │ ├── models │ │ ├── __init__.py │ │ ├── backbones │ │ │ ├── LowRankModules.py │ │ │ ├── __init__.py │ │ │ ├── alexnet.py │ │ │ ├── base_backbone.py │ │ │ ├── conformer.py │ │ │ ├── convmixer.py │ │ │ ├── convnext.py │ │ │ ├── cspnet.py │ │ │ ├── deit.py │ │ │ ├── densenet.py │ │ │ ├── efficientformer.py │ │ │ ├── efficientnet.py │ │ │ ├── hornet.py │ │ │ ├── hrnet.py │ │ │ ├── lenet.py │ │ │ ├── lora_layers.py │ │ │ ├── mlp_mixer.py │ │ │ ├── mobilenet_v2.py │ │ │ ├── mobilenet_v3.py │ │ │ ├── mvit.py │ │ │ ├── poolformer.py │ │ │ ├── regnet.py │ │ │ ├── repmlp.py │ │ │ ├── repvgg.py │ │ │ ├── res2net.py │ │ │ ├── resnest.py │ │ │ ├── resnet.py │ │ │ ├── resnet_cifar.py │ │ │ ├── resnext.py │ │ │ ├── seresnet.py │ │ │ ├── seresnext.py │ │ │ ├── shufflenet_v1.py │ │ │ ├── shufflenet_v2.py │ │ │ ├── swin_transformer.py │ │ │ ├── swin_transformer_adapter.py │ │ │ ├── swin_transformer_adaptformer.py │ │ │ ├── swin_transformer_bitfit.py │ │ │ ├── swin_transformer_fixed.py │ │ │ ├── swin_transformer_lora.py │ │ │ ├── swin_transformer_mona.py │ │ │ ├── swin_transformer_norm_tuning.py │ │ │ ├── swin_transformer_partial_1.py │ │ │ ├── swin_transformer_v2.py │ │ │ ├── t2t_vit.py │ │ │ ├── timm_backbone.py │ │ │ ├── tnt.py │ │ │ ├── twins.py │ │ │ ├── van.py │ │ │ ├── vgg.py │ │ │ └── vision_transformer.py │ │ ├── builder.py │ │ ├── classifiers │ │ │ ├── __init__.py │ │ │ ├── base.py │ │ │ └── image.py │ │ ├── heads │ │ │ ├── __init__.py │ │ │ ├── base_head.py │ │ │ ├── cls_head.py │ │ │ ├── conformer_head.py │ │ │ ├── deit_head.py │ │ │ ├── efficientformer_head.py │ │ │ ├── linear_head.py │ │ │ ├── multi_label_csra_head.py │ │ │ ├── multi_label_head.py │ │ │ ├── multi_label_linear_head.py │ │ │ ├── stacked_head.py │ │ │ └── vision_transformer_head.py │ │ ├── losses │ │ │ ├── __init__.py │ │ │ ├── accuracy.py │ │ │ ├── asymmetric_loss.py │ │ │ ├── cross_entropy_loss.py │ │ │ ├── focal_loss.py │ │ │ ├── label_smooth_loss.py │ │ │ ├── seesaw_loss.py │ │ │ └── utils.py │ │ ├── necks │ │ │ ├── __init__.py │ │ │ ├── gap.py │ │ │ ├── gem.py │ │ │ └── hr_fuse.py │ │ └── utils │ │ │ ├── __init__.py │ │ │ ├── attention.py │ │ │ ├── augment │ │ │ ├── __init__.py │ │ │ ├── augments.py │ │ │ ├── builder.py │ │ │ ├── cutmix.py │ │ │ ├── identity.py │ │ │ ├── mixup.py │ │ │ ├── resizemix.py │ │ │ └── utils.py │ │ │ ├── channel_shuffle.py │ │ │ ├── embed.py │ │ │ ├── helpers.py │ │ │ ├── inverted_residual.py │ │ │ ├── layer_scale.py │ │ │ ├── make_divisible.py │ │ │ ├── position_encoding.py │ │ │ └── se_layer.py │ ├── utils │ │ ├── __init__.py │ │ ├── collect_env.py │ │ ├── device.py │ │ ├── distribution.py │ │ ├── logger.py │ │ └── setup_env.py │ └── version.py ├── model-index.yml ├── mona_configs │ ├── _base_ │ │ ├── datasets │ │ │ ├── cifar100_bs16.py │ │ │ ├── cifar10_bs16.py │ │ │ ├── cub_bs8_384.py │ │ │ ├── cub_bs8_448.py │ │ │ ├── imagenet21k_bs128.py │ │ │ ├── imagenet_bs128_poolformer_medium_224.py │ │ │ ├── imagenet_bs128_poolformer_small_224.py │ │ │ ├── imagenet_bs256_rsb_a12.py │ │ │ ├── imagenet_bs256_rsb_a3.py │ │ │ ├── imagenet_bs32.py │ │ │ ├── imagenet_bs32_pil_bicubic.py │ │ │ ├── imagenet_bs32_pil_resize.py │ │ │ ├── imagenet_bs64.py │ │ │ ├── imagenet_bs64_autoaug.py │ │ │ ├── imagenet_bs64_convmixer_224.py │ │ │ ├── imagenet_bs64_mixer_224.py │ │ │ ├── imagenet_bs64_pil_resize.py │ │ │ ├── imagenet_bs64_pil_resize_autoaug.py │ │ │ ├── imagenet_bs64_swin_224.py │ │ │ ├── imagenet_bs64_swin_256.py │ │ │ ├── imagenet_bs64_swin_384.py │ │ │ ├── imagenet_bs64_t2t_224.py │ │ │ ├── mnist_bs16.py │ │ │ ├── oxford_flower_mona.py │ │ │ ├── oxford_pet_mona.py │ │ │ ├── pipelines │ │ │ │ ├── auto_aug.py │ │ │ │ └── rand_aug.py │ │ │ ├── stanford_cars_bs8_448.py │ │ │ ├── voc_bs16.py │ │ │ └── voc_mona.py │ │ ├── default_runtime.py │ │ ├── models │ │ │ ├── conformer │ │ │ │ ├── base-p16.py │ │ │ │ ├── small-p16.py │ │ │ │ ├── small-p32.py │ │ │ │ └── tiny-p16.py │ │ │ ├── convmixer │ │ │ │ ├── convmixer-1024-20.py │ │ │ │ ├── convmixer-1536-20.py │ │ │ │ └── convmixer-768-32.py │ │ │ ├── convnext │ │ │ │ ├── convnext-base.py │ │ │ │ ├── convnext-large.py │ │ │ │ ├── convnext-small.py │ │ │ │ ├── convnext-tiny.py │ │ │ │ └── convnext-xlarge.py │ │ │ ├── densenet │ │ │ │ ├── densenet121.py │ │ │ │ ├── densenet161.py │ │ │ │ ├── densenet169.py │ │ │ │ └── densenet201.py │ │ │ ├── efficientnet_b0.py │ │ │ ├── efficientnet_b1.py │ │ │ ├── efficientnet_b2.py │ │ │ ├── efficientnet_b3.py │ │ │ ├── efficientnet_b4.py │ │ │ ├── efficientnet_b5.py │ │ │ ├── efficientnet_b6.py │ │ │ ├── efficientnet_b7.py │ │ │ ├── efficientnet_b8.py │ │ │ ├── efficientnet_em.py │ │ │ ├── efficientnet_es.py │ │ │ ├── hornet │ │ │ │ ├── hornet-base-gf.py │ │ │ │ ├── hornet-base.py │ │ │ │ ├── hornet-large-gf.py │ │ │ │ ├── hornet-large-gf384.py │ │ │ │ ├── hornet-large.py │ │ │ │ ├── hornet-small-gf.py │ │ │ │ ├── hornet-small.py │ │ │ │ ├── hornet-tiny-gf.py │ │ │ │ └── hornet-tiny.py │ │ │ ├── hrnet │ │ │ │ ├── hrnet-w18.py │ │ │ │ ├── hrnet-w30.py │ │ │ │ ├── hrnet-w32.py │ │ │ │ ├── hrnet-w40.py │ │ │ │ ├── hrnet-w44.py │ │ │ │ ├── hrnet-w48.py │ │ │ │ └── hrnet-w64.py │ │ │ ├── mlp_mixer_base_patch16.py │ │ │ ├── mlp_mixer_large_patch16.py │ │ │ ├── mobilenet_v2_1x.py │ │ │ ├── mobilenet_v3_large_imagenet.py │ │ │ ├── mobilenet_v3_small_cifar.py │ │ │ ├── mobilenet_v3_small_imagenet.py │ │ │ ├── mvit │ │ │ │ ├── mvitv2-base.py │ │ │ │ ├── mvitv2-large.py │ │ │ │ ├── mvitv2-small.py │ │ │ │ └── mvitv2-tiny.py │ │ │ ├── poolformer │ │ │ │ ├── poolformer_m36.py │ │ │ │ ├── poolformer_m48.py │ │ │ │ ├── poolformer_s12.py │ │ │ │ ├── poolformer_s24.py │ │ │ │ └── poolformer_s36.py │ │ │ ├── regnet │ │ │ │ ├── regnetx_1.6gf.py │ │ │ │ ├── regnetx_12gf.py │ │ │ │ ├── regnetx_3.2gf.py │ │ │ │ ├── regnetx_4.0gf.py │ │ │ │ ├── regnetx_400mf.py │ │ │ │ ├── regnetx_6.4gf.py │ │ │ │ ├── regnetx_8.0gf.py │ │ │ │ └── regnetx_800mf.py │ │ │ ├── repmlp-base_224.py │ │ │ ├── repvgg-A0_in1k.py │ │ │ ├── repvgg-B3_lbs-mixup_in1k.py │ │ │ ├── res2net101-w26-s4.py │ │ │ ├── res2net50-w14-s8.py │ │ │ ├── res2net50-w26-s4.py │ │ │ ├── res2net50-w26-s6.py │ │ │ ├── res2net50-w26-s8.py │ │ │ ├── res2net50-w48-s2.py │ │ │ ├── resnest101.py │ │ │ ├── resnest200.py │ │ │ ├── resnest269.py │ │ │ ├── resnest50.py │ │ │ ├── resnet101.py │ │ │ ├── resnet101_cifar.py │ │ │ ├── resnet152.py │ │ │ ├── resnet152_cifar.py │ │ │ ├── resnet18.py │ │ │ ├── resnet18_cifar.py │ │ │ ├── resnet34.py │ │ │ ├── resnet34_cifar.py │ │ │ ├── resnet34_gem.py │ │ │ ├── resnet50.py │ │ │ ├── resnet50_cifar.py │ │ │ ├── resnet50_cifar_cutmix.py │ │ │ ├── resnet50_cifar_mixup.py │ │ │ ├── resnet50_cutmix.py │ │ │ ├── resnet50_label_smooth.py │ │ │ ├── resnet50_mixup.py │ │ │ ├── resnetv1c50.py │ │ │ ├── resnetv1d101.py │ │ │ ├── resnetv1d152.py │ │ │ ├── resnetv1d50.py │ │ │ ├── resnext101_32x4d.py │ │ │ ├── resnext101_32x8d.py │ │ │ ├── resnext152_32x4d.py │ │ │ ├── resnext50_32x4d.py │ │ │ ├── seresnet101.py │ │ │ ├── seresnet50.py │ │ │ ├── seresnext101_32x4d.py │ │ │ ├── seresnext50_32x4d.py │ │ │ ├── shufflenet_v1_1x.py │ │ │ ├── shufflenet_v2_1x.py │ │ │ ├── swin_transformer │ │ │ │ ├── base_224.py │ │ │ │ ├── base_224_mnist.py │ │ │ │ ├── base_384.py │ │ │ │ ├── large_224.py │ │ │ │ ├── large_224_det.py │ │ │ │ ├── large_384.py │ │ │ │ ├── small_224.py │ │ │ │ └── tiny_224.py │ │ │ ├── swin_transformer_v2 │ │ │ │ ├── base_256.py │ │ │ │ ├── base_384.py │ │ │ │ ├── large_256.py │ │ │ │ ├── large_384.py │ │ │ │ ├── small_256.py │ │ │ │ └── tiny_256.py │ │ │ ├── t2t-vit-t-14.py │ │ │ ├── t2t-vit-t-19.py │ │ │ ├── t2t-vit-t-24.py │ │ │ ├── tnt_s_patch16_224.py │ │ │ ├── twins_pcpvt_base.py │ │ │ ├── twins_svt_base.py │ │ │ ├── van │ │ │ │ ├── van_b0.py │ │ │ │ ├── van_b1.py │ │ │ │ ├── van_b2.py │ │ │ │ ├── van_b3.py │ │ │ │ ├── van_b4.py │ │ │ │ ├── van_b5.py │ │ │ │ ├── van_b6.py │ │ │ │ ├── van_base.py │ │ │ │ ├── van_large.py │ │ │ │ ├── van_small.py │ │ │ │ └── van_tiny.py │ │ │ ├── vgg11.py │ │ │ ├── vgg11bn.py │ │ │ ├── vgg13.py │ │ │ ├── vgg13bn.py │ │ │ ├── vgg16.py │ │ │ ├── vgg16bn.py │ │ │ ├── vgg19.py │ │ │ ├── vgg19bn.py │ │ │ ├── vit-base-p16.py │ │ │ ├── vit-base-p32.py │ │ │ ├── vit-large-p16.py │ │ │ ├── vit-large-p32.py │ │ │ └── wide-resnet50.py │ │ └── schedules │ │ │ ├── cifar10_bs128.py │ │ │ ├── cub_bs64.py │ │ │ ├── imagenet_bs1024_adamw_conformer.py │ │ │ ├── imagenet_bs1024_adamw_swin.py │ │ │ ├── imagenet_bs1024_coslr.py │ │ │ ├── imagenet_bs1024_linearlr_bn_nowd.py │ │ │ ├── imagenet_bs2048.py │ │ │ ├── imagenet_bs2048_AdamW.py │ │ │ ├── imagenet_bs2048_coslr.py │ │ │ ├── imagenet_bs2048_rsb.py │ │ │ ├── imagenet_bs256.py │ │ │ ├── imagenet_bs256_140e.py │ │ │ ├── imagenet_bs256_200e_coslr_warmup.py │ │ │ ├── imagenet_bs256_coslr.py │ │ │ ├── imagenet_bs256_epochstep.py │ │ │ ├── imagenet_bs4096_AdamW.py │ │ │ └── stanford_cars_bs8.py │ ├── swin-l_oxford-flower │ │ ├── swin-large_4xb8_oxford_flower.py │ │ ├── swin-large_4xb8_oxford_flower_LN.py │ │ ├── swin-large_4xb8_oxford_flower_adapter.py │ │ ├── swin-large_4xb8_oxford_flower_adaptformer.py │ │ ├── swin-large_4xb8_oxford_flower_bitfit.py │ │ ├── swin-large_4xb8_oxford_flower_fixed.py │ │ ├── swin-large_4xb8_oxford_flower_lora.py │ │ ├── swin-large_4xb8_oxford_flower_mona.py │ │ └── swin-large_4xb8_oxford_flower_partial_1.py │ ├── swin-l_oxford-pet │ │ ├── swin-large_4xb8_oxford_pet.py │ │ ├── swin-large_4xb8_oxford_pet_LN.py │ │ ├── swin-large_4xb8_oxford_pet_adapter.py │ │ ├── swin-large_4xb8_oxford_pet_adaptformer.py │ │ ├── swin-large_4xb8_oxford_pet_bitfit.py │ │ ├── swin-large_4xb8_oxford_pet_fixed.py │ │ ├── swin-large_4xb8_oxford_pet_lora.py │ │ ├── swin-large_4xb8_oxford_pet_mona.py │ │ └── swin-large_4xb8_oxford_pet_partial_1.py │ └── swin-l_voc │ │ ├── swin-large_4xb8_voc.py │ │ ├── swin-large_4xb8_voc_LN.py │ │ ├── swin-large_4xb8_voc_adapter.py │ │ ├── swin-large_4xb8_voc_adaptformer.py │ │ ├── swin-large_4xb8_voc_bitfit.py │ │ ├── swin-large_4xb8_voc_fixed.py │ │ ├── swin-large_4xb8_voc_lora.py │ │ ├── swin-large_4xb8_voc_mona.py │ │ └── swin-large_4xb8_voc_partial_1.py ├── requirements.txt ├── requirements │ ├── docs.txt │ ├── mminstall.txt │ ├── optional.txt │ ├── readthedocs.txt │ ├── runtime.txt │ └── tests.txt ├── resources │ └── mmcls-logo.png ├── setup.cfg ├── setup.py ├── tests │ ├── test_data │ │ ├── test_builder.py │ │ ├── test_datasets │ │ │ ├── test_common.py │ │ │ ├── test_dataset_utils.py │ │ │ ├── test_dataset_wrapper.py │ │ │ └── test_sampler.py │ │ └── test_pipelines │ │ │ ├── test_auto_augment.py │ │ │ ├── test_loading.py │ │ │ └── test_transform.py │ ├── test_downstream │ │ └── test_mmdet_inference.py │ ├── test_metrics │ │ ├── test_losses.py │ │ ├── test_metrics.py │ │ └── test_utils.py │ ├── test_models │ │ ├── test_backbones │ │ │ ├── __init__.py │ │ │ ├── test_conformer.py │ │ │ ├── test_convmixer.py │ │ │ ├── test_convnext.py │ │ │ ├── test_cspnet.py │ │ │ ├── test_deit.py │ │ │ ├── test_densenet.py │ │ │ ├── test_efficientformer.py │ │ │ ├── test_efficientnet.py │ │ │ ├── test_hornet.py │ │ │ ├── test_hrnet.py │ │ │ ├── test_mlp_mixer.py │ │ │ ├── test_mobilenet_v2.py │ │ │ ├── test_mobilenet_v3.py │ │ │ ├── test_mvit.py │ │ │ ├── test_poolformer.py │ │ │ ├── test_regnet.py │ │ │ ├── test_repmlp.py │ │ │ ├── test_repvgg.py │ │ │ ├── test_res2net.py │ │ │ ├── test_resnest.py │ │ │ ├── test_resnet.py │ │ │ ├── test_resnet_cifar.py │ │ │ ├── test_resnext.py │ │ │ ├── test_seresnet.py │ │ │ ├── test_seresnext.py │ │ │ ├── test_shufflenet_v1.py │ │ │ ├── test_shufflenet_v2.py │ │ │ ├── test_swin_transformer.py │ │ │ ├── test_swin_transformer_v2.py │ │ │ ├── test_t2t_vit.py │ │ │ ├── test_timm_backbone.py │ │ │ ├── test_tnt.py │ │ │ ├── test_twins.py │ │ │ ├── test_van.py │ │ │ ├── test_vgg.py │ │ │ ├── test_vision_transformer.py │ │ │ └── utils.py │ │ ├── test_classifiers.py │ │ ├── test_heads.py │ │ ├── test_neck.py │ │ └── test_utils │ │ │ ├── test_attention.py │ │ │ ├── test_augment.py │ │ │ ├── test_embed.py │ │ │ ├── test_inverted_residual.py │ │ │ ├── test_layer_scale.py │ │ │ ├── test_misc.py │ │ │ ├── test_position_encoding.py │ │ │ └── test_se.py │ ├── test_runtime │ │ ├── test_eval_hook.py │ │ ├── test_hooks.py │ │ ├── test_num_class_hook.py │ │ ├── test_optimizer.py │ │ └── test_preciseBN_hook.py │ └── test_utils │ │ ├── test_device.py │ │ ├── test_logger.py │ │ ├── test_setup_env.py │ │ ├── test_version_utils.py │ │ └── test_visualization.py └── tools │ ├── analysis_tools │ ├── analyze_logs.py │ ├── analyze_results.py │ ├── eval_metric.py │ └── get_flops.py │ ├── convert_models │ ├── efficientnet_to_mmcls.py │ ├── hornet2mmcls.py │ ├── mlpmixer_to_mmcls.py │ ├── mobilenetv2_to_mmcls.py │ ├── publish_model.py │ ├── reparameterize_model.py │ ├── reparameterize_repvgg.py │ ├── repvgg_to_mmcls.py │ ├── shufflenetv2_to_mmcls.py │ ├── torchvision_to_mmcls.py │ ├── twins2mmcls.py │ ├── van2mmcls.py │ └── vgg_to_mmcls.py │ ├── deployment │ ├── mmcls2torchserve.py │ ├── mmcls_handler.py │ ├── onnx2tensorrt.py │ ├── pytorch2mlmodel.py │ ├── pytorch2onnx.py │ ├── pytorch2torchscript.py │ ├── test.py │ └── test_torchserver.py │ ├── dist_test.sh │ ├── dist_train.sh │ ├── dist_train_arm.sh │ ├── dist_train_v100.sh │ ├── kfold-cross-valid.py │ ├── misc │ ├── print_config.py │ └── verify_dataset.py │ ├── slurm_test.sh │ ├── slurm_train.sh │ ├── test.py │ ├── train.py │ └── visualizations │ ├── vis_cam.py │ ├── vis_lr.py │ └── vis_pipeline.py ├── Swin-Transformer-Object-Detection ├── .dev_scripts │ ├── batch_test.py │ ├── batch_test.sh │ ├── benchmark_filter.py │ ├── convert_benchmark_script.py │ ├── gather_benchmark_metric.py │ ├── gather_models.py │ └── linter.sh ├── .github │ ├── CODE_OF_CONDUCT.md │ ├── CONTRIBUTING.md │ ├── ISSUE_TEMPLATE │ │ ├── config.yml │ │ ├── error-report.md │ │ ├── feature_request.md │ │ ├── general_questions.md │ │ └── reimplementation_questions.md │ └── workflows │ │ ├── build.yml │ │ ├── build_pat.yml │ │ └── deploy.yml ├── .gitignore ├── .pre-commit-config.yaml ├── .readthedocs.yml ├── LICENSE ├── README.md ├── configs │ ├── _base_ │ │ ├── datasets │ │ │ ├── cityscapes_detection.py │ │ │ ├── cityscapes_instance.py │ │ │ ├── coco_detection.py │ │ │ ├── coco_instance.py │ │ │ ├── coco_instance_semantic.py │ │ │ ├── deepfashion.py │ │ │ ├── lvis_v0.5_instance.py │ │ │ ├── lvis_v1_instance.py │ │ │ ├── voc0712.py │ │ │ └── wider_face.py │ │ ├── default_runtime.py │ │ ├── models │ │ │ ├── cascade_mask_rcnn_r50_fpn.py │ │ │ ├── cascade_mask_rcnn_swin_fpn.py │ │ │ ├── cascade_rcnn_r50_fpn.py │ │ │ ├── fast_rcnn_r50_fpn.py │ │ │ ├── fast_rcnn_swin_fpn.py │ │ │ ├── faster_rcnn_r50_caffe_c4.py │ │ │ ├── faster_rcnn_r50_caffe_dc5.py │ │ │ ├── faster_rcnn_r50_fpn.py │ │ │ ├── faster_rcnn_swin_fpn.py │ │ │ ├── mask_rcnn_r50_caffe_c4.py │ │ │ ├── mask_rcnn_r50_fpn.py │ │ │ ├── mask_rcnn_swin_fpn.py │ │ │ ├── mask_reppointsv2_swin_bifpn.py │ │ │ ├── reppointsv2_swin_bifpn.py │ │ │ ├── retinanet_r50_fpn.py │ │ │ ├── rpn_r50_caffe_c4.py │ │ │ ├── rpn_r50_fpn.py │ │ │ └── ssd300.py │ │ └── schedules │ │ │ ├── schedule_1x.py │ │ │ ├── schedule_20e.py │ │ │ └── schedule_2x.py │ ├── albu_example │ │ ├── README.md │ │ └── mask_rcnn_r50_fpn_albu_1x_coco.py │ ├── atss │ │ ├── README.md │ │ ├── atss_r101_fpn_1x_coco.py │ │ └── atss_r50_fpn_1x_coco.py │ ├── carafe │ │ ├── README.md │ │ ├── faster_rcnn_r50_fpn_carafe_1x_coco.py │ │ └── mask_rcnn_r50_fpn_carafe_1x_coco.py │ ├── cascade_rcnn │ │ ├── README.md │ │ ├── cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py │ │ ├── cascade_mask_rcnn_r101_fpn_1x_coco.py │ │ ├── cascade_mask_rcnn_r101_fpn_20e_coco.py │ │ ├── cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py │ │ ├── cascade_mask_rcnn_r50_fpn_1x_coco.py │ │ ├── cascade_mask_rcnn_r50_fpn_20e_coco.py │ │ ├── cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py │ │ ├── cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py │ │ ├── cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py │ │ ├── cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py │ │ ├── cascade_rcnn_r101_caffe_fpn_1x_coco.py │ │ ├── cascade_rcnn_r101_fpn_1x_coco.py │ │ ├── cascade_rcnn_r101_fpn_20e_coco.py │ │ ├── cascade_rcnn_r50_caffe_fpn_1x_coco.py │ │ ├── cascade_rcnn_r50_fpn_1x_coco.py │ │ ├── cascade_rcnn_r50_fpn_20e_coco.py │ │ ├── cascade_rcnn_x101_32x4d_fpn_1x_coco.py │ │ ├── cascade_rcnn_x101_32x4d_fpn_20e_coco.py │ │ ├── cascade_rcnn_x101_64x4d_fpn_1x_coco.py │ │ └── cascade_rcnn_x101_64x4d_fpn_20e_coco.py │ ├── cascade_rpn │ │ ├── README.md │ │ ├── crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py │ │ ├── crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py │ │ └── crpn_r50_caffe_fpn_1x_coco.py │ ├── centripetalnet │ │ ├── README.md │ │ └── centripetalnet_hourglass104_mstest_16x6_210e_coco.py │ ├── cityscapes │ │ ├── README.md │ │ ├── faster_rcnn_r50_fpn_1x_cityscapes.py │ │ └── mask_rcnn_r50_fpn_1x_cityscapes.py │ ├── cornernet │ │ ├── README.md │ │ ├── cornernet_hourglass104_mstest_10x5_210e_coco.py │ │ ├── cornernet_hourglass104_mstest_32x3_210e_coco.py │ │ └── cornernet_hourglass104_mstest_8x6_210e_coco.py │ ├── dcn │ │ ├── README.md │ │ ├── cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py │ │ ├── cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py │ │ ├── cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py │ │ ├── cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py │ │ ├── cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py │ │ ├── faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_dpool_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_mdpool_1x_coco.py │ │ ├── faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py │ │ ├── mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py │ │ ├── mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py │ │ └── mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py │ ├── deepfashion │ │ ├── README.md │ │ └── mask_rcnn_r50_fpn_15e_deepfashion.py │ ├── detectors │ │ ├── README.md │ │ ├── cascade_rcnn_r50_rfp_1x_coco.py │ │ ├── cascade_rcnn_r50_sac_1x_coco.py │ │ ├── detectors_cascade_rcnn_r50_1x_coco.py │ │ ├── detectors_htc_r50_1x_coco.py │ │ ├── htc_r50_rfp_1x_coco.py │ │ └── htc_r50_sac_1x_coco.py │ ├── detr │ │ ├── README.md │ │ └── detr_r50_8x2_150e_coco.py │ ├── double_heads │ │ ├── README.md │ │ └── dh_faster_rcnn_r50_fpn_1x_coco.py │ ├── dynamic_rcnn │ │ ├── README.md │ │ └── dynamic_rcnn_r50_fpn_1x.py │ ├── empirical_attention │ │ ├── README.md │ │ ├── faster_rcnn_r50_fpn_attention_0010_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_attention_1111_1x_coco.py │ │ └── faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py │ ├── fast_rcnn │ │ ├── README.md │ │ ├── fast_rcnn_r101_caffe_fpn_1x_coco.py │ │ ├── fast_rcnn_r101_fpn_1x_coco.py │ │ ├── fast_rcnn_r101_fpn_2x_coco.py │ │ ├── fast_rcnn_r50_caffe_fpn_1x_coco.py │ │ ├── fast_rcnn_r50_fpn_1x_coco.py │ │ └── fast_rcnn_r50_fpn_2x_coco.py │ ├── faster_rcnn │ │ ├── README.md │ │ ├── faster_rcnn_r101_caffe_fpn_1x_coco.py │ │ ├── faster_rcnn_r101_fpn_1x_coco.py │ │ ├── faster_rcnn_r101_fpn_2x_coco.py │ │ ├── faster_rcnn_r50_caffe_c4_1x_coco.py │ │ ├── faster_rcnn_r50_caffe_dc5_1x_coco.py │ │ ├── faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py │ │ ├── faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py │ │ ├── faster_rcnn_r50_caffe_fpn_1x_coco.py │ │ ├── faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py │ │ ├── faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py │ │ ├── faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py │ │ ├── faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py │ │ ├── faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py │ │ ├── faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py │ │ ├── faster_rcnn_r50_fpn_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_2x_coco.py │ │ ├── faster_rcnn_r50_fpn_bounded_iou_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_giou_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_iou_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_ohem_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_soft_nms_1x_coco.py │ │ ├── faster_rcnn_x101_32x4d_fpn_1x_coco.py │ │ ├── faster_rcnn_x101_32x4d_fpn_2x_coco.py │ │ ├── faster_rcnn_x101_64x4d_fpn_1x_coco.py │ │ └── faster_rcnn_x101_64x4d_fpn_2x_coco.py │ ├── fcos │ │ ├── README.md │ │ ├── fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py │ │ ├── fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py │ │ ├── fcos_center_r50_caffe_fpn_gn-head_1x_coco.py │ │ ├── fcos_r101_caffe_fpn_gn-head_1x_coco.py │ │ ├── fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py │ │ ├── fcos_r50_caffe_fpn_gn-head_1x_coco.py │ │ ├── fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py │ │ ├── fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py │ │ └── fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py │ ├── foveabox │ │ ├── README.md │ │ ├── fovea_align_r101_fpn_gn-head_4x4_2x_coco.py │ │ ├── fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py │ │ ├── fovea_align_r50_fpn_gn-head_4x4_2x_coco.py │ │ ├── fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py │ │ ├── fovea_r101_fpn_4x4_1x_coco.py │ │ ├── fovea_r101_fpn_4x4_2x_coco.py │ │ ├── fovea_r50_fpn_4x4_1x_coco.py │ │ └── fovea_r50_fpn_4x4_2x_coco.py │ ├── fp16 │ │ ├── README.md │ │ ├── faster_rcnn_r50_fpn_fp16_1x_coco.py │ │ ├── mask_rcnn_r50_fpn_fp16_1x_coco.py │ │ └── retinanet_r50_fpn_fp16_1x_coco.py │ ├── fpg │ │ ├── README.md │ │ ├── faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py │ │ ├── faster_rcnn_r50_fpg_crop640_50e_coco.py │ │ ├── faster_rcnn_r50_fpn_crop640_50e_coco.py │ │ ├── mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py │ │ ├── mask_rcnn_r50_fpg_crop640_50e_coco.py │ │ ├── mask_rcnn_r50_fpn_crop640_50e_coco.py │ │ ├── retinanet_r50_fpg-chn128_crop640_50e_coco.py │ │ └── retinanet_r50_fpg_crop640_50e_coco.py │ ├── free_anchor │ │ ├── README.md │ │ ├── retinanet_free_anchor_r101_fpn_1x_coco.py │ │ ├── retinanet_free_anchor_r50_fpn_1x_coco.py │ │ └── retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py │ ├── fsaf │ │ ├── README.md │ │ ├── fsaf_r101_fpn_1x_coco.py │ │ ├── fsaf_r50_fpn_1x_coco.py │ │ └── fsaf_x101_64x4d_fpn_1x_coco.py │ ├── gcnet │ │ ├── README.md │ │ ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py │ │ ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py │ │ ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py │ │ ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py │ │ ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py │ │ ├── cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py │ │ ├── mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py │ │ ├── mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py │ │ ├── mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py │ │ ├── mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py │ │ ├── mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py │ │ ├── mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py │ │ ├── mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py │ │ ├── mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py │ │ ├── mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py │ │ ├── mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py │ │ ├── mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py │ │ ├── mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py │ │ └── mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py │ ├── gfl │ │ ├── README.md │ │ ├── gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py │ │ ├── gfl_r101_fpn_mstrain_2x_coco.py │ │ ├── gfl_r50_fpn_1x_coco.py │ │ ├── gfl_r50_fpn_mstrain_2x_coco.py │ │ ├── gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py │ │ └── gfl_x101_32x4d_fpn_mstrain_2x_coco.py │ ├── ghm │ │ ├── README.md │ │ ├── retinanet_ghm_r101_fpn_1x_coco.py │ │ ├── retinanet_ghm_r50_fpn_1x_coco.py │ │ ├── retinanet_ghm_x101_32x4d_fpn_1x_coco.py │ │ └── retinanet_ghm_x101_64x4d_fpn_1x_coco.py │ ├── gn+ws │ │ ├── README.md │ │ ├── faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py │ │ ├── faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py │ │ ├── faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py │ │ ├── faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py │ │ ├── mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py │ │ ├── mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py │ │ ├── mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py │ │ ├── mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py │ │ ├── mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py │ │ ├── mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py │ │ ├── mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py │ │ └── mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py │ ├── gn │ │ ├── README.md │ │ ├── mask_rcnn_r101_fpn_gn-all_2x_coco.py │ │ ├── mask_rcnn_r101_fpn_gn-all_3x_coco.py │ │ ├── mask_rcnn_r50_fpn_gn-all_2x_coco.py │ │ ├── mask_rcnn_r50_fpn_gn-all_3x_coco.py │ │ ├── mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py │ │ └── mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py │ ├── grid_rcnn │ │ ├── README.md │ │ ├── grid_rcnn_r101_fpn_gn-head_2x_coco.py │ │ ├── grid_rcnn_r50_fpn_gn-head_1x_coco.py │ │ ├── grid_rcnn_r50_fpn_gn-head_2x_coco.py │ │ ├── grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py │ │ └── grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py │ ├── groie │ │ ├── README.md │ │ ├── faster_rcnn_r50_fpn_groie_1x_coco.py │ │ ├── grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py │ │ ├── mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py │ │ ├── mask_rcnn_r50_fpn_groie_1x_coco.py │ │ └── mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py │ ├── guided_anchoring │ │ ├── README.md │ │ ├── ga_fast_r50_caffe_fpn_1x_coco.py │ │ ├── ga_faster_r101_caffe_fpn_1x_coco.py │ │ ├── ga_faster_r50_caffe_fpn_1x_coco.py │ │ ├── ga_faster_r50_fpn_1x_coco.py │ │ ├── ga_faster_x101_32x4d_fpn_1x_coco.py │ │ ├── ga_faster_x101_64x4d_fpn_1x_coco.py │ │ ├── ga_retinanet_r101_caffe_fpn_1x_coco.py │ │ ├── ga_retinanet_r101_caffe_fpn_mstrain_2x.py │ │ ├── ga_retinanet_r50_caffe_fpn_1x_coco.py │ │ ├── ga_retinanet_r50_fpn_1x_coco.py │ │ ├── ga_retinanet_x101_32x4d_fpn_1x_coco.py │ │ ├── ga_retinanet_x101_64x4d_fpn_1x_coco.py │ │ ├── ga_rpn_r101_caffe_fpn_1x_coco.py │ │ ├── ga_rpn_r50_caffe_fpn_1x_coco.py │ │ ├── ga_rpn_r50_fpn_1x_coco.py │ │ ├── ga_rpn_x101_32x4d_fpn_1x_coco.py │ │ └── ga_rpn_x101_64x4d_fpn_1x_coco.py │ ├── hrnet │ │ ├── README.md │ │ ├── cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py │ │ ├── cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py │ │ ├── cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py │ │ ├── cascade_rcnn_hrnetv2p_w18_20e_coco.py │ │ ├── cascade_rcnn_hrnetv2p_w32_20e_coco.py │ │ ├── cascade_rcnn_hrnetv2p_w40_20e_coco.py │ │ ├── faster_rcnn_hrnetv2p_w18_1x_coco.py │ │ ├── faster_rcnn_hrnetv2p_w18_2x_coco.py │ │ ├── faster_rcnn_hrnetv2p_w32_1x_coco.py │ │ ├── faster_rcnn_hrnetv2p_w32_2x_coco.py │ │ ├── faster_rcnn_hrnetv2p_w40_1x_coco.py │ │ ├── faster_rcnn_hrnetv2p_w40_2x_coco.py │ │ ├── fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py │ │ ├── fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py │ │ ├── fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py │ │ ├── fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py │ │ ├── fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py │ │ ├── fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py │ │ ├── fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py │ │ ├── htc_hrnetv2p_w18_20e_coco.py │ │ ├── htc_hrnetv2p_w32_20e_coco.py │ │ ├── htc_hrnetv2p_w40_20e_coco.py │ │ ├── htc_hrnetv2p_w40_28e_coco.py │ │ ├── htc_x101_64x4d_fpn_16x1_28e_coco.py │ │ ├── mask_rcnn_hrnetv2p_w18_1x_coco.py │ │ ├── mask_rcnn_hrnetv2p_w18_2x_coco.py │ │ ├── mask_rcnn_hrnetv2p_w32_1x_coco.py │ │ ├── mask_rcnn_hrnetv2p_w32_2x_coco.py │ │ ├── mask_rcnn_hrnetv2p_w40_1x_coco.py │ │ └── mask_rcnn_hrnetv2p_w40_2x_coco.py │ ├── htc │ │ ├── README.md │ │ ├── htc_r101_fpn_20e_coco.py │ │ ├── htc_r50_fpn_1x_coco.py │ │ ├── htc_r50_fpn_20e_coco.py │ │ ├── htc_without_semantic_r50_fpn_1x_coco.py │ │ ├── htc_x101_32x4d_fpn_16x1_20e_coco.py │ │ ├── htc_x101_64x4d_fpn_16x1_20e_coco.py │ │ └── htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py │ ├── instaboost │ │ ├── README.md │ │ ├── cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py │ │ ├── cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py │ │ ├── cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py │ │ ├── mask_rcnn_r101_fpn_instaboost_4x_coco.py │ │ ├── mask_rcnn_r50_fpn_instaboost_4x_coco.py │ │ └── mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py │ ├── ld │ │ ├── ld_r101_gflv1_r101dcn_fpn_coco_2x.py │ │ ├── ld_r18_gflv1_r101_fpn_coco_1x.py │ │ ├── ld_r34_gflv1_r101_fpn_coco_1x.py │ │ ├── ld_r50_gflv1_r101_fpn_coco_1x.py │ │ └── readme.md │ ├── legacy_1.x │ │ ├── README.md │ │ ├── cascade_mask_rcnn_r50_fpn_1x_coco_v1.py │ │ ├── faster_rcnn_r50_fpn_1x_coco_v1.py │ │ ├── mask_rcnn_r50_fpn_1x_coco_v1.py │ │ ├── retinanet_r50_caffe_fpn_1x_coco_v1.py │ │ ├── retinanet_r50_fpn_1x_coco_v1.py │ │ └── ssd300_coco_v1.py │ ├── libra_rcnn │ │ ├── README.md │ │ ├── libra_fast_rcnn_r50_fpn_1x_coco.py │ │ ├── libra_faster_rcnn_r101_fpn_1x_coco.py │ │ ├── libra_faster_rcnn_r50_fpn_1x_coco.py │ │ ├── libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py │ │ └── libra_retinanet_r50_fpn_1x_coco.py │ ├── lvis │ │ ├── README.md │ │ ├── mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py │ │ ├── mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py │ │ ├── mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py │ │ ├── mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py │ │ ├── mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py │ │ ├── mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py │ │ ├── mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py │ │ └── mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py │ ├── mask_rcnn │ │ ├── README.md │ │ ├── mask_rcnn_r101_caffe_fpn_1x_coco.py │ │ ├── mask_rcnn_r101_fpn_1x_coco.py │ │ ├── mask_rcnn_r101_fpn_2x_coco.py │ │ ├── mask_rcnn_r50_caffe_c4_1x_coco.py │ │ ├── mask_rcnn_r50_caffe_fpn_1x_coco.py │ │ ├── mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py │ │ ├── mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py │ │ ├── mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py │ │ ├── mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py │ │ ├── mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py │ │ ├── mask_rcnn_r50_fpn_1x_coco.py │ │ ├── mask_rcnn_r50_fpn_2x_coco.py │ │ ├── mask_rcnn_r50_fpn_poly_1x_coco.py │ │ ├── mask_rcnn_x101_32x4d_fpn_1x_coco.py │ │ ├── mask_rcnn_x101_32x4d_fpn_2x_coco.py │ │ ├── mask_rcnn_x101_32x8d_fpn_1x_coco.py │ │ ├── mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py │ │ ├── mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py │ │ ├── mask_rcnn_x101_64x4d_fpn_1x_coco.py │ │ └── mask_rcnn_x101_64x4d_fpn_2x_coco.py │ ├── ms_rcnn │ │ ├── README.md │ │ ├── ms_rcnn_r101_caffe_fpn_1x_coco.py │ │ ├── ms_rcnn_r101_caffe_fpn_2x_coco.py │ │ ├── ms_rcnn_r50_caffe_fpn_1x_coco.py │ │ ├── ms_rcnn_r50_caffe_fpn_2x_coco.py │ │ ├── ms_rcnn_r50_fpn_1x_coco.py │ │ ├── ms_rcnn_x101_32x4d_fpn_1x_coco.py │ │ ├── ms_rcnn_x101_64x4d_fpn_1x_coco.py │ │ └── ms_rcnn_x101_64x4d_fpn_2x_coco.py │ ├── nas_fcos │ │ ├── README.md │ │ ├── nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py │ │ └── nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py │ ├── nas_fpn │ │ ├── README.md │ │ ├── retinanet_r50_fpn_crop640_50e_coco.py │ │ └── retinanet_r50_nasfpn_crop640_50e_coco.py │ ├── paa │ │ ├── README.md │ │ ├── paa_r101_fpn_1x_coco.py │ │ ├── paa_r101_fpn_2x_coco.py │ │ ├── paa_r101_fpn_mstrain_3x_coco.py │ │ ├── paa_r50_fpn_1.5x_coco.py │ │ ├── paa_r50_fpn_1x_coco.py │ │ ├── paa_r50_fpn_2x_coco.py │ │ └── paa_r50_fpn_mstrain_3x_coco.py │ ├── pafpn │ │ ├── README.md │ │ └── faster_rcnn_r50_pafpn_1x_coco.py │ ├── pascal_voc │ │ ├── README.md │ │ ├── faster_rcnn_r50_fpn_1x_voc0712.py │ │ ├── faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py │ │ ├── ssd300_voc0712.py │ │ └── ssd512_voc0712.py │ ├── pisa │ │ ├── README.md │ │ ├── pisa_faster_rcnn_r50_fpn_1x_coco.py │ │ ├── pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py │ │ ├── pisa_mask_rcnn_r50_fpn_1x_coco.py │ │ ├── pisa_mask_rcnn_x101_32x4d_fpn_1x_coco.py │ │ ├── pisa_retinanet_r50_fpn_1x_coco.py │ │ ├── pisa_retinanet_x101_32x4d_fpn_1x_coco.py │ │ ├── pisa_ssd300_coco.py │ │ └── pisa_ssd512_coco.py │ ├── point_rend │ │ ├── README.md │ │ ├── point_rend_r50_caffe_fpn_mstrain_1x_coco.py │ │ └── point_rend_r50_caffe_fpn_mstrain_3x_coco.py │ ├── regnet │ │ ├── README.md │ │ ├── faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py │ │ ├── faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py │ │ ├── faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py │ │ ├── mask_rcnn_regnetx-12GF_fpn_1x_coco.py │ │ ├── mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py │ │ ├── mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py │ │ ├── mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py │ │ ├── mask_rcnn_regnetx-4GF_fpn_1x_coco.py │ │ ├── mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py │ │ ├── mask_rcnn_regnetx-8GF_fpn_1x_coco.py │ │ ├── retinanet_regnetx-1.6GF_fpn_1x_coco.py │ │ ├── retinanet_regnetx-3.2GF_fpn_1x_coco.py │ │ └── retinanet_regnetx-800MF_fpn_1x_coco.py │ ├── reppoints │ │ ├── README.md │ │ ├── bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py │ │ ├── bbox_r50_grid_fpn_gn-neck+head_1x_coco.py │ │ ├── reppoints.png │ │ ├── reppoints_minmax_r50_fpn_gn-neck+head_1x_coco.py │ │ ├── reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py │ │ ├── reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py │ │ ├── reppoints_moment_r50_fpn_1x_coco.py │ │ ├── reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py │ │ ├── reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py │ │ ├── reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py │ │ └── reppoints_partial_minmax_r50_fpn_gn-neck+head_1x_coco.py │ ├── res2net │ │ ├── README.md │ │ ├── cascade_mask_rcnn_r2_101_fpn_20e_coco.py │ │ ├── cascade_rcnn_r2_101_fpn_20e_coco.py │ │ ├── faster_rcnn_r2_101_fpn_2x_coco.py │ │ ├── htc_r2_101_fpn_20e_coco.py │ │ └── mask_rcnn_r2_101_fpn_2x_coco.py │ ├── resnest │ │ ├── README.md │ │ ├── cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py │ │ ├── cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py │ │ ├── cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py │ │ ├── cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py │ │ ├── faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py │ │ ├── faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py │ │ ├── mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py │ │ └── mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py │ ├── retinanet │ │ ├── README.md │ │ ├── retinanet_r101_caffe_fpn_1x_coco.py │ │ ├── retinanet_r101_fpn_1x_coco.py │ │ ├── retinanet_r101_fpn_2x_coco.py │ │ ├── retinanet_r50_caffe_fpn_1x_coco.py │ │ ├── retinanet_r50_caffe_fpn_mstrain_1x_coco.py │ │ ├── retinanet_r50_caffe_fpn_mstrain_2x_coco.py │ │ ├── retinanet_r50_caffe_fpn_mstrain_3x_coco.py │ │ ├── retinanet_r50_fpn_1x_coco.py │ │ ├── retinanet_r50_fpn_1x_voc0712.py │ │ ├── retinanet_r50_fpn_2x_coco.py │ │ ├── retinanet_swin-t_fpn.py │ │ ├── retinanet_swin-t_fpn_1x_voc0712.py │ │ ├── retinanet_x101_32x4d_fpn_1x_coco.py │ │ ├── retinanet_x101_32x4d_fpn_2x_coco.py │ │ ├── retinanet_x101_64x4d_fpn_1x_coco.py │ │ └── retinanet_x101_64x4d_fpn_2x_coco.py │ ├── rpn │ │ ├── README.md │ │ ├── rpn_r101_caffe_fpn_1x_coco.py │ │ ├── rpn_r101_fpn_1x_coco.py │ │ ├── rpn_r101_fpn_2x_coco.py │ │ ├── rpn_r50_caffe_c4_1x_coco.py │ │ ├── rpn_r50_caffe_fpn_1x_coco.py │ │ ├── rpn_r50_fpn_1x_coco.py │ │ ├── rpn_r50_fpn_2x_coco.py │ │ ├── rpn_x101_32x4d_fpn_1x_coco.py │ │ ├── rpn_x101_32x4d_fpn_2x_coco.py │ │ ├── rpn_x101_64x4d_fpn_1x_coco.py │ │ └── rpn_x101_64x4d_fpn_2x_coco.py │ ├── sabl │ │ ├── README.md │ │ ├── sabl_cascade_rcnn_r101_fpn_1x_coco.py │ │ ├── sabl_cascade_rcnn_r50_fpn_1x_coco.py │ │ ├── sabl_faster_rcnn_r101_fpn_1x_coco.py │ │ ├── sabl_faster_rcnn_r50_fpn_1x_coco.py │ │ ├── sabl_retinanet_r101_fpn_1x_coco.py │ │ ├── sabl_retinanet_r101_fpn_gn_1x_coco.py │ │ ├── sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py │ │ ├── sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py │ │ ├── sabl_retinanet_r50_fpn_1x_coco.py │ │ └── sabl_retinanet_r50_fpn_gn_1x_coco.py │ ├── scnet │ │ ├── README.md │ │ ├── scnet_r101_fpn_20e_coco.py │ │ ├── scnet_r50_fpn_1x_coco.py │ │ ├── scnet_r50_fpn_20e_coco.py │ │ ├── scnet_x101_64x4d_fpn_20e_coco.py │ │ └── scnet_x101_64x4d_fpn_8x1_20e_coco.py │ ├── scratch │ │ ├── README.md │ │ ├── faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py │ │ └── mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py │ ├── sparse_rcnn │ │ ├── README.md │ │ ├── sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py │ │ ├── sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py │ │ ├── sparse_rcnn_r50_fpn_1x_coco.py │ │ ├── sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py │ │ └── sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py │ ├── ssd │ │ ├── README.md │ │ ├── ssd300_coco.py │ │ └── ssd512_coco.py │ ├── swin │ │ ├── cascade_mask_rcnn_swin_base_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py │ │ ├── cascade_mask_rcnn_swin_small_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py │ │ ├── cascade_mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_1x_coco.py │ │ ├── cascade_mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco.py │ │ ├── mask_rcnn_swin_small_patch4_window7_mstrain_480-800_adamw_3x_coco.py │ │ ├── mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py │ │ ├── mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco.py │ │ ├── mask_reppoitsv2_swin_tiny_patch4_window7_mstrain_480_960_giou_gfocal_bifpn_adamw_3x_coco.py │ │ └── reppoitsv2_swin_tiny_patch4_window7_mstrain_480_960_giou_gfocal_bifpn_adamw_3x_coco.py │ ├── tridentnet │ │ ├── README.md │ │ ├── tridentnet_r50_caffe_1x_coco.py │ │ ├── tridentnet_r50_caffe_mstrain_1x_coco.py │ │ └── tridentnet_r50_caffe_mstrain_3x_coco.py │ ├── vfnet │ │ ├── README.md │ │ ├── vfnet_r101_fpn_1x_coco.py │ │ ├── vfnet_r101_fpn_2x_coco.py │ │ ├── vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py │ │ ├── vfnet_r101_fpn_mstrain_2x_coco.py │ │ ├── vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py │ │ ├── vfnet_r2_101_fpn_mstrain_2x_coco.py │ │ ├── vfnet_r50_fpn_1x_coco.py │ │ ├── vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py │ │ ├── vfnet_r50_fpn_mstrain_2x_coco.py │ │ ├── vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py │ │ ├── vfnet_x101_32x4d_fpn_mstrain_2x_coco.py │ │ ├── vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py │ │ └── vfnet_x101_64x4d_fpn_mstrain_2x_coco.py │ ├── wider_face │ │ ├── README.md │ │ └── ssd300_wider_face.py │ ├── yolact │ │ ├── README.md │ │ ├── yolact_r101_1x8_coco.py │ │ ├── yolact_r50_1x8_coco.py │ │ └── yolact_r50_8x8_coco.py │ └── yolo │ │ ├── README.md │ │ ├── yolov3_d53_320_273e_coco.py │ │ ├── yolov3_d53_mstrain-416_273e_coco.py │ │ └── yolov3_d53_mstrain-608_273e_coco.py ├── demo │ ├── MMDet_Tutorial.ipynb │ ├── create_result_gif.py │ ├── demo.jpg │ ├── demo.mp4 │ ├── image_demo.py │ ├── inference_demo.ipynb │ ├── video_demo.py │ └── webcam_demo.py ├── docker │ ├── Dockerfile │ └── serve │ │ ├── Dockerfile │ │ ├── config.properties │ │ └── entrypoint.sh ├── docs │ ├── 1_exist_data_model.md │ ├── 2_new_data_model.md │ ├── 3_exist_data_new_model.md │ ├── Makefile │ ├── api.rst │ ├── changelog.md │ ├── compatibility.md │ ├── conf.py │ ├── conventions.md │ ├── faq.md │ ├── get_started.md │ ├── index.rst │ ├── make.bat │ ├── model_zoo.md │ ├── projects.md │ ├── robustness_benchmarking.md │ ├── stat.py │ ├── tutorials │ │ ├── config.md │ │ ├── customize_dataset.md │ │ ├── customize_losses.md │ │ ├── customize_models.md │ │ ├── customize_runtime.md │ │ ├── data_pipeline.md │ │ ├── finetune.md │ │ ├── index.rst │ │ ├── onnx2tensorrt.md │ │ └── pytorch2onnx.md │ └── useful_tools.md ├── log │ ├── ade20k │ │ ├── full.txt │ │ └── mona.txt │ ├── voc │ │ ├── adapter.txt │ │ ├── adaptformer.txt │ │ ├── lora.txt │ │ └── mona.txt │ └── 归档.zip ├── mmcv_custom │ ├── __init__.py │ ├── checkpoint.py │ └── runner │ │ ├── __init__.py │ │ ├── checkpoint.py │ │ └── epoch_based_runner.py ├── mmdet │ ├── __init__.py │ ├── apis │ │ ├── __init__.py │ │ ├── inference.py │ │ ├── test.py │ │ └── train.py │ ├── core │ │ ├── __init__.py │ │ ├── anchor │ │ │ ├── __init__.py │ │ │ ├── anchor_generator.py │ │ │ ├── builder.py │ │ │ ├── point_generator.py │ │ │ └── utils.py │ │ ├── bbox │ │ │ ├── __init__.py │ │ │ ├── assigners │ │ │ │ ├── __init__.py │ │ │ │ ├── approx_max_iou_assigner.py │ │ │ │ ├── assign_result.py │ │ │ │ ├── atss_assigner.py │ │ │ │ ├── atss_assigner_v2.py │ │ │ │ ├── base_assigner.py │ │ │ │ ├── center_region_assigner.py │ │ │ │ ├── grid_assigner.py │ │ │ │ ├── hungarian_assigner.py │ │ │ │ ├── max_iou_assigner.py │ │ │ │ ├── point_assigner.py │ │ │ │ ├── point_assigner_v2.py │ │ │ │ ├── point_hm_assigner.py │ │ │ │ └── region_assigner.py │ │ │ ├── builder.py │ │ │ ├── coder │ │ │ │ ├── __init__.py │ │ │ │ ├── base_bbox_coder.py │ │ │ │ ├── bucketing_bbox_coder.py │ │ │ │ ├── delta_xywh_bbox_coder.py │ │ │ │ ├── legacy_delta_xywh_bbox_coder.py │ │ │ │ ├── pseudo_bbox_coder.py │ │ │ │ ├── tblr_bbox_coder.py │ │ │ │ └── yolo_bbox_coder.py │ │ │ ├── demodata.py │ │ │ ├── iou_calculators │ │ │ │ ├── __init__.py │ │ │ │ ├── builder.py │ │ │ │ └── iou2d_calculator.py │ │ │ ├── match_costs │ │ │ │ ├── __init__.py │ │ │ │ ├── builder.py │ │ │ │ └── match_cost.py │ │ │ ├── samplers │ │ │ │ ├── __init__.py │ │ │ │ ├── base_sampler.py │ │ │ │ ├── combined_sampler.py │ │ │ │ ├── instance_balanced_pos_sampler.py │ │ │ │ ├── iou_balanced_neg_sampler.py │ │ │ │ ├── ohem_sampler.py │ │ │ │ ├── pseudo_sampler.py │ │ │ │ ├── random_sampler.py │ │ │ │ ├── sampling_result.py │ │ │ │ └── score_hlr_sampler.py │ │ │ └── transforms.py │ │ ├── evaluation │ │ │ ├── __init__.py │ │ │ ├── bbox_overlaps.py │ │ │ ├── class_names.py │ │ │ ├── eval_hooks.py │ │ │ ├── mean_ap.py │ │ │ └── recall.py │ │ ├── export │ │ │ ├── __init__.py │ │ │ └── pytorch2onnx.py │ │ ├── mask │ │ │ ├── __init__.py │ │ │ ├── mask_target.py │ │ │ ├── structures.py │ │ │ └── utils.py │ │ ├── post_processing │ │ │ ├── __init__.py │ │ │ ├── bbox_nms.py │ │ │ └── merge_augs.py │ │ ├── utils │ │ │ ├── __init__.py │ │ │ ├── dist_utils.py │ │ │ └── misc.py │ │ └── visualization │ │ │ ├── __init__.py │ │ │ └── image.py │ ├── datasets │ │ ├── __init__.py │ │ ├── builder.py │ │ ├── cityscapes.py │ │ ├── coco.py │ │ ├── custom.py │ │ ├── dataset_wrappers.py │ │ ├── deepfashion.py │ │ ├── lvis.py │ │ ├── pipelines │ │ │ ├── __init__.py │ │ │ ├── auto_augment.py │ │ │ ├── compose.py │ │ │ ├── formating.py │ │ │ ├── instaboost.py │ │ │ ├── loading.py │ │ │ ├── test_time_aug.py │ │ │ └── transforms.py │ │ ├── samplers │ │ │ ├── __init__.py │ │ │ ├── distributed_sampler.py │ │ │ └── group_sampler.py │ │ ├── utils.py │ │ ├── voc.py │ │ ├── wider_face.py │ │ └── xml_style.py │ ├── models │ │ ├── __init__.py │ │ ├── backbones │ │ │ ├── LowRankModules.py │ │ │ ├── __init__.py │ │ │ ├── darknet.py │ │ │ ├── detectors_resnet.py │ │ │ ├── detectors_resnext.py │ │ │ ├── hourglass.py │ │ │ ├── hrnet.py │ │ │ ├── lora_layers.py │ │ │ ├── regnet.py │ │ │ ├── res2net.py │ │ │ ├── resnest.py │ │ │ ├── resnet.py │ │ │ ├── resnext.py │ │ │ ├── ssd_vgg.py │ │ │ ├── swin_transformer.py │ │ │ ├── swin_transformer_adapter.py │ │ │ ├── swin_transformer_adaptformer.py │ │ │ ├── swin_transformer_bitfit.py │ │ │ ├── swin_transformer_fixed.py │ │ │ ├── swin_transformer_lora.py │ │ │ ├── swin_transformer_mona.py │ │ │ ├── swin_transformer_norm_tuning.py │ │ │ ├── swin_transformer_partial_1.py │ │ │ └── trident_resnet.py │ │ ├── builder.py │ │ ├── dense_heads │ │ │ ├── __init__.py │ │ │ ├── anchor_free_head.py │ │ │ ├── anchor_head.py │ │ │ ├── atss_head.py │ │ │ ├── base_dense_head.py │ │ │ ├── cascade_rpn_head.py │ │ │ ├── centripetal_head.py │ │ │ ├── corner_head.py │ │ │ ├── dense_test_mixins.py │ │ │ ├── embedding_rpn_head.py │ │ │ ├── fcos_head.py │ │ │ ├── fovea_head.py │ │ │ ├── free_anchor_retina_head.py │ │ │ ├── fsaf_head.py │ │ │ ├── ga_retina_head.py │ │ │ ├── ga_rpn_head.py │ │ │ ├── gfl_head.py │ │ │ ├── guided_anchor_head.py │ │ │ ├── ld_head.py │ │ │ ├── nasfcos_head.py │ │ │ ├── paa_head.py │ │ │ ├── pisa_retinanet_head.py │ │ │ ├── pisa_ssd_head.py │ │ │ ├── reppoints_head.py │ │ │ ├── reppoints_v2_head.py │ │ │ ├── retina_head.py │ │ │ ├── retina_sepbn_head.py │ │ │ ├── rpn_head.py │ │ │ ├── rpn_test_mixin.py │ │ │ ├── sabl_retina_head.py │ │ │ ├── ssd_head.py │ │ │ ├── transformer_head.py │ │ │ ├── vfnet_head.py │ │ │ ├── yolact_head.py │ │ │ └── yolo_head.py │ │ ├── detectors │ │ │ ├── __init__.py │ │ │ ├── atss.py │ │ │ ├── base.py │ │ │ ├── cascade_rcnn.py │ │ │ ├── cornernet.py │ │ │ ├── detr.py │ │ │ ├── fast_rcnn.py │ │ │ ├── faster_rcnn.py │ │ │ ├── fcos.py │ │ │ ├── fovea.py │ │ │ ├── fsaf.py │ │ │ ├── gfl.py │ │ │ ├── grid_rcnn.py │ │ │ ├── htc.py │ │ │ ├── kd_one_stage.py │ │ │ ├── mask_rcnn.py │ │ │ ├── mask_reppoints_v2_detector.py │ │ │ ├── mask_scoring_rcnn.py │ │ │ ├── nasfcos.py │ │ │ ├── paa.py │ │ │ ├── point_rend.py │ │ │ ├── reppoints_detector.py │ │ │ ├── reppoints_v2_detector.py │ │ │ ├── retinanet.py │ │ │ ├── rpn.py │ │ │ ├── scnet.py │ │ │ ├── single_stage.py │ │ │ ├── sparse_rcnn.py │ │ │ ├── trident_faster_rcnn.py │ │ │ ├── two_stage.py │ │ │ ├── vfnet.py │ │ │ ├── yolact.py │ │ │ └── yolo.py │ │ ├── losses │ │ │ ├── __init__.py │ │ │ ├── accuracy.py │ │ │ ├── ae_loss.py │ │ │ ├── balanced_l1_loss.py │ │ │ ├── cross_entropy_loss.py │ │ │ ├── focal_loss.py │ │ │ ├── gaussian_focal_loss.py │ │ │ ├── gfocal_loss.py │ │ │ ├── ghm_loss.py │ │ │ ├── iou_loss.py │ │ │ ├── kd_loss.py │ │ │ ├── mse_loss.py │ │ │ ├── pisa_loss.py │ │ │ ├── smooth_l1_loss.py │ │ │ ├── utils.py │ │ │ └── varifocal_loss.py │ │ ├── necks │ │ │ ├── __init__.py │ │ │ ├── bfp.py │ │ │ ├── bifpn.py │ │ │ ├── channel_mapper.py │ │ │ ├── fpg.py │ │ │ ├── fpn.py │ │ │ ├── fpn_carafe.py │ │ │ ├── hrfpn.py │ │ │ ├── nas_fpn.py │ │ │ ├── nasfcos_fpn.py │ │ │ ├── pafpn.py │ │ │ ├── rfp.py │ │ │ └── yolo_neck.py │ │ ├── roi_heads │ │ │ ├── __init__.py │ │ │ ├── base_roi_head.py │ │ │ ├── bbox_heads │ │ │ │ ├── __init__.py │ │ │ │ ├── bbox_head.py │ │ │ │ ├── convfc_bbox_head.py │ │ │ │ ├── dii_head.py │ │ │ │ ├── double_bbox_head.py │ │ │ │ ├── sabl_head.py │ │ │ │ └── scnet_bbox_head.py │ │ │ ├── cascade_roi_head.py │ │ │ ├── double_roi_head.py │ │ │ ├── dynamic_roi_head.py │ │ │ ├── grid_roi_head.py │ │ │ ├── htc_roi_head.py │ │ │ ├── mask_heads │ │ │ │ ├── __init__.py │ │ │ │ ├── coarse_mask_head.py │ │ │ │ ├── condconv_mask_head.py │ │ │ │ ├── fcn_mask_head.py │ │ │ │ ├── feature_relay_head.py │ │ │ │ ├── fused_semantic_head.py │ │ │ │ ├── global_context_head.py │ │ │ │ ├── grid_head.py │ │ │ │ ├── htc_mask_head.py │ │ │ │ ├── mask_point_head.py │ │ │ │ ├── maskiou_head.py │ │ │ │ ├── scnet_mask_head.py │ │ │ │ └── scnet_semantic_head.py │ │ │ ├── mask_scoring_roi_head.py │ │ │ ├── pisa_roi_head.py │ │ │ ├── point_rend_roi_head.py │ │ │ ├── roi_extractors │ │ │ │ ├── __init__.py │ │ │ │ ├── base_roi_extractor.py │ │ │ │ ├── generic_roi_extractor.py │ │ │ │ └── single_level_roi_extractor.py │ │ │ ├── scnet_roi_head.py │ │ │ ├── shared_heads │ │ │ │ ├── __init__.py │ │ │ │ └── res_layer.py │ │ │ ├── sparse_roi_head.py │ │ │ ├── standard_roi_head.py │ │ │ ├── test_mixins.py │ │ │ └── trident_roi_head.py │ │ └── utils │ │ │ ├── __init__.py │ │ │ ├── builder.py │ │ │ ├── gaussian_target.py │ │ │ ├── positional_encoding.py │ │ │ ├── res_layer.py │ │ │ └── transformer.py │ ├── utils │ │ ├── __init__.py │ │ ├── collect_env.py │ │ ├── common.py │ │ ├── contextmanagers.py │ │ ├── instances.py │ │ ├── logger.py │ │ ├── optimizer.py │ │ ├── profiling.py │ │ ├── util_mixins.py │ │ └── util_random.py │ └── version.py ├── mona_configs │ ├── _base_ │ │ ├── datasets │ │ │ ├── cityscapes_detection.py │ │ │ ├── cityscapes_instance.py │ │ │ ├── coco_detection.py │ │ │ ├── coco_instance.py │ │ │ ├── coco_instance_sample_1.py │ │ │ ├── coco_instance_semantic.py │ │ │ ├── deepfashion.py │ │ │ ├── lvis_v0.5_instance.py │ │ │ ├── lvis_v1_instance.py │ │ │ ├── voc0712.py │ │ │ └── wider_face.py │ │ ├── default_runtime.py │ │ ├── models │ │ │ ├── cascade_mask_rcnn_r50_fpn.py │ │ │ ├── cascade_mask_rcnn_swin_fpn.py │ │ │ ├── cascade_rcnn_r50_fpn.py │ │ │ ├── fast_rcnn_r50_fpn.py │ │ │ ├── fast_rcnn_swin_fpn.py │ │ │ ├── faster_rcnn_r50_caffe_c4.py │ │ │ ├── faster_rcnn_r50_caffe_dc5.py │ │ │ ├── faster_rcnn_r50_fpn.py │ │ │ ├── faster_rcnn_swin_fpn.py │ │ │ ├── mask_rcnn_r50_caffe_c4.py │ │ │ ├── mask_rcnn_r50_fpn.py │ │ │ ├── mask_rcnn_swin_fpn.py │ │ │ ├── mask_reppointsv2_swin_bifpn.py │ │ │ ├── reppointsv2_swin_bifpn.py │ │ │ ├── retinanet_r50_fpn.py │ │ │ ├── retinanet_swin_large_fpn.py │ │ │ ├── rpn_r50_caffe_c4.py │ │ │ ├── rpn_r50_fpn.py │ │ │ └── ssd300.py │ │ └── schedules │ │ │ ├── schedule_1x.py │ │ │ ├── schedule_20e.py │ │ │ └── schedule_2x.py │ ├── swin-b_coco │ │ ├── cascade_mask_swin_base_3x_coco_sample_1_bs_16_LN.py │ │ ├── cascade_mask_swin_base_3x_coco_sample_1_bs_16_adapter.py │ │ ├── cascade_mask_swin_base_3x_coco_sample_1_bs_16_adaptformer.py │ │ ├── cascade_mask_swin_base_3x_coco_sample_1_bs_16_bitfit.py │ │ ├── cascade_mask_swin_base_3x_coco_sample_1_bs_16_fixed.py │ │ ├── cascade_mask_swin_base_3x_coco_sample_1_bs_16_full.py │ │ ├── cascade_mask_swin_base_3x_coco_sample_1_bs_16_lora.py │ │ ├── cascade_mask_swin_base_3x_coco_sample_1_bs_16_mona.py │ │ └── cascade_mask_swin_base_3x_coco_sample_1_bs_16_partial_1.py │ ├── swin-l_voc │ │ ├── retinanet_swin-t.py │ │ ├── voc_retinanet_swin_large_1x_LN.py │ │ ├── voc_retinanet_swin_large_1x_adapter.py │ │ ├── voc_retinanet_swin_large_1x_adaptformer.py │ │ ├── voc_retinanet_swin_large_1x_bitfit.py │ │ ├── voc_retinanet_swin_large_1x_fixed.py │ │ ├── voc_retinanet_swin_large_1x_full.py │ │ ├── voc_retinanet_swin_large_1x_lora.py │ │ ├── voc_retinanet_swin_large_1x_mona.py │ │ └── voc_retinanet_swin_large_1x_partial_1.py │ └── test.py ├── pytest.ini ├── requirements.txt ├── requirements │ ├── build.txt │ ├── docs.txt │ ├── optional.txt │ ├── readthedocs.txt │ ├── runtime.txt │ └── tests.txt ├── resources │ ├── coco_test_12510.jpg │ ├── corruptions_sev_3.png │ ├── data_pipeline.png │ ├── loss_curve.png │ └── mmdet-logo.png ├── setup.cfg ├── setup.py ├── tests │ ├── test_data │ │ ├── test_datasets │ │ │ ├── test_coco_dataset.py │ │ │ ├── test_common.py │ │ │ ├── test_custom_dataset.py │ │ │ ├── test_dataset_wrapper.py │ │ │ └── test_xml_dataset.py │ │ ├── test_pipelines │ │ │ ├── test_formatting.py │ │ │ ├── test_loading.py │ │ │ ├── test_sampler.py │ │ │ └── test_transform │ │ │ │ ├── test_img_augment.py │ │ │ │ ├── test_models_aug_test.py │ │ │ │ ├── test_rotate.py │ │ │ │ ├── test_shear.py │ │ │ │ ├── test_transform.py │ │ │ │ └── test_translate.py │ │ └── test_utils.py │ ├── test_metrics │ │ ├── test_box_overlap.py │ │ └── test_losses.py │ ├── test_models │ │ ├── test_backbones │ │ │ ├── __init__.py │ │ │ ├── test_hourglass.py │ │ │ ├── test_regnet.py │ │ │ ├── test_renext.py │ │ │ ├── test_res2net.py │ │ │ ├── test_resnest.py │ │ │ ├── test_resnet.py │ │ │ ├── test_trident_resnet.py │ │ │ └── utils.py │ │ ├── test_dense_heads │ │ │ ├── test_anchor_head.py │ │ │ ├── test_corner_head.py │ │ │ ├── test_fcos_head.py │ │ │ ├── test_fsaf_head.py │ │ │ ├── test_ga_anchor_head.py │ │ │ ├── test_ld_head.py │ │ │ ├── test_paa_head.py │ │ │ ├── test_pisa_head.py │ │ │ ├── test_sabl_retina_head.py │ │ │ ├── test_transformer_head.py │ │ │ ├── test_vfnet_head.py │ │ │ └── test_yolact_head.py │ │ ├── test_forward.py │ │ ├── test_necks.py │ │ ├── test_roi_heads │ │ │ ├── __init__.py │ │ │ ├── test_bbox_head.py │ │ │ ├── test_mask_head.py │ │ │ ├── test_roi_extractor.py │ │ │ ├── test_sabl_bbox_head.py │ │ │ └── utils.py │ │ └── test_utils │ │ │ ├── test_position_encoding.py │ │ │ └── test_transformer.py │ ├── test_onnx │ │ ├── __init__.py │ │ ├── test_head.py │ │ ├── test_neck.py │ │ └── utils.py │ ├── test_runtime │ │ ├── async_benchmark.py │ │ ├── test_async.py │ │ ├── test_config.py │ │ ├── test_eval_hook.py │ │ └── test_fp16.py │ └── test_utils │ │ ├── test_anchor.py │ │ ├── test_assigner.py │ │ ├── test_coder.py │ │ ├── test_masks.py │ │ ├── test_misc.py │ │ ├── test_version.py │ │ └── test_visualization.py └── tools │ ├── SwinPth2MmPth.py │ ├── analysis_tools │ ├── analyze_logs.py │ ├── analyze_results.py │ ├── benchmark.py │ ├── coco_error_analysis.py │ ├── eval_metric.py │ ├── get_flops.py │ ├── robustness_eval.py │ └── test_robustness.py │ ├── dataset_converters │ ├── cityscapes.py │ └── pascal_voc.py │ ├── deployment │ ├── mmdet2torchserve.py │ ├── mmdet_handler.py │ ├── onnx2tensorrt.py │ └── pytorch2onnx.py │ ├── dist_test.sh │ ├── dist_train.sh │ ├── dist_train_debug.sh │ ├── misc │ ├── browse_dataset.py │ └── print_config.py │ ├── model_converters │ ├── detectron2pytorch.py │ ├── publish_model.py │ ├── regnet2mmdet.py │ └── upgrade_model_version.py │ ├── slurm_test.sh │ ├── slurm_train.sh │ ├── test.py │ ├── train.py │ └── train_eval-non-dist.py ├── Swin-Transformer-Semantic-Segmentation ├── .dev │ ├── gather_models.py │ └── upload_modelzoo.py ├── .github │ ├── CODE_OF_CONDUCT.md │ ├── CONTRIBUTING.md │ ├── ISSUE_TEMPLATE │ │ ├── config.yml │ │ ├── error-report.md │ │ ├── feature_request.md │ │ └── general_questions.md │ └── workflows │ │ ├── build.yml │ │ └── deploy.yml ├── .gitignore ├── .pre-commit-config.yaml ├── .readthedocs.yml ├── LICENSE ├── README.md ├── configs │ ├── _base_ │ │ ├── datasets │ │ │ ├── ade20k.py │ │ │ ├── chase_db1.py │ │ │ ├── cityscapes.py │ │ │ ├── cityscapes_769x769.py │ │ │ ├── drive.py │ │ │ ├── hrf.py │ │ │ ├── pascal_context.py │ │ │ ├── pascal_voc12.py │ │ │ ├── pascal_voc12_aug.py │ │ │ └── stare.py │ │ ├── default_runtime.py │ │ ├── models │ │ │ ├── ann_r50-d8.py │ │ │ ├── apcnet_r50-d8.py │ │ │ ├── ccnet_r50-d8.py │ │ │ ├── cgnet.py │ │ │ ├── danet_r50-d8.py │ │ │ ├── deeplabv3_r50-d8.py │ │ │ ├── deeplabv3_unet_s5-d16.py │ │ │ ├── deeplabv3plus_r50-d8.py │ │ │ ├── dmnet_r50-d8.py │ │ │ ├── dnl_r50-d8.py │ │ │ ├── emanet_r50-d8.py │ │ │ ├── encnet_r50-d8.py │ │ │ ├── fast_scnn.py │ │ │ ├── fcn_hr18.py │ │ │ ├── fcn_r50-d8.py │ │ │ ├── fcn_unet_s5-d16.py │ │ │ ├── fpn_r50.py │ │ │ ├── gcnet_r50-d8.py │ │ │ ├── lraspp_m-v3-d8.py │ │ │ ├── nonlocal_r50-d8.py │ │ │ ├── ocrnet_hr18.py │ │ │ ├── ocrnet_r50-d8.py │ │ │ ├── pointrend_r50.py │ │ │ ├── psanet_r50-d8.py │ │ │ ├── pspnet_r50-d8.py │ │ │ ├── pspnet_unet_s5-d16.py │ │ │ ├── upernet_r50.py │ │ │ └── upernet_swin.py │ │ └── schedules │ │ │ ├── schedule_160k.py │ │ │ ├── schedule_20k.py │ │ │ ├── schedule_40k.py │ │ │ └── schedule_80k.py │ ├── ann │ │ ├── README.md │ │ ├── ann_r101-d8_512x1024_40k_cityscapes.py │ │ ├── ann_r101-d8_512x1024_80k_cityscapes.py │ │ ├── ann_r101-d8_512x512_160k_ade20k.py │ │ ├── ann_r101-d8_512x512_20k_voc12aug.py │ │ ├── ann_r101-d8_512x512_40k_voc12aug.py │ │ ├── ann_r101-d8_512x512_80k_ade20k.py │ │ ├── ann_r101-d8_769x769_40k_cityscapes.py │ │ ├── ann_r101-d8_769x769_80k_cityscapes.py │ │ ├── ann_r50-d8_512x1024_40k_cityscapes.py │ │ ├── ann_r50-d8_512x1024_80k_cityscapes.py │ │ ├── ann_r50-d8_512x512_160k_ade20k.py │ │ ├── ann_r50-d8_512x512_20k_voc12aug.py │ │ ├── ann_r50-d8_512x512_40k_voc12aug.py │ │ ├── ann_r50-d8_512x512_80k_ade20k.py │ │ ├── ann_r50-d8_769x769_40k_cityscapes.py │ │ └── ann_r50-d8_769x769_80k_cityscapes.py │ ├── apcnet │ │ ├── README.md │ │ ├── apcnet_r101-d8_512x1024_40k_cityscapes.py │ │ ├── apcnet_r101-d8_512x1024_80k_cityscapes.py │ │ ├── apcnet_r101-d8_512x512_160k_ade20k.py │ │ ├── apcnet_r101-d8_512x512_80k_ade20k.py │ │ ├── apcnet_r101-d8_769x769_40k_cityscapes.py │ │ ├── apcnet_r101-d8_769x769_80k_cityscapes.py │ │ ├── apcnet_r50-d8_512x1024_40k_cityscapes.py │ │ ├── apcnet_r50-d8_512x1024_80k_cityscapes.py │ │ ├── apcnet_r50-d8_512x512_160k_ade20k.py │ │ ├── apcnet_r50-d8_512x512_80k_ade20k.py │ │ ├── apcnet_r50-d8_769x769_40k_cityscapes.py │ │ └── apcnet_r50-d8_769x769_80k_cityscapes.py │ ├── ccnet │ │ ├── README.md │ │ ├── ccnet_r101-d8_512x1024_40k_cityscapes.py │ │ ├── ccnet_r101-d8_512x1024_80k_cityscapes.py │ │ ├── ccnet_r101-d8_512x512_160k_ade20k.py │ │ ├── ccnet_r101-d8_512x512_20k_voc12aug.py │ │ ├── ccnet_r101-d8_512x512_40k_voc12aug.py │ │ ├── ccnet_r101-d8_512x512_80k_ade20k.py │ │ ├── ccnet_r101-d8_769x769_40k_cityscapes.py │ │ ├── ccnet_r101-d8_769x769_80k_cityscapes.py │ │ ├── ccnet_r50-d8_512x1024_40k_cityscapes.py │ │ ├── ccnet_r50-d8_512x1024_80k_cityscapes.py │ │ ├── ccnet_r50-d8_512x512_160k_ade20k.py │ │ ├── ccnet_r50-d8_512x512_20k_voc12aug.py │ │ ├── ccnet_r50-d8_512x512_40k_voc12aug.py │ │ ├── ccnet_r50-d8_512x512_80k_ade20k.py │ │ ├── ccnet_r50-d8_769x769_40k_cityscapes.py │ │ └── ccnet_r50-d8_769x769_80k_cityscapes.py │ ├── cgnet │ │ ├── README.md │ │ ├── cgnet_512x1024_60k_cityscapes.py │ │ └── cgnet_680x680_60k_cityscapes.py │ ├── danet │ │ ├── README.md │ │ ├── danet_r101-d8_512x1024_40k_cityscapes.py │ │ ├── danet_r101-d8_512x1024_80k_cityscapes.py │ │ ├── danet_r101-d8_512x512_160k_ade20k.py │ │ ├── danet_r101-d8_512x512_20k_voc12aug.py │ │ ├── danet_r101-d8_512x512_40k_voc12aug.py │ │ ├── danet_r101-d8_512x512_80k_ade20k.py │ │ ├── danet_r101-d8_769x769_40k_cityscapes.py │ │ ├── danet_r101-d8_769x769_80k_cityscapes.py │ │ ├── danet_r50-d8_512x1024_40k_cityscapes.py │ │ ├── danet_r50-d8_512x1024_80k_cityscapes.py │ │ ├── danet_r50-d8_512x512_160k_ade20k.py │ │ ├── danet_r50-d8_512x512_20k_voc12aug.py │ │ ├── danet_r50-d8_512x512_40k_voc12aug.py │ │ ├── danet_r50-d8_512x512_80k_ade20k.py │ │ ├── danet_r50-d8_769x769_40k_cityscapes.py │ │ └── danet_r50-d8_769x769_80k_cityscapes.py │ ├── deeplabv3 │ │ ├── README.md │ │ ├── deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes.py │ │ ├── deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py │ │ ├── deeplabv3_r101-d8_480x480_40k_pascal_context.py │ │ ├── deeplabv3_r101-d8_480x480_80k_pascal_context.py │ │ ├── deeplabv3_r101-d8_512x1024_40k_cityscapes.py │ │ ├── deeplabv3_r101-d8_512x1024_80k_cityscapes.py │ │ ├── deeplabv3_r101-d8_512x512_160k_ade20k.py │ │ ├── deeplabv3_r101-d8_512x512_20k_voc12aug.py │ │ ├── deeplabv3_r101-d8_512x512_40k_voc12aug.py │ │ ├── deeplabv3_r101-d8_512x512_80k_ade20k.py │ │ ├── deeplabv3_r101-d8_769x769_40k_cityscapes.py │ │ ├── deeplabv3_r101-d8_769x769_80k_cityscapes.py │ │ ├── deeplabv3_r101b-d8_512x1024_80k_cityscapes.py │ │ ├── deeplabv3_r101b-d8_769x769_80k_cityscapes.py │ │ ├── deeplabv3_r18-d8_512x1024_80k_cityscapes.py │ │ ├── deeplabv3_r18-d8_769x769_80k_cityscapes.py │ │ ├── deeplabv3_r18b-d8_512x1024_80k_cityscapes.py │ │ ├── deeplabv3_r18b-d8_769x769_80k_cityscapes.py │ │ ├── deeplabv3_r50-d8_480x480_40k_pascal_context.py │ │ ├── deeplabv3_r50-d8_480x480_80k_pascal_context.py │ │ ├── deeplabv3_r50-d8_512x1024_40k_cityscapes.py │ │ ├── deeplabv3_r50-d8_512x1024_80k_cityscapes.py │ │ ├── deeplabv3_r50-d8_512x512_160k_ade20k.py │ │ ├── deeplabv3_r50-d8_512x512_20k_voc12aug.py │ │ ├── deeplabv3_r50-d8_512x512_40k_voc12aug.py │ │ ├── deeplabv3_r50-d8_512x512_80k_ade20k.py │ │ ├── deeplabv3_r50-d8_769x769_40k_cityscapes.py │ │ ├── deeplabv3_r50-d8_769x769_80k_cityscapes.py │ │ ├── deeplabv3_r50b-d8_512x1024_80k_cityscapes.py │ │ └── deeplabv3_r50b-d8_769x769_80k_cityscapes.py │ ├── deeplabv3plus │ │ ├── README.md │ │ ├── deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py │ │ ├── deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py │ │ ├── deeplabv3plus_r101-d8_480x480_40k_pascal_context.py │ │ ├── deeplabv3plus_r101-d8_480x480_80k_pascal_context.py │ │ ├── deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py │ │ ├── deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py │ │ ├── deeplabv3plus_r101-d8_512x512_160k_ade20k.py │ │ ├── deeplabv3plus_r101-d8_512x512_20k_voc12aug.py │ │ ├── deeplabv3plus_r101-d8_512x512_40k_voc12aug.py │ │ ├── deeplabv3plus_r101-d8_512x512_80k_ade20k.py │ │ ├── deeplabv3plus_r101-d8_769x769_40k_cityscapes.py │ │ ├── deeplabv3plus_r101-d8_769x769_80k_cityscapes.py │ │ ├── deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py │ │ ├── deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py │ │ ├── deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py │ │ ├── deeplabv3plus_r18-d8_769x769_80k_cityscapes.py │ │ ├── deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py │ │ ├── deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py │ │ ├── deeplabv3plus_r50-d8_480x480_40k_pascal_context.py │ │ ├── deeplabv3plus_r50-d8_480x480_80k_pascal_context.py │ │ ├── deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py │ │ ├── deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py │ │ ├── deeplabv3plus_r50-d8_512x512_160k_ade20k.py │ │ ├── deeplabv3plus_r50-d8_512x512_20k_voc12aug.py │ │ ├── deeplabv3plus_r50-d8_512x512_40k_voc12aug.py │ │ ├── deeplabv3plus_r50-d8_512x512_80k_ade20k.py │ │ ├── deeplabv3plus_r50-d8_769x769_40k_cityscapes.py │ │ ├── deeplabv3plus_r50-d8_769x769_80k_cityscapes.py │ │ ├── deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py │ │ └── deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py │ ├── dmnet │ │ ├── README.md │ │ ├── dmnet_r101-d8_512x1024_40k_cityscapes.py │ │ ├── dmnet_r101-d8_512x1024_80k_cityscapes.py │ │ ├── dmnet_r101-d8_512x512_160k_ade20k.py │ │ ├── dmnet_r101-d8_512x512_80k_ade20k.py │ │ ├── dmnet_r101-d8_769x769_40k_cityscapes.py │ │ ├── dmnet_r101-d8_769x769_80k_cityscapes.py │ │ ├── dmnet_r50-d8_512x1024_40k_cityscapes.py │ │ ├── dmnet_r50-d8_512x1024_80k_cityscapes.py │ │ ├── dmnet_r50-d8_512x512_160k_ade20k.py │ │ ├── dmnet_r50-d8_512x512_80k_ade20k.py │ │ ├── dmnet_r50-d8_769x769_40k_cityscapes.py │ │ └── dmnet_r50-d8_769x769_80k_cityscapes.py │ ├── dnlnet │ │ ├── README.md │ │ ├── dnl_r101-d8_512x1024_40k_cityscapes.py │ │ ├── dnl_r101-d8_512x1024_80k_cityscapes.py │ │ ├── dnl_r101-d8_512x512_160k_ade20k.py │ │ ├── dnl_r101-d8_512x512_80k_ade20k.py │ │ ├── dnl_r101-d8_769x769_40k_cityscapes.py │ │ ├── dnl_r101-d8_769x769_80k_cityscapes.py │ │ ├── dnl_r50-d8_512x1024_40k_cityscapes.py │ │ ├── dnl_r50-d8_512x1024_80k_cityscapes.py │ │ ├── dnl_r50-d8_512x512_160k_ade20k.py │ │ ├── dnl_r50-d8_512x512_80k_ade20k.py │ │ ├── dnl_r50-d8_769x769_40k_cityscapes.py │ │ └── dnl_r50-d8_769x769_80k_cityscapes.py │ ├── emanet │ │ ├── README.md │ │ ├── emanet_r101-d8_512x1024_80k_cityscapes.py │ │ ├── emanet_r101-d8_769x769_80k_cityscapes.py │ │ ├── emanet_r50-d8_512x1024_80k_cityscapes.py │ │ └── emanet_r50-d8_769x769_80k_cityscapes.py │ ├── encnet │ │ ├── README.md │ │ ├── encnet_r101-d8_512x1024_40k_cityscapes.py │ │ ├── encnet_r101-d8_512x1024_80k_cityscapes.py │ │ ├── encnet_r101-d8_512x512_160k_ade20k.py │ │ ├── encnet_r101-d8_512x512_20k_voc12aug.py │ │ ├── encnet_r101-d8_512x512_40k_voc12aug.py │ │ ├── encnet_r101-d8_512x512_80k_ade20k.py │ │ ├── encnet_r101-d8_769x769_40k_cityscapes.py │ │ ├── encnet_r101-d8_769x769_80k_cityscapes.py │ │ ├── encnet_r50-d8_512x1024_40k_cityscapes.py │ │ ├── encnet_r50-d8_512x1024_80k_cityscapes.py │ │ ├── encnet_r50-d8_512x512_160k_ade20k.py │ │ ├── encnet_r50-d8_512x512_20k_voc12aug.py │ │ ├── encnet_r50-d8_512x512_40k_voc12aug.py │ │ ├── encnet_r50-d8_512x512_80k_ade20k.py │ │ ├── encnet_r50-d8_769x769_40k_cityscapes.py │ │ ├── encnet_r50-d8_769x769_80k_cityscapes.py │ │ └── encnet_r50s-d8_512x512_80k_ade20k.py │ ├── fastscnn │ │ ├── README.md │ │ └── fast_scnn_4x8_80k_lr0.12_cityscapes.py │ ├── fcn │ │ ├── README.md │ │ ├── fcn_r101-d8_480x480_40k_pascal_context.py │ │ ├── fcn_r101-d8_480x480_80k_pascal_context.py │ │ ├── fcn_r101-d8_512x1024_40k_cityscapes.py │ │ ├── fcn_r101-d8_512x1024_80k_cityscapes.py │ │ ├── fcn_r101-d8_512x512_160k_ade20k.py │ │ ├── fcn_r101-d8_512x512_20k_voc12aug.py │ │ ├── fcn_r101-d8_512x512_40k_voc12aug.py │ │ ├── fcn_r101-d8_512x512_80k_ade20k.py │ │ ├── fcn_r101-d8_769x769_40k_cityscapes.py │ │ ├── fcn_r101-d8_769x769_80k_cityscapes.py │ │ ├── fcn_r101b-d8_512x1024_80k_cityscapes.py │ │ ├── fcn_r101b-d8_769x769_80k_cityscapes.py │ │ ├── fcn_r18-d8_512x1024_80k_cityscapes.py │ │ ├── fcn_r18-d8_769x769_80k_cityscapes.py │ │ ├── fcn_r18b-d8_512x1024_80k_cityscapes.py │ │ ├── fcn_r18b-d8_769x769_80k_cityscapes.py │ │ ├── fcn_r50-d8_480x480_40k_pascal_context.py │ │ ├── fcn_r50-d8_480x480_80k_pascal_context.py │ │ ├── fcn_r50-d8_512x1024_40k_cityscapes.py │ │ ├── fcn_r50-d8_512x1024_80k_cityscapes.py │ │ ├── fcn_r50-d8_512x512_160k_ade20k.py │ │ ├── fcn_r50-d8_512x512_20k_voc12aug.py │ │ ├── fcn_r50-d8_512x512_40k_voc12aug.py │ │ ├── fcn_r50-d8_512x512_80k_ade20k.py │ │ ├── fcn_r50-d8_769x769_40k_cityscapes.py │ │ ├── fcn_r50-d8_769x769_80k_cityscapes.py │ │ ├── fcn_r50b-d8_512x1024_80k_cityscapes.py │ │ └── fcn_r50b-d8_769x769_80k_cityscapes.py │ ├── fp16 │ │ ├── README.md │ │ ├── deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes.py │ │ ├── deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes.py │ │ ├── fcn_r101-d8_512x1024_80k_fp16_cityscapes.py │ │ └── pspnet_r101-d8_512x1024_80k_fp16_cityscapes.py │ ├── gcnet │ │ ├── README.md │ │ ├── gcnet_r101-d8_512x1024_40k_cityscapes.py │ │ ├── gcnet_r101-d8_512x1024_80k_cityscapes.py │ │ ├── gcnet_r101-d8_512x512_160k_ade20k.py │ │ ├── gcnet_r101-d8_512x512_20k_voc12aug.py │ │ ├── gcnet_r101-d8_512x512_40k_voc12aug.py │ │ ├── gcnet_r101-d8_512x512_80k_ade20k.py │ │ ├── gcnet_r101-d8_769x769_40k_cityscapes.py │ │ ├── gcnet_r101-d8_769x769_80k_cityscapes.py │ │ ├── gcnet_r50-d8_512x1024_40k_cityscapes.py │ │ ├── gcnet_r50-d8_512x1024_80k_cityscapes.py │ │ ├── gcnet_r50-d8_512x512_160k_ade20k.py │ │ ├── gcnet_r50-d8_512x512_20k_voc12aug.py │ │ ├── gcnet_r50-d8_512x512_40k_voc12aug.py │ │ ├── gcnet_r50-d8_512x512_80k_ade20k.py │ │ ├── gcnet_r50-d8_769x769_40k_cityscapes.py │ │ └── gcnet_r50-d8_769x769_80k_cityscapes.py │ ├── hrnet │ │ ├── README.md │ │ ├── fcn_hr18_480x480_40k_pascal_context.py │ │ ├── fcn_hr18_480x480_80k_pascal_context.py │ │ ├── fcn_hr18_512x1024_160k_cityscapes.py │ │ ├── fcn_hr18_512x1024_40k_cityscapes.py │ │ ├── fcn_hr18_512x1024_80k_cityscapes.py │ │ ├── fcn_hr18_512x512_160k_ade20k.py │ │ ├── fcn_hr18_512x512_20k_voc12aug.py │ │ ├── fcn_hr18_512x512_40k_voc12aug.py │ │ ├── fcn_hr18_512x512_80k_ade20k.py │ │ ├── fcn_hr18s_480x480_40k_pascal_context.py │ │ ├── fcn_hr18s_480x480_80k_pascal_context.py │ │ ├── fcn_hr18s_512x1024_160k_cityscapes.py │ │ ├── fcn_hr18s_512x1024_40k_cityscapes.py │ │ ├── fcn_hr18s_512x1024_80k_cityscapes.py │ │ ├── fcn_hr18s_512x512_160k_ade20k.py │ │ ├── fcn_hr18s_512x512_20k_voc12aug.py │ │ ├── fcn_hr18s_512x512_40k_voc12aug.py │ │ ├── fcn_hr18s_512x512_80k_ade20k.py │ │ ├── fcn_hr48_480x480_40k_pascal_context.py │ │ ├── fcn_hr48_480x480_80k_pascal_context.py │ │ ├── fcn_hr48_512x1024_160k_cityscapes.py │ │ ├── fcn_hr48_512x1024_40k_cityscapes.py │ │ ├── fcn_hr48_512x1024_80k_cityscapes.py │ │ ├── fcn_hr48_512x512_160k_ade20k.py │ │ ├── fcn_hr48_512x512_20k_voc12aug.py │ │ ├── fcn_hr48_512x512_40k_voc12aug.py │ │ └── fcn_hr48_512x512_80k_ade20k.py │ ├── mobilenet_v2 │ │ ├── README.md │ │ ├── deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py │ │ ├── deeplabv3_m-v2-d8_512x512_160k_ade20k.py │ │ ├── deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py │ │ ├── deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py │ │ ├── fcn_m-v2-d8_512x1024_80k_cityscapes.py │ │ ├── fcn_m-v2-d8_512x512_160k_ade20k.py │ │ ├── pspnet_m-v2-d8_512x1024_80k_cityscapes.py │ │ └── pspnet_m-v2-d8_512x512_160k_ade20k.py │ ├── mobilenet_v3 │ │ ├── README.md │ │ ├── lraspp_m-v3-d8_512x1024_320k_cityscapes.py │ │ ├── lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes.py │ │ ├── lraspp_m-v3s-d8_512x1024_320k_cityscapes.py │ │ └── lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes.py │ ├── nonlocal_net │ │ ├── README.md │ │ ├── nonlocal_r101-d8_512x1024_40k_cityscapes.py │ │ ├── nonlocal_r101-d8_512x1024_80k_cityscapes.py │ │ ├── nonlocal_r101-d8_512x512_160k_ade20k.py │ │ ├── nonlocal_r101-d8_512x512_20k_voc12aug.py │ │ ├── nonlocal_r101-d8_512x512_40k_voc12aug.py │ │ ├── nonlocal_r101-d8_512x512_80k_ade20k.py │ │ ├── nonlocal_r101-d8_769x769_40k_cityscapes.py │ │ ├── nonlocal_r101-d8_769x769_80k_cityscapes.py │ │ ├── nonlocal_r50-d8_512x1024_40k_cityscapes.py │ │ ├── nonlocal_r50-d8_512x1024_80k_cityscapes.py │ │ ├── nonlocal_r50-d8_512x512_160k_ade20k.py │ │ ├── nonlocal_r50-d8_512x512_20k_voc12aug.py │ │ ├── nonlocal_r50-d8_512x512_40k_voc12aug.py │ │ ├── nonlocal_r50-d8_512x512_80k_ade20k.py │ │ ├── nonlocal_r50-d8_769x769_40k_cityscapes.py │ │ └── nonlocal_r50-d8_769x769_80k_cityscapes.py │ ├── ocrnet │ │ ├── README.md │ │ ├── ocrnet_hr18_512x1024_160k_cityscapes.py │ │ ├── ocrnet_hr18_512x1024_40k_cityscapes.py │ │ ├── ocrnet_hr18_512x1024_80k_cityscapes.py │ │ ├── ocrnet_hr18_512x512_160k_ade20k.py │ │ ├── ocrnet_hr18_512x512_20k_voc12aug.py │ │ ├── ocrnet_hr18_512x512_40k_voc12aug.py │ │ ├── ocrnet_hr18_512x512_80k_ade20k.py │ │ ├── ocrnet_hr18s_512x1024_160k_cityscapes.py │ │ ├── ocrnet_hr18s_512x1024_40k_cityscapes.py │ │ ├── ocrnet_hr18s_512x1024_80k_cityscapes.py │ │ ├── ocrnet_hr18s_512x512_160k_ade20k.py │ │ ├── ocrnet_hr18s_512x512_20k_voc12aug.py │ │ ├── ocrnet_hr18s_512x512_40k_voc12aug.py │ │ ├── ocrnet_hr18s_512x512_80k_ade20k.py │ │ ├── ocrnet_hr48_512x1024_160k_cityscapes.py │ │ ├── ocrnet_hr48_512x1024_40k_cityscapes.py │ │ ├── ocrnet_hr48_512x1024_80k_cityscapes.py │ │ ├── ocrnet_hr48_512x512_160k_ade20k.py │ │ ├── ocrnet_hr48_512x512_20k_voc12aug.py │ │ ├── ocrnet_hr48_512x512_40k_voc12aug.py │ │ ├── ocrnet_hr48_512x512_80k_ade20k.py │ │ ├── ocrnet_r101-d8_512x1024_40k_b16_cityscapes.py │ │ ├── ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py │ │ └── ocrnet_r101-d8_512x1024_80k_b16_cityscapes.py │ ├── point_rend │ │ ├── README.md │ │ ├── pointrend_r101_512x1024_80k_cityscapes.py │ │ ├── pointrend_r101_512x512_160k_ade20k.py │ │ ├── pointrend_r50_512x1024_80k_cityscapes.py │ │ └── pointrend_r50_512x512_160k_ade20k.py │ ├── psanet │ │ ├── README.md │ │ ├── psanet_r101-d8_512x1024_40k_cityscapes.py │ │ ├── psanet_r101-d8_512x1024_80k_cityscapes.py │ │ ├── psanet_r101-d8_512x512_160k_ade20k.py │ │ ├── psanet_r101-d8_512x512_20k_voc12aug.py │ │ ├── psanet_r101-d8_512x512_40k_voc12aug.py │ │ ├── psanet_r101-d8_512x512_80k_ade20k.py │ │ ├── psanet_r101-d8_769x769_40k_cityscapes.py │ │ ├── psanet_r101-d8_769x769_80k_cityscapes.py │ │ ├── psanet_r50-d8_512x1024_40k_cityscapes.py │ │ ├── psanet_r50-d8_512x1024_80k_cityscapes.py │ │ ├── psanet_r50-d8_512x512_160k_ade20k.py │ │ ├── psanet_r50-d8_512x512_20k_voc12aug.py │ │ ├── psanet_r50-d8_512x512_40k_voc12aug.py │ │ ├── psanet_r50-d8_512x512_80k_ade20k.py │ │ ├── psanet_r50-d8_769x769_40k_cityscapes.py │ │ └── psanet_r50-d8_769x769_80k_cityscapes.py │ ├── pspnet │ │ ├── README.md │ │ ├── pspnet_r101-d8_480x480_40k_pascal_context.py │ │ ├── pspnet_r101-d8_480x480_80k_pascal_context.py │ │ ├── pspnet_r101-d8_512x1024_40k_cityscapes.py │ │ ├── pspnet_r101-d8_512x1024_80k_cityscapes.py │ │ ├── pspnet_r101-d8_512x512_160k_ade20k.py │ │ ├── pspnet_r101-d8_512x512_20k_voc12aug.py │ │ ├── pspnet_r101-d8_512x512_40k_voc12aug.py │ │ ├── pspnet_r101-d8_512x512_80k_ade20k.py │ │ ├── pspnet_r101-d8_769x769_40k_cityscapes.py │ │ ├── pspnet_r101-d8_769x769_80k_cityscapes.py │ │ ├── pspnet_r101b-d8_512x1024_80k_cityscapes.py │ │ ├── pspnet_r101b-d8_769x769_80k_cityscapes.py │ │ ├── pspnet_r18-d8_512x1024_80k_cityscapes.py │ │ ├── pspnet_r18-d8_769x769_80k_cityscapes.py │ │ ├── pspnet_r18b-d8_512x1024_80k_cityscapes.py │ │ ├── pspnet_r18b-d8_769x769_80k_cityscapes.py │ │ ├── pspnet_r50-d8_480x480_40k_pascal_context.py │ │ ├── pspnet_r50-d8_480x480_80k_pascal_context.py │ │ ├── pspnet_r50-d8_512x1024_40k_cityscapes.py │ │ ├── pspnet_r50-d8_512x1024_80k_cityscapes.py │ │ ├── pspnet_r50-d8_512x512_160k_ade20k.py │ │ ├── pspnet_r50-d8_512x512_20k_voc12aug.py │ │ ├── pspnet_r50-d8_512x512_40k_voc12aug.py │ │ ├── pspnet_r50-d8_512x512_80k_ade20k.py │ │ ├── pspnet_r50-d8_769x769_40k_cityscapes.py │ │ ├── pspnet_r50-d8_769x769_80k_cityscapes.py │ │ ├── pspnet_r50b-d8_512x1024_80k_cityscapes.py │ │ └── pspnet_r50b-d8_769x769_80k_cityscapes.py │ ├── resnest │ │ ├── README.md │ │ ├── deeplabv3_s101-d8_512x1024_80k_cityscapes.py │ │ ├── deeplabv3_s101-d8_512x512_160k_ade20k.py │ │ ├── deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py │ │ ├── deeplabv3plus_s101-d8_512x512_160k_ade20k.py │ │ ├── fcn_s101-d8_512x1024_80k_cityscapes.py │ │ ├── fcn_s101-d8_512x512_160k_ade20k.py │ │ ├── pspnet_s101-d8_512x1024_80k_cityscapes.py │ │ └── pspnet_s101-d8_512x512_160k_ade20k.py │ ├── sem_fpn │ │ ├── README.md │ │ ├── fpn_r101_512x1024_80k_cityscapes.py │ │ ├── fpn_r101_512x512_160k_ade20k.py │ │ ├── fpn_r50_512x1024_80k_cityscapes.py │ │ └── fpn_r50_512x512_160k_ade20k.py │ ├── swin │ │ ├── upernet_swin_base_patch4_window7_512x512_160k_ade20k.py │ │ ├── upernet_swin_small_patch4_window7_512x512_160k_ade20k.py │ │ └── upernet_swin_tiny_patch4_window7_512x512_160k_ade20k.py │ ├── unet │ │ ├── README.md │ │ ├── deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py │ │ ├── deeplabv3_unet_s5-d16_128x128_40k_stare.py │ │ ├── deeplabv3_unet_s5-d16_256x256_40k_hrf.py │ │ ├── deeplabv3_unet_s5-d16_64x64_40k_drive.py │ │ ├── fcn_unet_s5-d16_128x128_40k_chase_db1.py │ │ ├── fcn_unet_s5-d16_128x128_40k_stare.py │ │ ├── fcn_unet_s5-d16_256x256_40k_hrf.py │ │ ├── fcn_unet_s5-d16_64x64_40k_drive.py │ │ ├── pspnet_unet_s5-d16_128x128_40k_chase_db1.py │ │ ├── pspnet_unet_s5-d16_128x128_40k_stare.py │ │ ├── pspnet_unet_s5-d16_256x256_40k_hrf.py │ │ └── pspnet_unet_s5-d16_64x64_40k_drive.py │ └── upernet │ │ ├── README.md │ │ ├── upernet_r101_512x1024_40k_cityscapes.py │ │ ├── upernet_r101_512x1024_80k_cityscapes.py │ │ ├── upernet_r101_512x512_160k_ade20k.py │ │ ├── upernet_r101_512x512_20k_voc12aug.py │ │ ├── upernet_r101_512x512_40k_voc12aug.py │ │ ├── upernet_r101_512x512_80k_ade20k.py │ │ ├── upernet_r101_769x769_40k_cityscapes.py │ │ ├── upernet_r101_769x769_80k_cityscapes.py │ │ ├── upernet_r50_512x1024_40k_cityscapes.py │ │ ├── upernet_r50_512x1024_80k_cityscapes.py │ │ ├── upernet_r50_512x512_160k_ade20k.py │ │ ├── upernet_r50_512x512_20k_voc12aug.py │ │ ├── upernet_r50_512x512_40k_voc12aug.py │ │ ├── upernet_r50_512x512_80k_ade20k.py │ │ ├── upernet_r50_769x769_40k_cityscapes.py │ │ └── upernet_r50_769x769_80k_cityscapes.py ├── demo │ ├── MMSegmentation_Tutorial.ipynb │ ├── demo.png │ ├── image_demo.py │ └── inference_demo.ipynb ├── docker │ └── Dockerfile ├── docs │ ├── Makefile │ ├── api.rst │ ├── changelog.md │ ├── conf.py │ ├── dataset_prepare.md │ ├── get_started.md │ ├── index.rst │ ├── inference.md │ ├── make.bat │ ├── model_zoo.md │ ├── stat.py │ ├── train.md │ ├── tutorials │ │ ├── config.md │ │ ├── customize_datasets.md │ │ ├── customize_models.md │ │ ├── customize_runtime.md │ │ ├── data_pipeline.md │ │ ├── index.rst │ │ └── training_tricks.md │ └── useful_tools.md ├── mmcv_custom │ ├── __init__.py │ └── checkpoint.py ├── mmseg │ ├── __init__.py │ ├── apis │ │ ├── __init__.py │ │ ├── inference.py │ │ ├── test.py │ │ └── train.py │ ├── core │ │ ├── __init__.py │ │ ├── evaluation │ │ │ ├── __init__.py │ │ │ ├── class_names.py │ │ │ ├── eval_hooks.py │ │ │ └── metrics.py │ │ ├── seg │ │ │ ├── __init__.py │ │ │ ├── builder.py │ │ │ └── sampler │ │ │ │ ├── __init__.py │ │ │ │ ├── base_pixel_sampler.py │ │ │ │ └── ohem_pixel_sampler.py │ │ └── utils │ │ │ ├── __init__.py │ │ │ └── misc.py │ ├── datasets │ │ ├── __init__.py │ │ ├── ade.py │ │ ├── builder.py │ │ ├── chase_db1.py │ │ ├── cityscapes.py │ │ ├── custom.py │ │ ├── dataset_wrappers.py │ │ ├── drive.py │ │ ├── hrf.py │ │ ├── pascal_context.py │ │ ├── pipelines │ │ │ ├── __init__.py │ │ │ ├── compose.py │ │ │ ├── formating.py │ │ │ ├── loading.py │ │ │ ├── test_time_aug.py │ │ │ └── transforms.py │ │ ├── stare.py │ │ └── voc.py │ ├── models │ │ ├── __init__.py │ │ ├── backbones │ │ │ ├── LowRankModules.py │ │ │ ├── __init__.py │ │ │ ├── cgnet.py │ │ │ ├── fast_scnn.py │ │ │ ├── hrnet.py │ │ │ ├── lora_layers.py │ │ │ ├── mobilenet_v2.py │ │ │ ├── mobilenet_v3.py │ │ │ ├── resnest.py │ │ │ ├── resnet.py │ │ │ ├── resnext.py │ │ │ ├── swin_transformer.py │ │ │ ├── swin_transformer_adapter.py │ │ │ ├── swin_transformer_adaptformer.py │ │ │ ├── swin_transformer_bitfit.py │ │ │ ├── swin_transformer_fixed.py │ │ │ ├── swin_transformer_lora.py │ │ │ ├── swin_transformer_mona.py │ │ │ ├── swin_transformer_norm_tuning.py │ │ │ ├── swin_transformer_partial_1.py │ │ │ └── unet.py │ │ ├── builder.py │ │ ├── decode_heads │ │ │ ├── __init__.py │ │ │ ├── ann_head.py │ │ │ ├── apc_head.py │ │ │ ├── aspp_head.py │ │ │ ├── cascade_decode_head.py │ │ │ ├── cc_head.py │ │ │ ├── da_head.py │ │ │ ├── decode_head.py │ │ │ ├── dm_head.py │ │ │ ├── dnl_head.py │ │ │ ├── ema_head.py │ │ │ ├── enc_head.py │ │ │ ├── fcn_head.py │ │ │ ├── fpn_head.py │ │ │ ├── gc_head.py │ │ │ ├── lraspp_head.py │ │ │ ├── nl_head.py │ │ │ ├── ocr_head.py │ │ │ ├── point_head.py │ │ │ ├── psa_head.py │ │ │ ├── psp_head.py │ │ │ ├── sep_aspp_head.py │ │ │ ├── sep_fcn_head.py │ │ │ └── uper_head.py │ │ ├── losses │ │ │ ├── __init__.py │ │ │ ├── accuracy.py │ │ │ ├── cross_entropy_loss.py │ │ │ ├── lovasz_loss.py │ │ │ ├── nfocal_loss.py │ │ │ └── utils.py │ │ ├── necks │ │ │ ├── __init__.py │ │ │ └── fpn.py │ │ ├── segmentors │ │ │ ├── __init__.py │ │ │ ├── base.py │ │ │ ├── cascade_encoder_decoder.py │ │ │ └── encoder_decoder.py │ │ └── utils │ │ │ ├── __init__.py │ │ │ ├── inverted_residual.py │ │ │ ├── make_divisible.py │ │ │ ├── res_layer.py │ │ │ ├── se_layer.py │ │ │ ├── self_attention_block.py │ │ │ └── up_conv_block.py │ ├── ops │ │ ├── __init__.py │ │ ├── encoding.py │ │ └── wrappers.py │ ├── utils │ │ ├── __init__.py │ │ ├── collect_env.py │ │ └── logger.py │ └── version.py ├── mona_configs │ ├── _base_ │ │ ├── datasets │ │ │ ├── ade20k.py │ │ │ ├── chase_db1.py │ │ │ ├── cityscapes.py │ │ │ ├── cityscapes_512x512.py │ │ │ ├── cityscapes_769x769.py │ │ │ ├── drive.py │ │ │ ├── hrf.py │ │ │ ├── pascal_context.py │ │ │ ├── pascal_voc12.py │ │ │ ├── pascal_voc12_aug.py │ │ │ └── stare.py │ │ ├── default_runtime.py │ │ ├── models │ │ │ ├── ann_r50-d8.py │ │ │ ├── apcnet_r50-d8.py │ │ │ ├── ccnet_r50-d8.py │ │ │ ├── cgnet.py │ │ │ ├── danet_r50-d8.py │ │ │ ├── deeplabv3_r50-d8.py │ │ │ ├── deeplabv3_unet_s5-d16.py │ │ │ ├── deeplabv3plus_r50-d8.py │ │ │ ├── dmnet_r50-d8.py │ │ │ ├── dnl_r50-d8.py │ │ │ ├── emanet_r50-d8.py │ │ │ ├── encnet_r50-d8.py │ │ │ ├── fast_scnn.py │ │ │ ├── fcn_hr18.py │ │ │ ├── fcn_r50-d8.py │ │ │ ├── fcn_unet_s5-d16.py │ │ │ ├── fpn_r50.py │ │ │ ├── gcnet_r50-d8.py │ │ │ ├── lraspp_m-v3-d8.py │ │ │ ├── nonlocal_r50-d8.py │ │ │ ├── ocrnet_hr18.py │ │ │ ├── ocrnet_r50-d8.py │ │ │ ├── pointrend_r50.py │ │ │ ├── psanet_r50-d8.py │ │ │ ├── pspnet_r50-d8.py │ │ │ ├── pspnet_unet_s5-d16.py │ │ │ ├── upernet_r50.py │ │ │ └── upernet_swin.py │ │ └── schedules │ │ │ ├── schedule_1600k.py │ │ │ ├── schedule_160k.py │ │ │ ├── schedule_20k.py │ │ │ ├── schedule_3200k.py │ │ │ ├── schedule_40k.py │ │ │ ├── schedule_80k.py │ │ │ └── schedule_80k_single_gpu.py │ └── swin-l_ade20k │ │ ├── ade20k_upernet_swin_large_160k_LN.py │ │ ├── ade20k_upernet_swin_large_160k_adapter.py │ │ ├── ade20k_upernet_swin_large_160k_adaptformer.py │ │ ├── ade20k_upernet_swin_large_160k_bitfit.py │ │ ├── ade20k_upernet_swin_large_160k_fixed.py │ │ ├── ade20k_upernet_swin_large_160k_full.py │ │ ├── ade20k_upernet_swin_large_160k_lora.py │ │ ├── ade20k_upernet_swin_large_160k_mona.py │ │ └── ade20k_upernet_swin_large_160k_partial_1.py ├── pytest.ini ├── requirements.txt ├── requirements │ ├── docs.txt │ ├── optional.txt │ ├── readthedocs.txt │ ├── runtime.txt │ └── tests.txt ├── resources │ ├── mmseg-logo.png │ └── seg_demo.gif ├── setup.cfg ├── setup.py ├── tests │ ├── test_config.py │ ├── test_data │ │ ├── test_dataset.py │ │ ├── test_dataset_builder.py │ │ ├── test_loading.py │ │ ├── test_transform.py │ │ └── test_tta.py │ ├── test_eval_hook.py │ ├── test_inference.py │ ├── test_metrics.py │ ├── test_models │ │ ├── test_backbone.py │ │ ├── test_forward.py │ │ ├── test_heads.py │ │ ├── test_losses.py │ │ ├── test_necks.py │ │ ├── test_segmentor.py │ │ └── test_unet.py │ ├── test_sampler.py │ └── test_utils │ │ ├── test_inverted_residual_module.py │ │ ├── test_make_divisible.py │ │ └── test_se_layer.py └── tools │ ├── benchmark.py │ ├── convert_datasets │ ├── chase_db1.py │ ├── cityscapes.py │ ├── drive.py │ ├── hrf.py │ ├── pascal_context.py │ ├── stare.py │ └── voc_aug.py │ ├── dist_test.sh │ ├── dist_train.sh │ ├── get_flops.py │ ├── print_config.py │ ├── publish_model.py │ ├── pytorch2onnx.py │ ├── slurm_test.sh │ ├── slurm_train.sh │ ├── test.py │ └── train.py └── resources ├── convergency.png ├── convergency2.png ├── meta.png ├── mona.png ├── mona2.png ├── mona3.png ├── mona_layer.png ├── performance.png ├── performance2.png └── variants.png /Swin-Transformer-Classification/.gitattributes: -------------------------------------------------------------------------------- 1 | docs/** linguist-documentation 2 | docs_zh-CN/** linguist-documentation 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/.readthedocs.yml: -------------------------------------------------------------------------------- 1 | version: 2 2 | 3 | formats: all 4 | 5 | python: 6 | version: 3.7 7 | install: 8 | - requirements: requirements/docs.txt 9 | - requirements: requirements/readthedocs.txt 10 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/MANIFEST.in: -------------------------------------------------------------------------------- 1 | include requirements/*.txt 2 | include mmcls/.mim/model-index.yml 3 | recursive-include mmcls/.mim/configs *.py *.yml 4 | recursive-include mmcls/.mim/tools *.py *.sh 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/_base_/models/van/van_base.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./van-b2.py'] 2 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/_base_/models/van/van_large.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./van-b3.py'] 2 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/_base_/models/van/van_small.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./van-b1.py'] 2 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/_base_/models/van/van_tiny.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./van-b0.py'] 2 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/deit/deit-small-distilled_pt-4xb256_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = './deit-small_pt-4xb256_in1k.py' 2 | 3 | # model settings 4 | model = dict( 5 | backbone=dict(type='DistilledVisionTransformer', arch='deit-small'), 6 | head=dict(type='DeiTClsHead', in_channels=384), 7 | ) 8 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/deit/deit-tiny-distilled_pt-4xb256_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = './deit-small_pt-4xb256_in1k.py' 2 | 3 | # model settings 4 | model = dict( 5 | backbone=dict(type='DistilledVisionTransformer', arch='deit-tiny'), 6 | head=dict(type='DeiTClsHead', in_channels=192), 7 | ) 8 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/deit/deit-tiny_pt-4xb256_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = './deit-small_pt-4xb256_in1k.py' 2 | 3 | # model settings 4 | model = dict( 5 | backbone=dict(type='VisionTransformer', arch='deit-tiny'), 6 | head=dict(type='VisionTransformerClsHead', in_channels=192), 7 | ) 8 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/fp16/resnet50_b32x8_fp16_dynamic_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = '../resnet/resnet50_8xb32-fp16-dynamic_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='../resnet/resnet50_8xb32-fp16-dynamic_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/fp16/resnet50_b32x8_fp16_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = '../resnet/resnet50_8xb32-fp16_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='../resnet/resnet50_8xb32-fp16_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/hrnet/hrnet-w18_4xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/hrnet/hrnet-w18.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256_coslr.py', 5 | '../_base_/default_runtime.py' 6 | ] 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/hrnet/hrnet-w30_4xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/hrnet/hrnet-w30.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256_coslr.py', 5 | '../_base_/default_runtime.py' 6 | ] 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/hrnet/hrnet-w32_4xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/hrnet/hrnet-w32.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256_coslr.py', 5 | '../_base_/default_runtime.py' 6 | ] 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/hrnet/hrnet-w40_4xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/hrnet/hrnet-w40.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256_coslr.py', 5 | '../_base_/default_runtime.py' 6 | ] 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/hrnet/hrnet-w44_4xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/hrnet/hrnet-w44.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256_coslr.py', 5 | '../_base_/default_runtime.py' 6 | ] 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/hrnet/hrnet-w48_4xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/hrnet/hrnet-w48.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256_coslr.py', 5 | '../_base_/default_runtime.py' 6 | ] 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/hrnet/hrnet-w64_4xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/hrnet/hrnet-w64.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256_coslr.py', 5 | '../_base_/default_runtime.py' 6 | ] 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/mlp_mixer/mlp-mixer-base-p16_64xb64_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/mlp_mixer_base_patch16.py', 3 | '../_base_/datasets/imagenet_bs64_mixer_224.py', 4 | '../_base_/schedules/imagenet_bs4096_AdamW.py', 5 | '../_base_/default_runtime.py', 6 | ] 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/mlp_mixer/mlp-mixer-large-p16_64xb64_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/mlp_mixer_large_patch16.py', 3 | '../_base_/datasets/imagenet_bs64_mixer_224.py', 4 | '../_base_/schedules/imagenet_bs4096_AdamW.py', 5 | '../_base_/default_runtime.py', 6 | ] 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/mobilenet_v2_1x.