├── .circleci
├── config.yml
├── docker
│ └── Dockerfile
└── test.yml
├── .dev_scripts
├── batch_test_list.py
├── batch_train_list.txt
├── benchmark_evaluation.sh
├── benchmark_full_models.txt
├── benchmark_inference.py
├── benchmark_options.py
├── benchmark_train.sh
├── benchmark_train_models.txt
├── check_urls.py
├── gather_benchmark_evaluation_results.py
├── gather_benchmark_train_results.py
├── gather_models.py
├── generate_benchmark_evaluation_script.py
├── generate_benchmark_train_script.py
├── log_collector
│ ├── example_config.py
│ ├── log_collector.py
│ ├── readme.md
│ └── utils.py
├── update_model_index.py
└── upload_modelzoo.py
├── .github
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── ISSUE_TEMPLATE
│ ├── config.yml
│ ├── error-report.md
│ ├── feature_request.md
│ ├── general_questions.md
│ └── reimplementation_questions.md
├── pull_request_template.md
└── workflows
│ └── deploy.yml
├── .gitignore
├── .owners.yml
├── .pre-commit-config.yaml
├── CITATION.cff
├── LICENSE
├── MANIFEST.in
├── README.md
├── configs
├── _base_
│ ├── datasets
│ │ ├── ade20k.py
│ │ ├── ade20k_640x640.py
│ │ ├── bdd100k.py
│ │ ├── chase_db1.py
│ │ ├── cityscapes.py
│ │ ├── cityscapes_1024x1024.py
│ │ ├── cityscapes_768x768.py
│ │ ├── cityscapes_769x769.py
│ │ ├── cityscapes_832x832.py
│ │ ├── coco-stuff10k.py
│ │ ├── coco-stuff164k.py
│ │ ├── drive.py
│ │ ├── hrf.py
│ │ ├── hsi_drive.py
│ │ ├── isaid.py
│ │ ├── levir_256x256.py
│ │ ├── loveda.py
│ │ ├── mapillary_v1.py
│ │ ├── mapillary_v1_65.py
│ │ ├── mapillary_v2.py
│ │ ├── nyu.py
│ │ ├── nyu_512x512.py
│ │ ├── pascal_context.py
│ │ ├── pascal_context_59.py
│ │ ├── pascal_voc12.py
│ │ ├── pascal_voc12_aug.py
│ │ ├── potsdam.py
│ │ ├── refuge.py
│ │ ├── stare.py
│ │ ├── synapse.py
│ │ └── vaihingen.py
│ ├── default_runtime.py
│ ├── models
│ │ ├── ann_r50-d8.py
│ │ ├── apcnet_r50-d8.py
│ │ ├── bisenetv1_r18-d32.py
│ │ ├── bisenetv2.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
│ │ ├── dpt_vit-b16.py
│ │ ├── emanet_r50-d8.py
│ │ ├── encnet_r50-d8.py
│ │ ├── erfnet_fcn.py
│ │ ├── fast_scnn.py
│ │ ├── fastfcn_r50-d32_jpu_psp.py
│ │ ├── fcn_hr18.py
│ │ ├── fcn_r50-d8.py
│ │ ├── fcn_unet_s5-d16.py
│ │ ├── fpn_poolformer_s12.py
│ │ ├── fpn_r50.py
│ │ ├── gcnet_r50-d8.py
│ │ ├── icnet_r50-d8.py
│ │ ├── isanet_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
│ │ ├── samba_upernet.py
│ │ ├── san_vit-b16.py
│ │ ├── segformer_mit-b0.py
│ │ ├── segmenter_vit-b16_mask.py
│ │ ├── setr_mla.py
│ │ ├── setr_naive.py
│ │ ├── setr_pup.py
│ │ ├── stdc.py
│ │ ├── twins_pcpvt-s_fpn.py
│ │ ├── twins_pcpvt-s_upernet.py
│ │ ├── upernet_beit.py
│ │ ├── upernet_convnext.py
│ │ ├── upernet_mae.py
│ │ ├── upernet_r50.py
│ │ ├── upernet_swin.py
│ │ ├── upernet_vit-b16_ln_mln.py
│ │ └── vpd_sd.py
│ └── schedules
│ │ ├── schedule_160k.py
│ │ ├── schedule_20k.py
│ │ ├── schedule_240k.py
│ │ ├── schedule_25k.py
│ │ ├── schedule_320k.py
│ │ ├── schedule_40k.py
│ │ └── schedule_80k.py
├── ann
│ ├── README.md
│ ├── ann_r101-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── ann_r101-d8_4xb2-40k_cityscapes-769x769.py