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256_epochstep.py', 5 | '../_base_/default_runtime.py' 6 | ] 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/mobilenet_v2/mobilenet_v2_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'mobilenet-v2_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='mobilenet-v2_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/mobilenet_v3/mobilenet_v3_large_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'mobilenet-v3-large_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='mobilenet-v3-large_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/mobilenet_v3/mobilenet_v3_small_cifar.py: -------------------------------------------------------------------------------- 1 | _base_ = 'mobilenet-v3-small_8xb16_cifar10.py' 2 | 3 | _deprecation_ = dict( 4 | expected='mobilenet-v3-small_8xb16_cifar10.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/mobilenet_v3/mobilenet_v3_small_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'mobilenet-v3-small_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='mobilenet-v3-small_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/regnet/regnetx-1.6gf_8xb128_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./regnetx-400mf_8xb128_in1k.py'] 2 | 3 | # model settings 4 | model = dict( 5 | backbone=dict(type='RegNet', arch='regnetx_1.6gf'), 6 | head=dict(in_channels=912, )) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/regnet/regnetx-800mf_8xb128_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./regnetx-400mf_8xb128_in1k.py'] 2 | 3 | # model settings 4 | model = dict( 5 | backbone=dict(type='RegNet', arch='regnetx_800mf'), 6 | head=dict(in_channels=672, )) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repmlp/repmlp-base_delopy_8xb64_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./repmlp-base_8xb64_in1k.py'] 2 | 3 | model = dict(backbone=dict(deploy=True)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repmlp/repmlp-base_deploy_8xb64_in1k-256px.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./repmlp-base_8xb64_in1k-256px.py'] 2 | 3 | model = dict(backbone=dict(deploy=True)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/deploy/repvgg-A0_deploy_4xb64-coslr-120e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = '../repvgg-A0_4xb64-coslr-120e_in1k.py' 2 | 3 | model = dict(backbone=dict(deploy=True)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/deploy/repvgg-A1_deploy_4xb64-coslr-120e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = '../repvgg-A1_4xb64-coslr-120e_in1k.py' 2 | 3 | model = dict(backbone=dict(deploy=True)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/deploy/repvgg-A2_deploy_4xb64-coslr-120e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = '../repvgg-A2_4xb64-coslr-120e_in1k.py' 2 | 3 | model = dict(backbone=dict(deploy=True)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/deploy/repvgg-B0_deploy_4xb64-coslr-120e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = '../repvgg-B0_4xb64-coslr-120e_in1k.py' 2 | 3 | model = dict(backbone=dict(deploy=True)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/deploy/repvgg-B1_deploy_4xb64-coslr-120e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = '../repvgg-B1_4xb64-coslr-120e_in1k.py' 2 | 3 | model = dict(backbone=dict(deploy=True)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/deploy/repvgg-B1g2_deploy_4xb64-coslr-120e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = '../repvgg-B1g2_4xb64-coslr-120e_in1k.py' 2 | 3 | model = dict(backbone=dict(deploy=True)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/deploy/repvgg-B1g4_deploy_4xb64-coslr-120e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = '../repvgg-B1g4_4xb64-coslr-120e_in1k.py' 2 | 3 | model = dict(backbone=dict(deploy=True)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/deploy/repvgg-B2_deploy_4xb64-coslr-120e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = '../repvgg-B2_4xb64-coslr-120e_in1k.py' 2 | 3 | model = dict(backbone=dict(deploy=True)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/deploy/repvgg-B2g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = '../repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py' 2 | 3 | model = dict(backbone=dict(deploy=True)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/deploy/repvgg-B3_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = '../repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py' 2 | 3 | model = dict(backbone=dict(deploy=True)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/deploy/repvgg-B3g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = '../repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py' 2 | 3 | model = dict(backbone=dict(deploy=True)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/deploy/repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = '../repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py' 2 | 3 | model = dict(backbone=dict(deploy=True)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/repvgg-A1_4xb64-coslr-120e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py' 2 | 3 | model = dict(backbone=dict(arch='A1')) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/repvgg-A2_4xb64-coslr-120e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py' 2 | 3 | model = dict(backbone=dict(arch='A2'), head=dict(in_channels=1408)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/repvgg-B0_4xb64-coslr-120e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py' 2 | 3 | model = dict(backbone=dict(arch='B0'), head=dict(in_channels=1280)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/repvgg-B1_4xb64-coslr-120e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py' 2 | 3 | model = dict(backbone=dict(arch='B1'), head=dict(in_channels=2048)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/repvgg-B1g2_4xb64-coslr-120e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py' 2 | 3 | model = dict(backbone=dict(arch='B1g2'), head=dict(in_channels=2048)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/repvgg-B1g4_4xb64-coslr-120e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py' 2 | 3 | model = dict(backbone=dict(arch='B1g4'), head=dict(in_channels=2048)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/repvgg-B2_4xb64-coslr-120e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py' 2 | 3 | model = dict(backbone=dict(arch='B2'), head=dict(in_channels=2560)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = './repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py' 2 | 3 | model = dict(backbone=dict(arch='B2g4')) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = './repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py' 2 | 3 | model = dict(backbone=dict(arch='B3g4')) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/repvgg/repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = './repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py' 2 | 3 | model = dict(backbone=dict(arch='D2se')) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/res2net/res2net101-w26-s4_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/res2net101-w26-s4.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/res2net/res2net50-w14-s8_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/res2net50-w14-s8.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/res2net/res2net50-w26-s8_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/res2net50-w26-s8.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnest/resnest101_b64x32_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnest101_32xb64_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnest101_32xb64_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnest/resnest200_b32x64_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnest200_64xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnest200_64xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnest/resnest269_b32x64_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnest269_64xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnest269_64xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnest/resnest50_b64x32_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnest50_32xb64_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnest50_32xb64_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet101_8xb16_cifar10.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnet101_cifar.py', 3 | '../_base_/datasets/cifar10_bs16.py', 4 | '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet101_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnet101.py', '../_base_/datasets/imagenet_bs32.py', 3 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet101_b16x8_cifar10.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnet101_8xb16_cifar10.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnet101_8xb16_cifar10.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet101_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnet101_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnet101_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet152_8xb16_cifar10.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnet152_cifar.py', 3 | '../_base_/datasets/cifar10_bs16.py', 4 | '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet152_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnet152.py', '../_base_/datasets/imagenet_bs32.py', 3 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet152_b16x8_cifar10.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnet152_8xb16_cifar10.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnet152_8xb16_cifar10.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet152_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnet152_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnet152_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet18_8xb16_cifar10.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnet18_cifar.py', '../_base_/datasets/cifar10_bs16.py', 3 | '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet18_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnet18.py', '../_base_/datasets/imagenet_bs32.py', 3 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet18_b16x8_cifar10.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnet18_8xb16_cifar10.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnet18_8xb16_cifar10.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet18_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnet18_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnet18_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet34_8xb16_cifar10.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnet34_cifar.py', '../_base_/datasets/cifar10_bs16.py', 3 | '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet34_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnet34.py', '../_base_/datasets/imagenet_bs32.py', 3 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet34_b16x8_cifar10.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnet34_8xb16_cifar10.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnet34_8xb16_cifar10.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet34_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnet34_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnet34_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_32xb64-warmup-coslr_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs64.py', 3 | '../_base_/schedules/imagenet_bs2048_coslr.py', 4 | '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_32xb64-warmup_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs64.py', 3 | '../_base_/schedules/imagenet_bs2048.py', '../_base_/default_runtime.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_8xb16-mixup_cifar10.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnet50_cifar_mixup.py', 3 | '../_base_/datasets/cifar10_bs16.py', 4 | '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_8xb16_cifar10.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnet50_cifar.py', '../_base_/datasets/cifar10_bs16.py', 3 | '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_8xb32-coslr_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs32.py', 3 | '../_base_/schedules/imagenet_bs256_coslr.py', 4 | '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_8xb32-cutmix_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnet50_cutmix.py', 3 | '../_base_/datasets/imagenet_bs32.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_8xb32-fp16-dynamic_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./resnet50_8xb32_in1k.py'] 2 | 3 | # fp16 settings 4 | fp16 = dict(loss_scale='dynamic') 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_8xb32-fp16_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./resnet50_8xb32_in1k.py'] 2 | 3 | # fp16 settings 4 | fp16 = dict(loss_scale=512.) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_8xb32-lbs_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnet50_label_smooth.py', 3 | '../_base_/datasets/imagenet_bs32.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_8xb32-mixup_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnet50_mixup.py', 3 | '../_base_/datasets/imagenet_bs32.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs32.py', 3 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_b16x8_cifar10.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnet50_8xb16_cifar10.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnet50_8xb16_cifar10.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_b16x8_cifar100.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnet50_8xb16_cifar100.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnet50_8xb16_cifar100.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_b16x8_cifar10_mixup.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnet50_8xb16-mixup_cifar10.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnet50_8xb16-mixup_cifar10.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_b32x8_coslr_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnet50_8xb32-coslr_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnet50_8xb32-coslr_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_b32x8_cutmix_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnet50_8xb32-cutmix_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnet50_8xb32-cutmix_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnet50_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnet50_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_b32x8_label_smooth_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnet50_8xb32-lbs_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnet50_8xb32-lbs_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_b32x8_mixup_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnet50_8xb32-mixup_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnet50_8xb32-mixup_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_b64x32_warmup_coslr_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnet50_32xb64-warmup-coslr_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnet50_32xb64-warmup-coslr_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_b64x32_warmup_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnet50_32xb64-warmup_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnet50_32xb64-warmup_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnet50_b64x32_warmup_label_smooth_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnet50_32xb64-warmup-lbs_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnet50_32xb64-warmup-lbs_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnetv1c101_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnetv1c50.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | 7 | model = dict(backbone=dict(depth=101)) 8 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnetv1c152_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnetv1c50.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | 7 | model = dict(backbone=dict(depth=152)) 8 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnetv1c50_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnetv1c50.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnetv1d101_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnetv1d101.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnetv1d101_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnetv1d101_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnetv1d101_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnetv1d152_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnetv1d152.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnetv1d152_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnetv1d152_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnetv1d152_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnetv1d50_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnetv1d50.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnet/resnetv1d50_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnetv1d50_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnetv1d50_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnext/resnext101-32x4d_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnext101_32x4d.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnext/resnext101-32x8d_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnext101_32x8d.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnext/resnext101_32x4d_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnext101-32x4d_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnext101-32x4d_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnext/resnext101_32x8d_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnext101-32x8d_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnext101-32x8d_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnext/resnext152-32x4d_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnext152_32x4d.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnext/resnext152_32x4d_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnext152-32x4d_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnext152-32x4d_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnext/resnext50-32x4d_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/resnext50_32x4d.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/resnext/resnext50_32x4d_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'resnext50-32x4d_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='resnext50-32x4d_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/seresnet/seresnet101_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/seresnet101.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/seresnet/seresnet101_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'seresnet101_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='seresnet101_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/seresnet/seresnet50_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/seresnet50.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256_140e.py', 5 | '../_base_/default_runtime.py' 6 | ] 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/seresnet/seresnet50_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'seresnet50_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='seresnet50_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/seresnet/seresnext101-32x4d_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/seresnext101_32x4d.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/seresnet/seresnext101_32x4d_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'seresnext101-32x4d_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='seresnext101-32x4d_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/seresnet/seresnext50-32x4d_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/seresnext50_32x4d.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/seresnet/seresnext50_32x4d_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'seresnext50-32x4d_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='seresnext50-32x4d_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/shufflenet_v1_1x.py', 3 | '../_base_/datasets/imagenet_bs64_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs1024_linearlr_bn_nowd.py', 5 | '../_base_/default_runtime.py' 6 | ] 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/shufflenet_v1/shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'shufflenet-v1-1x_16xb64_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='shufflenet-v1-1x_16xb64_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/shufflenet_v2_1x.py', 3 | '../_base_/datasets/imagenet_bs64_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs1024_linearlr_bn_nowd.py', 5 | '../_base_/default_runtime.py' 6 | ] 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/shufflenet_v2/shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'shufflenet-v2-1x_16xb64_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='shufflenet-v2-1x_16xb64_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/swin_transformer/swin-base_16xb64_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/swin_transformer/base_224.py', 3 | '../_base_/datasets/imagenet_bs64_swin_224.py', 4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py', 5 | '../_base_/default_runtime.py' 6 | ] 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/swin_transformer/swin-small_16xb64_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/swin_transformer/small_224.py', 3 | '../_base_/datasets/imagenet_bs64_swin_224.py', 4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py', 5 | '../_base_/default_runtime.py' 6 | ] 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/swin_transformer/swin-tiny_16xb64_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/swin_transformer/tiny_224.py', 3 | '../_base_/datasets/imagenet_bs64_swin_224.py', 4 | '../_base_/schedules/imagenet_bs1024_adamw_swin.py', 5 | '../_base_/default_runtime.py' 6 | ] 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'swin-base_16xb64_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='swin-base_16xb64_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/swin_transformer/swin_base_384_evalonly_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'swin-base_16xb64_in1k-384px.py' 2 | 3 | _deprecation_ = dict( 4 | expected='swin-base_16xb64_in1k-384px.