│ ├── ann_r101-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── ann_r101-d8_4xb2-80k_cityscapes-769x769.py
│ ├── ann_r101-d8_4xb4-160k_ade20k-512x512.py
│ ├── ann_r101-d8_4xb4-20k_voc12aug-512x512.py
│ ├── ann_r101-d8_4xb4-40k_voc12aug-512x512.py
│ ├── ann_r101-d8_4xb4-80k_ade20k-512x512.py
│ ├── ann_r50-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── ann_r50-d8_4xb2-40k_cityscapes-769x769.py
│ ├── ann_r50-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── ann_r50-d8_4xb2-80k_cityscapes-769x769.py
│ ├── ann_r50-d8_4xb4-160k_ade20k-512x512.py
│ ├── ann_r50-d8_4xb4-20k_voc12aug-512x512.py
│ ├── ann_r50-d8_4xb4-40k_voc12aug-512x512.py
│ ├── ann_r50-d8_4xb4-80k_ade20k-512x512.py
│ └── metafile.yaml
├── apcnet
│ ├── README.md
│ ├── apcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── apcnet_r101-d8_4xb2-40k_cityscapes-769x769.py
│ ├── apcnet_r101-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── apcnet_r101-d8_4xb2-80k_cityscapes-769x769.py
│ ├── apcnet_r101-d8_4xb4-160k_ade20k-512x512.py
│ ├── apcnet_r101-d8_4xb4-80k_ade20k-512x512.py
│ ├── apcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── apcnet_r50-d8_4xb2-40k_cityscapes-769x769.py
│ ├── apcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── apcnet_r50-d8_4xb2-80k_cityscapes-769x769.py
│ ├── apcnet_r50-d8_4xb4-160k_ade20k-512x512.py
│ ├── apcnet_r50-d8_4xb4-80k_ade20k-512x512.py
│ └── metafile.yaml
├── beit
│ ├── README.md
│ ├── beit-base_upernet_8xb2-160k_ade20k-640x640.py
│ ├── beit-base_upernet_8xb2-160k_ade20k-640x640_ms.py
│ ├── beit-large_upernet_8xb1-amp-160k_ade20k-640x640.py
│ ├── beit-large_upernet_8xb1-amp-160k_ade20k-640x640_ms.py
│ └── metafile.yaml
├── bisenetv1
│ ├── README.md
│ ├── bisenetv1_r101-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py
│ ├── bisenetv1_r101-d32_4xb4-160k_coco-stuff164k-512x512.py
│ ├── bisenetv1_r18-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py
│ ├── bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py
│ ├── bisenetv1_r18-d32-in1k-pre_4xb8-160k_cityscapes-1024x1024.py
│ ├── bisenetv1_r18-d32_4xb4-160k_cityscapes-1024x1024.py
│ ├── bisenetv1_r18-d32_4xb4-160k_coco-stuff164k-512x512.py
│ ├── bisenetv1_r50-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py
│ ├── bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py
│ ├── bisenetv1_r50-d32_4xb4-160k_cityscapes-1024x1024.py
│ ├── bisenetv1_r50-d32_4xb4-160k_coco-stuff164k-512x512.py
│ └── metafile.yaml
├── bisenetv2
│ ├── README.md
│ ├── bisenetv2_fcn_4xb4-160k_cityscapes-1024x1024.py
│ ├── bisenetv2_fcn_4xb4-amp-160k_cityscapes-1024x1024.py
│ ├── bisenetv2_fcn_4xb4-ohem-160k_cityscapes-1024x1024.py
│ ├── bisenetv2_fcn_4xb8-160k_cityscapes-1024x1024.py
│ └── metafile.yaml
├── ccnet
│ ├── README.md
│ ├── ccnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── ccnet_r101-d8_4xb2-40k_cityscapes-769x769.py
│ ├── ccnet_r101-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── ccnet_r101-d8_4xb2-80k_cityscapes-769x769.py
│ ├── ccnet_r101-d8_4xb4-160k_ade20k-512x512.py
│ ├── ccnet_r101-d8_4xb4-20k_voc12aug-512x512.py
│ ├── ccnet_r101-d8_4xb4-40k_voc12aug-512x512.py
│ ├── ccnet_r101-d8_4xb4-80k_ade20k-512x512.py
│ ├── ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── ccnet_r50-d8_4xb2-40k_cityscapes-769x769.py
│ ├── ccnet_r50-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── ccnet_r50-d8_4xb2-80k_cityscapes-769x769.py
│ ├── ccnet_r50-d8_4xb4-160k_ade20k-512x512.py
│ ├── ccnet_r50-d8_4xb4-20k_voc12aug-512x512.py
│ ├── ccnet_r50-d8_4xb4-40k_voc12aug-512x512.py
│ ├── ccnet_r50-d8_4xb4-80k_ade20k-512x512.py
│ └── metafile.yaml
├── cgnet
│ ├── README.md
│ ├── cgnet_fcn_4xb4-60k_cityscapes-680x680.py
│ ├── cgnet_fcn_4xb8-60k_cityscapes-512x1024.py
│ └── metafile.yaml
├── convnext
│ ├── README.md
│ ├── convnext-base_upernet_8xb2-amp-160k_ade20k-512x512.py
│ ├── convnext-base_upernet_8xb2-amp-160k_ade20k-640x640.py
│ ├── convnext-large_upernet_8xb2-amp-160k_ade20k-640x640.py
│ ├── convnext-small_upernet_8xb2-amp-160k_ade20k-512x512.py
│ ├── convnext-tiny_upernet_8xb2-amp-160k_ade20k-512x512.py
│ ├── convnext-xlarge_upernet_8xb2-amp-160k_ade20k-640x640.py
│ └── metafile.yaml
├── danet
│ ├── README.md
│ ├── danet_r101-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── danet_r101-d8_4xb2-40k_cityscapes-769x769.py
│ ├── danet_r101-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── danet_r101-d8_4xb2-80k_cityscapes-769x769.py
│ ├── danet_r101-d8_4xb4-160k_ade20k-512x512.py
│ ├── danet_r101-d8_4xb4-20k_voc12aug-512x512.py
│ ├── danet_r101-d8_4xb4-40k_voc12aug-512x512.py
│ ├── danet_r101-d8_4xb4-80k_ade20k-512x512.py
│ ├── danet_r50-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── danet_r50-d8_4xb2-40k_cityscapes-769x769.py
│ ├── danet_r50-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── danet_r50-d8_4xb2-80k_cityscapes-769x769.py
│ ├── danet_r50-d8_4xb4-160k_ade20k-512x512.py
│ ├── danet_r50-d8_4xb4-20k_voc12aug-512x512.py
│ ├── danet_r50-d8_4xb4-40k_voc12aug-512x512.py
│ ├── danet_r50-d8_4xb4-80k_ade20k-512x512.py
│ └── metafile.yaml
├── ddrnet
│ ├── README.md
│ ├── ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024.py
│ ├── ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024.py
│ └── metafile.yaml
├── deeplabv3
│ ├── README.md
│ ├── deeplabv3_r101-d16-mg124_4xb2-40k_cityscapes-512x1024.py
│ ├── deeplabv3_r101-d16-mg124_4xb2-80k_cityscapes-512x1024.py
│ ├── deeplabv3_r101-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── deeplabv3_r101-d8_4xb2-40k_cityscapes-769x769.py
│ ├── deeplabv3_r101-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── deeplabv3_r101-d8_4xb2-80k_cityscapes-769x769.py
│ ├── deeplabv3_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py
│ ├── deeplabv3_r101-d8_4xb4-160k_ade20k-512x512.py
│ ├── deeplabv3_r101-d8_4xb4-160k_coco-stuff164k-512x512.py
│ ├── deeplabv3_r101-d8_4xb4-20k_coco-stuff10k-512x512.py
│ ├── deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512.py
│ ├── deeplabv3_r101-d8_4xb4-320k_coco-stuff164k-512x512.py
│ ├── deeplabv3_r101-d8_4xb4-40k_coco-stuff10k-512x512.py
│ ├── deeplabv3_r101-d8_4xb4-40k_pascal-context-480x480.py
│ ├── deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480.py
│ ├── deeplabv3_r101-d8_4xb4-40k_voc12aug-512x512.py