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/swin_transformer/swin_large_224_evalonly_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'swin-large_16xb64_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='swin-large_16xb64_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/swin_transformer/swin_large_384_evalonly_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'swin-large_16xb64_in1k-384px.py' 2 | 3 | _deprecation_ = dict( 4 | expected='swin-large_16xb64_in1k-384px.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/swin_transformer/swin_small_224_b16x64_300e_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'swin-small_16xb64_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='swin-small_16xb64_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/swin_transformer/swin_tiny_224_b16x64_300e_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'swin-tiny_16xb64_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='swin-tiny_16xb64_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/tnt/tnt_s_patch16_224_evalonly_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'tnt-s-p16_16xb64_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='tnt-s-p16_16xb64_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/twins/twins-pcpvt-large_16xb64_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = ['twins-pcpvt-base_8xb128_in1k.py'] 2 | 3 | model = dict(backbone=dict(arch='large'), head=dict(in_channels=512)) 4 | 5 | data = dict(samples_per_gpu=64) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/twins/twins-pcpvt-small_8xb128_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = ['twins-pcpvt-base_8xb128_in1k.py'] 2 | 3 | model = dict(backbone=dict(arch='small'), head=dict(in_channels=512)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/twins/twins-svt-large_16xb64_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = ['twins-svt-base_8xb128_in1k.py'] 2 | 3 | data = dict(samples_per_gpu=64) 4 | 5 | model = dict(backbone=dict(arch='large'), head=dict(in_channels=1024)) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/twins/twins-svt-small_8xb128_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = ['twins-svt-base_8xb128_in1k.py'] 2 | 3 | model = dict(backbone=dict(arch='small'), head=dict(in_channels=512)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/van/van-base_8xb128_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./van-b2_8xb128_in1k.py'] 2 | 3 | _deprecation_ = dict( 4 | expected='van-b2_8xb128_in1k.p', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/1017', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/van/van-large_8xb128_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./van-b3_8xb128_in1k.py'] 2 | 3 | _deprecation_ = dict( 4 | expected='van-b3_8xb128_in1k.p', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/1017', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/van/van-small_8xb128_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./van-b1_8xb128_in1k.py'] 2 | 3 | _deprecation_ = dict( 4 | expected='van-b1_8xb128_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/1017', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/van/van-tiny_8xb128_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./van-b0_8xb128_in1k.py'] 2 | 3 | _deprecation_ = dict( 4 | expected='van-b0_8xb128_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/1017', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/vgg/vgg11_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/vgg11.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', 5 | '../_base_/default_runtime.py', 6 | ] 7 | optimizer = dict(lr=0.01) 8 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/vgg/vgg11_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'vgg11_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='vgg11_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/vgg/vgg11bn_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/vgg11bn.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/vgg/vgg11bn_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'vgg11bn_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='vgg11bn_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/vgg/vgg13_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/vgg13.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | optimizer = dict(lr=0.01) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/vgg/vgg13_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'vgg13_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='vgg13_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/vgg/vgg13bn_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/vgg13bn.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/vgg/vgg13bn_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'vgg13bn_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='vgg13bn_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/vgg/vgg16_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/vgg16.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | optimizer = dict(lr=0.01) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/vgg/vgg16_b16x8_voc.py: -------------------------------------------------------------------------------- 1 | _base_ = 'vgg16_8xb16_voc.py' 2 | 3 | _deprecation_ = dict( 4 | expected='vgg16_8xb16_voc.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/vgg/vgg16_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'vgg16_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='vgg16_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/vgg/vgg16bn_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/vgg16bn.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/vgg/vgg16bn_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'vgg16bn_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='vgg16bn_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/vgg/vgg19_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/vgg19.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | optimizer = dict(lr=0.01) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/vgg/vgg19_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'vgg19_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='vgg19_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/vgg/vgg19bn_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/vgg19bn.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/vgg/vgg19bn_b32x8_imagenet.py: -------------------------------------------------------------------------------- 1 | _base_ = 'vgg19bn_8xb32_in1k.py' 2 | 3 | _deprecation_ = dict( 4 | expected='vgg19bn_8xb32_in1k.py', 5 | reference='https://github.com/open-mmlab/mmclassification/pull/508', 6 | ) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/wrn/wide-resnet101_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/wide-resnet50.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | 7 | model = dict(backbone=dict(depth=101)) 8 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/wrn/wide-resnet50_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/wide-resnet50.py', 3 | '../_base_/datasets/imagenet_bs32_pil_resize.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/configs/wrn/wide-resnet50_timm_8xb32_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/wide-resnet50.py', 3 | '../_base_/datasets/imagenet_bs32_pil_bicubic.py', 4 | '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/demo/bird.JPEG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Leiyi-Hu/mona/e9aaed8784de0fd716df731316f7e1d327b901a9/Swin-Transformer-Classification/demo/bird.JPEG -------------------------------------------------------------------------------- /Swin-Transformer-Classification/demo/cat-dog.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | inference_address=http://0.0.0.0:8080 2 | management_address=http://0.0.0.0:8081 3 | metrics_address=http://0.0.0.0:8082 4 | model_store=/home/model-server/model-store 5 | load_models=all 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/docker/serve/entrypoint.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | set -e 3 | 4 | if [[ "$1" = "serve" ]]; then 5 | shift 1 6 | torchserve --start --ts-config /home/model-server/config.properties 7 | else 8 | eval "$@" 9 | fi 10 | 11 | # prevent docker exit 12 | tail -f /dev/null 13 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/docs/en/_static/image/mmcls-logo.png: -------------------------------------------------------------------------------- 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/Swin-Transformer-Classification/docs/en/docutils.conf: -------------------------------------------------------------------------------- 1 | [html writers] 2 | table_style: colwidths-auto 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/docs/zh_CN/_static/image/mmcls-logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Leiyi-Hu/mona/e9aaed8784de0fd716df731316f7e1d327b901a9/Swin-Transformer-Classification/docs/zh_CN/_static/image/mmcls-logo.png -------------------------------------------------------------------------------- /Swin-Transformer-Classification/docs/zh_CN/_static/image/tools/analysis/analyze_log.jpg: -------------------------------------------------------------------------------- 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/Swin-Transformer-Classification/docs/zh_CN/docutils.conf: -------------------------------------------------------------------------------- 1 | [html writers] 2 | table_style: colwidths-auto 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/docs/zh_CN/imgs/qq_group_qrcode.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Leiyi-Hu/mona/e9aaed8784de0fd716df731316f7e1d327b901a9/Swin-Transformer-Classification/docs/zh_CN/imgs/qq_group_qrcode.jpg -------------------------------------------------------------------------------- /Swin-Transformer-Classification/docs/zh_CN/imgs/zhihu_qrcode.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Leiyi-Hu/mona/e9aaed8784de0fd716df731316f7e1d327b901a9/Swin-Transformer-Classification/docs/zh_CN/imgs/zhihu_qrcode.jpg -------------------------------------------------------------------------------- /Swin-Transformer-Classification/mmcls/core/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .evaluation import * # noqa: F401, F403 3 | from .hook import * # noqa: F401, F403 4 | from .optimizers import * # noqa: F401, F403 5 | from .utils import * # noqa: F401, F403 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/mmcls/core/export/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .test import ONNXRuntimeClassifier, TensorRTClassifier 3 | 4 | __all__ = ['ONNXRuntimeClassifier', 'TensorRTClassifier'] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/mmcls/core/optimizers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .lamb import Lamb 3 | 4 | __all__ = [ 5 | 'Lamb', 6 | ] 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/mmcls/datasets/samplers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .distributed_sampler import DistributedSampler 3 | from .repeat_aug import RepeatAugSampler 4 | 5 | __all__ = ('DistributedSampler', 'RepeatAugSampler') 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/mmcls/models/classifiers/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .base import BaseClassifier 3 | from .image import ImageClassifier 4 | 5 | __all__ = ['BaseClassifier', 'ImageClassifier'] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/mmcls/models/necks/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from .gap import GlobalAveragePooling 3 | from .gem import GeneralizedMeanPooling 4 | from .hr_fuse import HRFuseScales 5 | 6 | __all__ = ['GlobalAveragePooling', 'GeneralizedMeanPooling', 'HRFuseScales'] 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/mmcls/models/utils/augment/builder.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | from mmcv.utils import Registry, build_from_cfg 3 | 4 | AUGMENT = Registry('augment') 5 | 6 | 7 | def build_augment(cfg, default_args=None): 8 | return build_from_cfg(cfg, AUGMENT, default_args) 9 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/mona_configs/_base_/models/van/van_base.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./van-b2.py'] 2 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/mona_configs/_base_/models/van/van_large.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./van-b3.py'] 2 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/mona_configs/_base_/models/van/van_small.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./van-b1.py'] 2 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/mona_configs/_base_/models/van/van_tiny.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./van-b0.py'] 2 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/requirements.txt: -------------------------------------------------------------------------------- 1 | -r requirements/optional.txt 2 | -r requirements/runtime.txt 3 | -r requirements/tests.txt 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/requirements/docs.txt: -------------------------------------------------------------------------------- 1 | docutils==0.17.1 2 | myst-parser 3 | git+https://github.com/open-mmlab/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme 4 | sphinx==4.5.0 5 | sphinx-copybutton 6 | sphinx_markdown_tables 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/requirements/mminstall.txt: -------------------------------------------------------------------------------- 1 | mmcv-full>=1.4.2,<1.9.0 2 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/requirements/optional.txt: -------------------------------------------------------------------------------- 1 | albumentations>=0.3.2 --no-binary qudida,albumentations 2 | colorama 3 | requests 4 | rich 5 | scipy 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/requirements/readthedocs.txt: -------------------------------------------------------------------------------- 1 | mmcv>=1.4.2 2 | torch 3 | torchvision 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/requirements/runtime.txt: -------------------------------------------------------------------------------- 1 | matplotlib>=3.1.0 2 | numpy 3 | packaging 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/requirements/tests.txt: -------------------------------------------------------------------------------- 1 | codecov 2 | flake8 3 | interrogate 4 | isort==4.3.21 5 | mmdet 6 | pytest 7 | xdoctest >= 0.10.0 8 | yapf 9 | -------------------------------------------------------------------------------- /Swin-Transformer-Classification/resources/mmcls-logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Leiyi-Hu/mona/e9aaed8784de0fd716df731316f7e1d327b901a9/Swin-Transformer-Classification/resources/mmcls-logo.png -------------------------------------------------------------------------------- /Swin-Transformer-Classification/tests/test_models/test_backbones/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) OpenMMLab. All rights reserved. 2 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/.dev_scripts/linter.sh: -------------------------------------------------------------------------------- 1 | yapf -r -i mmdet/ configs/ tests/ tools/ 2 | isort -rc mmdet/ configs/ tests/ tools/ 3 | flake8 . 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/.github/CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | We appreciate all contributions to improve MMDetection. Please refer to [CONTRIBUTING.md](https://github.com/open-mmlab/mmcv/blob/master/CONTRIBUTING.md) in MMCV for more details about the contributing guideline. 2 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/.github/ISSUE_TEMPLATE/general_questions.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: General questions 3 | about: Ask general questions to get help 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/.readthedocs.yml: -------------------------------------------------------------------------------- 1 | version: 2 2 | 3 | python: 4 | version: 3.7 5 | install: 6 | - requirements: requirements/docs.txt 7 | - requirements: requirements/readthedocs.txt 8 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/atss/atss_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './atss_r50_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(depth=101), 5 | ) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://detectron2/resnet101_caffe', 4 | backbone=dict(depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/cascade_mask_rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_instance.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/cascade_mask_rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_instance.py', 4 | '../_base_/schedules/schedule_20e.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_rcnn_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://detectron2/resnet101_caffe', 4 | backbone=dict(depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_rcnn_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_rcnn_r50_fpn_20e_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/cascade_rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_detection.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_rcnn_r50_fpn_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 19]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=20) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fast_rcnn_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://detectron2/resnet101_caffe', 4 | backbone=dict(depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fast_rcnn_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fast_rcnn_r50_fpn_2x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/fast_rcnn/fast_rcnn_r50_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fast_rcnn_r50_fpn_1x_coco.py' 2 | 3 | # learning policy 4 | lr_config = dict(step=[16, 22]) 5 | runner = dict(type='EpochBasedRunner', max_epochs=24) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://detectron2/resnet101_caffe', 4 | backbone=dict(depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_2x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[28, 34]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=36) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 23]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[28, 34]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=36) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/faster_rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_detection.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/faster_rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_detection.py', 4 | '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | roi_head=dict( 4 | bbox_head=dict( 5 | reg_decoded_bbox=True, 6 | loss_bbox=dict(type='BoundedIoULoss', loss_weight=10.0)))) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | roi_head=dict( 4 | bbox_head=dict( 5 | reg_decoded_bbox=True, 6 | loss_bbox=dict(type='GIoULoss', loss_weight=10.0)))) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | roi_head=dict( 4 | bbox_head=dict( 5 | reg_decoded_bbox=True, 6 | loss_bbox=dict(type='IoULoss', loss_weight=10.0)))) 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict(train_cfg=dict(rcnn=dict(sampler=dict(type='OHEMSampler')))) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' 2 | model = dict(bbox_head=dict(center_sampling=True, center_sample_radius=1.5)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://detectron/resnet101_caffe', 4 | backbone=dict(depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py: -------------------------------------------------------------------------------- 1 | # TODO: Remove this config after benchmarking all related configs 2 | _base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py' 3 | 4 | data = dict(samples_per_gpu=4, workers_per_gpu=4) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fovea_r50_fpn_4x4_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fovea_r50_fpn_4x4_2x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fovea_r50_fpn_4x4_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/fp16/faster_rcnn_r50_fpn_fp16_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' 2 | # fp16 settings 3 | fp16 = dict(loss_scale=512.) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' 2 | # fp16 settings 3 | fp16 = dict(loss_scale=512.) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' 2 | # fp16 settings 3 | fp16 = dict(loss_scale=512.) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'retinanet_r50_fpg_crop640_50e_coco.py' 2 | 3 | model = dict( 4 | neck=dict(out_channels=128, inter_channels=128), 5 | bbox_head=dict(in_channels=128)) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/fsaf/fsaf_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fsaf_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/gn+ws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://jhu/resnet101_gn_ws', backbone=dict(depth=101)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[20, 23]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://jhu/resnet101_gn_ws', backbone=dict(depth=101)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[20, 23]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[20, 23]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[20, 23]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_gn-all_2x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://detectron/resnet101_gn', backbone=dict(depth=101)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r101_fpn_gn-all_2x_coco.py' 2 | 3 | # learning policy 4 | lr_config = dict(step=[28, 34]) 5 | runner = dict(type='EpochBasedRunner', max_epochs=36) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_gn-all_2x_coco.py' 2 | 3 | # learning policy 4 | lr_config = dict(step=[28, 34]) 5 | runner = dict(type='EpochBasedRunner', max_epochs=36) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py' 2 | 3 | # learning policy 4 | lr_config = dict(step=[28, 34]) 5 | runner = dict(type='EpochBasedRunner', max_epochs=36) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './grid_rcnn_r50_fpn_gn-head_2x_coco.py' 2 | 3 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga_faster_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://detectron2/resnet101_caffe', 4 | backbone=dict(depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga_retinanet_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://detectron2/resnet101_caffe', 4 | backbone=dict(depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga_rpn_r50_caffe_fpn_1x_coco.py' 2 | # model settings 3 | model = dict( 4 | pretrained='open-mmlab://detectron2/resnet101_caffe', 5 | backbone=dict(depth=101)) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_hrnetv2p_w18_1x_coco.py' 2 | 3 | # learning policy 4 | lr_config = dict(step=[16, 22]) 5 | runner = dict(type='EpochBasedRunner', max_epochs=24) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_hrnetv2p_w40_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/hrnet/htc_hrnetv2p_w40_28e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './htc_hrnetv2p_w40_20e_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[24, 27]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=28) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/hrnet/htc_x101_64x4d_fpn_16x1_28e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../htc/htc_x101_64x4d_fpn_16x1_20e_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[24, 27]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=28) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_hrnetv2p_w40_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/htc/htc_r101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './htc_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | # learning policy 4 | lr_config = dict(step=[16, 19]) 5 | runner = dict(type='EpochBasedRunner', max_epochs=20) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/htc/htc_r50_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './htc_r50_fpn_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 19]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=20) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/instaboost/cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py' 2 | 3 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_instaboost_4x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './libra_faster_rcnn_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://detectron2/resnet101_caffe', 4 | backbone=dict(depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_fpn_2x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 23]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[28, 34]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=36) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/mask_rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_instance.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/mask_rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_instance.py', 4 | '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://detectron2/resnet101_caffe', 4 | backbone=dict(depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ms_rcnn_r101_caffe_fpn_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ms_rcnn_x101_64x4d_fpn_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/paa/paa_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './paa_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/paa/paa_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './paa_r101_fpn_1x_coco.py' 2 | lr_config = dict(step=[16, 22]) 3 | runner = dict(type='EpochBasedRunner', max_epochs=24) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/paa/paa_r101_fpn_mstrain_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './paa_r50_fpn_mstrain_3x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/paa/paa_r50_fpn_1.5x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './paa_r50_fpn_1x_coco.py' 2 | lr_config = dict(step=[12, 16]) 3 | runner = dict(type='EpochBasedRunner', max_epochs=18) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/paa/paa_r50_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './paa_r50_fpn_1x_coco.py' 2 | lr_config = dict(step=[16, 22]) 3 | runner = dict(type='EpochBasedRunner', max_epochs=24) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' 2 | 3 | model = dict( 4 | neck=dict( 5 | type='PAFPN', 6 | in_channels=[256, 512, 1024, 2048], 7 | out_channels=256, 8 | num_outs=5)) 9 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './point_rend_r50_caffe_fpn_mstrain_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[28, 34]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=36) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py' 2 | lr_config = dict(step=[16, 22]) 3 | runner = dict(type='EpochBasedRunner', max_epochs=24) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py' 2 | model = dict(bbox_head=dict(transform_method='minmax', use_grid_points=True)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/reppoints/reppoints.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Leiyi-Hu/mona/e9aaed8784de0fd716df731316f7e1d327b901a9/Swin-Transformer-Object-Detection/configs/reppoints/reppoints.png -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/reppoints/reppoints_minmax_r50_fpn_gn-neck+head_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py' 2 | model = dict(bbox_head=dict(transform_method='minmax')) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py' 2 | lr_config = dict(step=[16, 22]) 3 | runner = dict(type='EpochBasedRunner', max_epochs=24) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/reppoints/reppoints_partial_minmax_r50_fpn_gn-neck+head_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py' 2 | model = dict(bbox_head=dict(transform_method='partial_minmax')) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://res2net101_v1d_26w_4s', 4 | backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://res2net101_v1d_26w_4s', 4 | backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://res2net101_v1d_26w_4s', 4 | backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://res2net101_v1d_26w_4s', 4 | backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnest101', 4 | backbone=dict(stem_channels=128, depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnest101', 4 | backbone=dict(stem_channels=128, depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnest101', 4 | backbone=dict(stem_channels=128, depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://resnest101', 4 | backbone=dict(stem_channels=128, depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://detectron2/resnet101_caffe', 4 | backbone=dict(depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/retinanet/retinanet_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/retinanet/retinanet_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_2x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 23]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[28, 34]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=36) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/retinanet/retinanet_r50_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_caffe_fpn_1x_coco.py' 2 | model = dict( 3 | pretrained='open-mmlab://detectron2/resnet101_caffe', 4 | backbone=dict(depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/rpn/rpn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/rpn/rpn_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_2x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/rpn/rpn_r50_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_1x_coco.py' 2 | 3 | # learning policy 4 | lr_config = dict(step=[16, 22]) 5 | runner = dict(type='EpochBasedRunner', max_epochs=24) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/scnet/scnet_r101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './scnet_r50_fpn_20e_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/scnet/scnet_r50_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './scnet_r50_fpn_1x_coco.py' 2 | # learning policy 3 | lr_config = dict(step=[16, 19]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=20) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/scnet/scnet_x101_64x4d_fpn_8x1_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './scnet_x101_64x4d_fpn_20e_coco.py' 2 | data = dict(samples_per_gpu=1, workers_per_gpu=1) 3 | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py' 2 | 3 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py' 2 | 3 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'tridentnet_r50_caffe_mstrain_1x_coco.py' 2 | 3 | lr_config = dict(step=[28, 34]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=36) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/vfnet/vfnet_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/vfnet/vfnet_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_1x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | lr_config = dict(step=[16, 22]) 4 | runner = dict(type='EpochBasedRunner', max_epochs=24) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_mstrain_2x_coco.py' 2 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/configs/yolact/yolact_r101_1x8_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './yolact_r50_1x8_coco.py' 2 | 3 | model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/demo/demo.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Leiyi-Hu/mona/e9aaed8784de0fd716df731316f7e1d327b901a9/Swin-Transformer-Object-Detection/demo/demo.jpg -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/demo/demo.mp4: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Leiyi-Hu/mona/e9aaed8784de0fd716df731316f7e1d327b901a9/Swin-Transformer-Object-Detection/demo/demo.mp4 -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/docker/serve/config.properties: -------------------------------------------------------------------------------- 1 | inference_address=http://0.0.0.0:8080 2 | management_address=http://0.0.0.0:8081 3 | metrics_address=http://0.0.0.0:8082 4 | model_store=/home/model-server/model-store 5 | load_models=all 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/docker/serve/entrypoint.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | set -e 3 | 4 | if [[ "$1" = "serve" ]]; then 5 | shift 1 6 | torchserve --start --ts-config /home/model-server/config.properties 7 | else 8 | eval "$@" 9 | fi 10 | 11 | # prevent docker exit 12 | tail -f /dev/null 13 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/log/归档.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Leiyi-Hu/mona/e9aaed8784de0fd716df731316f7e1d327b901a9/Swin-Transformer-Object-Detection/log/归档.zip -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/mmcv_custom/__init__.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | from .checkpoint import load_checkpoint 4 | 5 | __all__ = ['load_checkpoint'] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/mmcv_custom/runner/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Open-MMLab. All rights reserved. 2 | from .checkpoint import save_checkpoint 3 | from .epoch_based_runner import EpochBasedRunnerAmp 4 | 5 | 6 | __all__ = [ 7 | 'EpochBasedRunnerAmp', 'save_checkpoint' 8 | ] 9 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/mmdet/core/anchor/builder.py: -------------------------------------------------------------------------------- 1 | from mmcv.utils import Registry, build_from_cfg 2 | 3 | ANCHOR_GENERATORS = Registry('Anchor generator') 4 | 5 | 6 | def build_anchor_generator(cfg, default_args=None): 7 | return build_from_cfg(cfg, ANCHOR_GENERATORS, default_args) 8 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/mmdet/core/bbox/iou_calculators/__init__.py: -------------------------------------------------------------------------------- 1 | from .builder import build_iou_calculator 2 | from .iou2d_calculator import BboxOverlaps2D, bbox_overlaps 3 | 4 | __all__ = ['build_iou_calculator', 'BboxOverlaps2D', 'bbox_overlaps'] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/mmdet/core/visualization/__init__.py: -------------------------------------------------------------------------------- 1 | from .image import (color_val_matplotlib, imshow_det_bboxes, 2 | imshow_gt_det_bboxes) 3 | 4 | __all__ = ['imshow_det_bboxes', 'imshow_gt_det_bboxes', 'color_val_matplotlib'] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/mmdet/datasets/samplers/__init__.py: -------------------------------------------------------------------------------- 1 | from .distributed_sampler import DistributedSampler 2 | from .group_sampler import DistributedGroupSampler, GroupSampler 3 | 4 | __all__ = ['DistributedSampler', 'DistributedGroupSampler', 'GroupSampler'] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/mmdet/models/roi_heads/roi_extractors/__init__.py: -------------------------------------------------------------------------------- 1 | from .generic_roi_extractor import GenericRoIExtractor 2 | from .single_level_roi_extractor import SingleRoIExtractor 3 | 4 | __all__ = [ 5 | 'SingleRoIExtractor', 6 | 'GenericRoIExtractor', 7 | ] 8 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/mmdet/models/roi_heads/shared_heads/__init__.py: -------------------------------------------------------------------------------- 1 | from .res_layer import ResLayer 2 | 3 | __all__ = ['ResLayer'] 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/mmdet/utils/__init__.py: -------------------------------------------------------------------------------- 1 | from .collect_env import collect_env 2 | from .logger import get_root_logger 3 | from .optimizer import DistOptimizerHook 4 | 5 | __all__ = ['get_root_logger', 'collect_env', 'DistOptimizerHook'] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/mona_configs/test.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Leiyi-Hu/mona/e9aaed8784de0fd716df731316f7e1d327b901a9/Swin-Transformer-Object-Detection/mona_configs/test.py -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/requirements.txt: -------------------------------------------------------------------------------- 1 | -r requirements/build.txt 2 | -r requirements/optional.txt 3 | -r requirements/runtime.txt 4 | -r requirements/tests.txt 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/requirements/build.txt: -------------------------------------------------------------------------------- 1 | # These must be installed before building mmdetection 2 | cython 3 | numpy 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/requirements/docs.txt: -------------------------------------------------------------------------------- 1 | recommonmark 2 | sphinx 3 | sphinx_markdown_tables 4 | sphinx_rtd_theme 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/requirements/optional.txt: -------------------------------------------------------------------------------- 1 | albumentations>=0.3.2 2 | cityscapesscripts 3 | imagecorruptions 4 | mmlvis 5 | scipy 6 | sklearn 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/requirements/readthedocs.txt: -------------------------------------------------------------------------------- 1 | mmcv 2 | torch 3 | torchvision 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/requirements/runtime.txt: -------------------------------------------------------------------------------- 1 | matplotlib 2 | mmpycocotools 3 | numpy 4 | six 5 | terminaltables 6 | timm 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/resources/coco_test_12510.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Leiyi-Hu/mona/e9aaed8784de0fd716df731316f7e1d327b901a9/Swin-Transformer-Object-Detection/resources/coco_test_12510.jpg -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/resources/corruptions_sev_3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Leiyi-Hu/mona/e9aaed8784de0fd716df731316f7e1d327b901a9/Swin-Transformer-Object-Detection/resources/corruptions_sev_3.png -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/resources/data_pipeline.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Leiyi-Hu/mona/e9aaed8784de0fd716df731316f7e1d327b901a9/Swin-Transformer-Object-Detection/resources/data_pipeline.png -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/resources/loss_curve.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Leiyi-Hu/mona/e9aaed8784de0fd716df731316f7e1d327b901a9/Swin-Transformer-Object-Detection/resources/loss_curve.png -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/resources/mmdet-logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Leiyi-Hu/mona/e9aaed8784de0fd716df731316f7e1d327b901a9/Swin-Transformer-Object-Detection/resources/mmdet-logo.png -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/tests/test_models/test_backbones/__init__.py: -------------------------------------------------------------------------------- 1 | from .utils import check_norm_state, is_block, is_norm 2 | 3 | __all__ = ['is_block', 'is_norm', 'check_norm_state'] 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/tests/test_models/test_roi_heads/__init__.py: -------------------------------------------------------------------------------- 1 | from .utils import _dummy_bbox_sampling 2 | 3 | __all__ = ['_dummy_bbox_sampling'] 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Object-Detection/tests/test_onnx/__init__.py: -------------------------------------------------------------------------------- 1 | from .utils import (WrapFunction, convert_result_list, ort_validate, 2 | verify_model) 3 | 4 | __all__ = [ 5 | 'WrapFunction', 'verify_model', 'convert_result_list', 'ort_validate' 6 | ] 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/.github/ISSUE_TEMPLATE/config.yml: -------------------------------------------------------------------------------- 1 | blank_issues_enabled: false 2 | 3 | contact_links: 4 | - name: MMSegmentation Documentation 5 | url: https://mmsegmentation.readthedocs.io 6 | about: Check the docs and FAQ to see if you question is already anwsered. 7 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/.github/ISSUE_TEMPLATE/general_questions.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: General questions 3 | about: Ask general questions to get help 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/.readthedocs.yml: -------------------------------------------------------------------------------- 1 | version: 2 2 | 3 | python: 4 | version: 3.7 5 | install: 6 | - requirements: requirements/docs.txt 7 | - requirements: requirements/readthedocs.txt 8 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './ann_r50-d8_512x1024_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './ann_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ann/ann_r101-d8_512x512_160k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './ann_r50-d8_512x512_160k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ann/ann_r101-d8_512x512_20k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './ann_r50-d8_512x512_20k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ann/ann_r101-d8_512x512_40k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './ann_r50-d8_512x512_40k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ann/ann_r101-d8_512x512_80k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './ann_r50-d8_512x512_80k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ann/ann_r101-d8_769x769_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './ann_r50-d8_769x769_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ann/ann_r101-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './ann_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/ann_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/ann_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './apcnet_r50-d8_512x1024_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './apcnet_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './apcnet_r50-d8_512x512_160k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './apcnet_r50-d8_512x512_80k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './apcnet_r50-d8_769x769_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './apcnet_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/apcnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/apcnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './ccnet_r50-d8_512x1024_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './ccnet_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './ccnet_r50-d8_512x512_160k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './ccnet_r50-d8_512x512_20k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './ccnet_r50-d8_512x512_40k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './ccnet_r50-d8_512x512_80k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './ccnet_r50-d8_769x769_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './ccnet_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/ccnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/ccnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './danet_r50-d8_512x1024_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './danet_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/danet/danet_r101-d8_512x512_160k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './danet_r50-d8_512x512_160k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/danet/danet_r101-d8_512x512_20k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './danet_r50-d8_512x512_20k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/danet/danet_r101-d8_512x512_40k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './danet_r50-d8_512x512_40k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/danet/danet_r101-d8_512x512_80k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './danet_r50-d8_512x512_80k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/danet/danet_r101-d8_769x769_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './danet_r50-d8_769x769_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/danet/danet_r101-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './danet_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/danet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/danet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_480x480_40k_pascal_context.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_480x480_80k_pascal_context.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_512x1024_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_512x512_160k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_512x512_20k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_512x512_40k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_512x512_80k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_769x769_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(type='ResNet', depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(type='ResNet', depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_480x480_40k_pascal_context.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_480x480_80k_pascal_context.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_512x512_160k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_512x512_20k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_512x512_40k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_512x512_80k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_769x769_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(type='ResNet', depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(type='ResNet', depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/deeplabv3plus_r50-d8.py', 3 | '../_base_/datasets/cityscapes.py', '../_base_/default_runtime.py', 4 | '../_base_/schedules/schedule_40k.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/deeplabv3plus_r50-d8.py', 3 | '../_base_/datasets/cityscapes.py', '../_base_/default_runtime.py', 4 | '../_base_/schedules/schedule_80k.py' 5 | ] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './deeplabv3plus_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './dmnet_r50-d8_512x1024_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './dmnet_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './dmnet_r50-d8_512x512_160k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './dmnet_r50-d8_512x512_80k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './dmnet_r50-d8_769x769_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './dmnet_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/dmnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/dmnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './dnl_r50-d8_512x1024_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './dnl_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/dnlnet/dnl_r101-d8_512x512_160k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './dnl_r50-d8_512x512_160k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/dnlnet/dnl_r101-d8_512x512_80k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './dnl_r50-d8_512x512_80k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/dnlnet/dnl_r101-d8_769x769_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './dnl_r50-d8_769x769_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './dnl_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/dnl_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/dnl_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './emanet_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './emanet_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/emanet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './encnet_r50-d8_512x1024_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/encnet/encnet_r101-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './encnet_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/encnet/encnet_r101-d8_512x512_160k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './encnet_r50-d8_512x512_160k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/encnet/encnet_r101-d8_512x512_20k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './encnet_r50-d8_512x512_20k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/encnet/encnet_r101-d8_512x512_40k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './encnet_r50-d8_512x512_40k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './encnet_r50-d8_512x512_80k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './encnet_r50-d8_769x769_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/encnet/encnet_r101-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './encnet_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/encnet/encnet_r50-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/encnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/encnet/encnet_r50-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/encnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_480x480_40k_pascal_context.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/fcn/fcn_r101-d8_480x480_80k_pascal_context.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_480x480_80k_pascal_context.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_512x1024_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/fcn/fcn_r101-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/fcn/fcn_r101-d8_512x512_160k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_512x512_160k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/fcn/fcn_r101-d8_512x512_20k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_512x512_20k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/fcn/fcn_r101-d8_512x512_40k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_512x512_40k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/fcn/fcn_r101-d8_512x512_80k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_512x512_80k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_769x769_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(type='ResNet', depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(type='ResNet', depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/fcn/fcn_r50-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcn_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = '../deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py' 2 | # fp16 settings 3 | optimizer_config = dict(type='Fp16OptimizerHook', loss_scale=512.) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py' 2 | # fp16 settings 3 | optimizer_config = dict(type='Fp16OptimizerHook', loss_scale=512.) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = '../fcn/fcn_r101-d8_512x1024_80k_cityscapes.py' 2 | # fp16 settings 3 | optimizer_config = dict(type='Fp16OptimizerHook', loss_scale=512.) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = '../pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py' 2 | # fp16 settings 3 | optimizer_config = dict(type='Fp16OptimizerHook', loss_scale=512.) 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './gcnet_r50-d8_512x1024_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './gcnet_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/gcnet/gcnet_r101-d8_512x512_160k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './gcnet_r50-d8_512x512_160k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './gcnet_r50-d8_512x512_20k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './gcnet_r50-d8_512x512_40k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/gcnet/gcnet_r101-d8_512x512_80k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './gcnet_r50-d8_512x512_80k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './gcnet_r50-d8_769x769_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './gcnet_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/gcnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/gcnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/hrnet/fcn_hr18_512x1024_160k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/hrnet/fcn_hr18_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/hrnet/fcn_hr18_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/hrnet/fcn_hr18_512x512_160k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/ade20k.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' 4 | ] 5 | model = dict(decode_head=dict(num_classes=150)) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/hrnet/fcn_hr18_512x512_20k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/pascal_voc12_aug.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' 4 | ] 5 | model = dict(decode_head=dict(num_classes=21)) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/hrnet/fcn_hr18_512x512_40k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/pascal_voc12_aug.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | model = dict(decode_head=dict(num_classes=21)) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/hrnet/fcn_hr18_512x512_80k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/ade20k.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | model = dict(decode_head=dict(num_classes=150)) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './nonlocal_r50-d8_512x1024_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './nonlocal_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './nonlocal_r50-d8_512x512_160k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './nonlocal_r50-d8_512x512_20k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './nonlocal_r50-d8_512x512_40k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './nonlocal_r50-d8_512x512_80k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './nonlocal_r50-d8_769x769_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './nonlocal_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/nonlocal_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/nonlocal_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/ocrnet_hr18.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/ocrnet_hr18.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/ocrnet_hr18.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './pointrend_r50_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/point_rend/pointrend_r101_512x512_160k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './pointrend_r50_512x512_160k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/psanet/psanet_r101-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './psanet_r50-d8_512x1024_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/psanet/psanet_r101-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './psanet_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './psanet_r50-d8_512x512_160k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './psanet_r50-d8_512x512_20k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './psanet_r50-d8_512x512_40k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/psanet/psanet_r101-d8_512x512_80k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './psanet_r50-d8_512x512_80k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/psanet/psanet_r101-d8_769x769_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './psanet_r50-d8_769x769_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/psanet/psanet_r101-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './psanet_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/psanet/psanet_r50-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/psanet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/psanet/psanet_r50-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/psanet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_480x480_40k_pascal_context.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_480x480_80k_pascal_context.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_512x1024_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_512x512_160k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_512x512_20k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_512x512_40k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_512x512_80k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_769x769_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(type='ResNet', depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict( 3 | pretrained='torchvision://resnet101', 4 | backbone=dict(type='ResNet', depth=101)) 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py' 2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/sem_fpn/fpn_r101_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './fpn_r50_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/sem_fpn/fpn_r101_512x512_160k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './fpn_r50_512x512_160k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/sem_fpn/fpn_r50_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fpn_r50.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/sem_fpn/fpn_r50_512x512_160k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/fpn_r50.py', '../_base_/datasets/ade20k.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' 4 | ] 5 | model = dict(decode_head=dict(num_classes=150)) 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/upernet/upernet_r101_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './upernet_r50_512x1024_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/upernet/upernet_r101_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './upernet_r50_512x1024_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/upernet/upernet_r101_512x512_160k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './upernet_r50_512x512_160k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/upernet/upernet_r101_512x512_20k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './upernet_r50_512x512_20k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/upernet/upernet_r101_512x512_40k_voc12aug.py: -------------------------------------------------------------------------------- 1 | _base_ = './upernet_r50_512x512_40k_voc12aug.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/upernet/upernet_r101_512x512_80k_ade20k.py: -------------------------------------------------------------------------------- 1 | _base_ = './upernet_r50_512x512_80k_ade20k.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/upernet/upernet_r101_769x769_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './upernet_r50_769x769_40k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/upernet/upernet_r101_769x769_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = './upernet_r50_769x769_80k_cityscapes.py' 2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) 3 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/upernet/upernet_r50_512x1024_40k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/upernet_r50.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/configs/upernet/upernet_r50_512x1024_80k_cityscapes.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/upernet_r50.py', '../_base_/datasets/cityscapes.py', 3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' 4 | ] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/demo/demo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Leiyi-Hu/mona/e9aaed8784de0fd716df731316f7e1d327b901a9/Swin-Transformer-Semantic-Segmentation/demo/demo.png -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/docs/tutorials/index.rst: -------------------------------------------------------------------------------- 1 | .. toctree:: 2 | :maxdepth: 2 3 | 4 | config.md 5 | customize_datasets.md 6 | data_pipeline.md 7 | customize_models.md 8 | training_tricks.md 9 | customize_runtime.md 10 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/mmcv_custom/__init__.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | from .checkpoint import load_checkpoint 4 | 5 | __all__ = ['load_checkpoint'] 6 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/mmseg/core/__init__.py: -------------------------------------------------------------------------------- 1 | from .evaluation import * # noqa: F401, F403 2 | from .seg import * # noqa: F401, F403 3 | from .utils import * # noqa: F401, F403 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/mmseg/core/seg/__init__.py: -------------------------------------------------------------------------------- 1 | from .builder import build_pixel_sampler 2 | from .sampler import BasePixelSampler, OHEMPixelSampler 3 | 4 | __all__ = ['build_pixel_sampler', 'BasePixelSampler', 'OHEMPixelSampler'] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/mmseg/core/seg/sampler/__init__.py: -------------------------------------------------------------------------------- 1 | from .base_pixel_sampler import BasePixelSampler 2 | from .ohem_pixel_sampler import OHEMPixelSampler 3 | 4 | __all__ = ['BasePixelSampler', 'OHEMPixelSampler'] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/mmseg/core/utils/__init__.py: -------------------------------------------------------------------------------- 1 | from .misc import add_prefix 2 | 3 | __all__ = ['add_prefix'] 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/mmseg/models/necks/__init__.py: -------------------------------------------------------------------------------- 1 | from .fpn import FPN 2 | 3 | __all__ = ['FPN'] 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/mmseg/models/segmentors/__init__.py: -------------------------------------------------------------------------------- 1 | from .cascade_encoder_decoder import CascadeEncoderDecoder 2 | from .encoder_decoder import EncoderDecoder 3 | 4 | __all__ = ['EncoderDecoder', 'CascadeEncoderDecoder'] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/mmseg/ops/__init__.py: -------------------------------------------------------------------------------- 1 | from .encoding import Encoding 2 | from .wrappers import Upsample, resize 3 | 4 | __all__ = ['Upsample', 'resize', 'Encoding'] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/mmseg/utils/__init__.py: -------------------------------------------------------------------------------- 1 | from .collect_env import collect_env 2 | from .logger import get_root_logger 3 | 4 | __all__ = ['get_root_logger', 'collect_env'] 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/requirements.txt: -------------------------------------------------------------------------------- 1 | -r requirements/optional.txt 2 | -r requirements/runtime.txt 3 | -r requirements/tests.txt 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/requirements/docs.txt: -------------------------------------------------------------------------------- 1 | recommonmark 2 | sphinx 3 | sphinx_markdown_tables 4 | sphinx_rtd_theme 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/requirements/optional.txt: -------------------------------------------------------------------------------- 1 | cityscapesscripts 2 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/requirements/readthedocs.txt: -------------------------------------------------------------------------------- 1 | mmcv 2 | torch 3 | torchvision 4 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/requirements/runtime.txt: -------------------------------------------------------------------------------- 1 | matplotlib 2 | numpy 3 | terminaltables 4 | timm 5 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/requirements/tests.txt: -------------------------------------------------------------------------------- 1 | codecov 2 | flake8 3 | interrogate 4 | isort==4.3.21 5 | pytest 6 | xdoctest>=0.10.0 7 | yapf 8 | -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/resources/mmseg-logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Leiyi-Hu/mona/e9aaed8784de0fd716df731316f7e1d327b901a9/Swin-Transformer-Semantic-Segmentation/resources/mmseg-logo.png -------------------------------------------------------------------------------- /Swin-Transformer-Semantic-Segmentation/resources/seg_demo.gif: -------------------------------------------------------------------------------- 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