│ ├── deeplabv3_r101-d8_4xb4-80k_ade20k-512x512.py
│ ├── deeplabv3_r101-d8_4xb4-80k_coco-stuff164k-512x512.py
│ ├── deeplabv3_r101-d8_4xb4-80k_pascal-context-480x480.py
│ ├── deeplabv3_r101-d8_4xb4-80k_pascal-context-59-480x480.py
│ ├── deeplabv3_r101b-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── deeplabv3_r101b-d8_4xb2-80k_cityscapes-769x769.py
│ ├── deeplabv3_r18-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── deeplabv3_r18-d8_4xb2-80k_cityscapes-769x769.py
│ ├── deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── deeplabv3_r18b-d8_4xb2-80k_cityscapes-769x769.py
│ ├── deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── deeplabv3_r50-d8_4xb2-40k_cityscapes-769x769.py
│ ├── deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py
│ ├── deeplabv3_r50-d8_4xb4-160k_ade20k-512x512.py
│ ├── deeplabv3_r50-d8_4xb4-160k_coco-stuff164k-512x512.py
│ ├── deeplabv3_r50-d8_4xb4-20k_coco-stuff10k-512x512.py
│ ├── deeplabv3_r50-d8_4xb4-20k_voc12aug-512x512.py
│ ├── deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py
│ ├── deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512.py
│ ├── deeplabv3_r50-d8_4xb4-40k_pascal-context-480x480.py
│ ├── deeplabv3_r50-d8_4xb4-40k_pascal-context-59-480x480.py
│ ├── deeplabv3_r50-d8_4xb4-40k_voc12aug-512x512.py
│ ├── deeplabv3_r50-d8_4xb4-80k_ade20k-512x512.py
│ ├── deeplabv3_r50-d8_4xb4-80k_coco-stuff164k-512x512.py
│ ├── deeplabv3_r50-d8_4xb4-80k_pascal-context-480x480.py
│ ├── deeplabv3_r50-d8_4xb4-80k_pascal-context-59-480x480.py
│ ├── deeplabv3_r50b-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── deeplabv3_r50b-d8_4xb2-80k_cityscapes-769x769.py
│ └── metafile.yaml
├── deeplabv3plus
│ ├── README.md
│ ├── deeplabv3plus_r101-d16-mg124_4xb2-40k_cityscapes-512x1024.py
│ ├── deeplabv3plus_r101-d16-mg124_4xb2-80k_cityscapes-512x1024.py
│ ├── deeplabv3plus_r101-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── deeplabv3plus_r101-d8_4xb2-40k_cityscapes-769x769.py
│ ├── deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── deeplabv3plus_r101-d8_4xb2-80k_cityscapes-769x769.py
│ ├── deeplabv3plus_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py
│ ├── deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py
│ ├── deeplabv3plus_r101-d8_4xb4-20k_voc12aug-512x512.py
│ ├── deeplabv3plus_r101-d8_4xb4-40k_pascal-context-480x480.py
│ ├── deeplabv3plus_r101-d8_4xb4-40k_pascal-context-59-480x480.py
│ ├── deeplabv3plus_r101-d8_4xb4-40k_voc12aug-512x512.py
│ ├── deeplabv3plus_r101-d8_4xb4-80k_ade20k-512x512.py
│ ├── deeplabv3plus_r101-d8_4xb4-80k_loveda-512x512.py
│ ├── deeplabv3plus_r101-d8_4xb4-80k_pascal-context-480x480.py
│ ├── deeplabv3plus_r101-d8_4xb4-80k_pascal-context-59-480x480.py
│ ├── deeplabv3plus_r101-d8_4xb4-80k_potsdam-512x512.py
│ ├── deeplabv3plus_r101-d8_4xb4-80k_vaihingen-512x512.py
│ ├── deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-769x769.py
│ ├── deeplabv3plus_r18-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── deeplabv3plus_r18-d8_4xb2-80k_cityscapes-769x769.py
│ ├── deeplabv3plus_r18-d8_4xb4-80k_isaid-896x896.py
│ ├── deeplabv3plus_r18-d8_4xb4-80k_loveda-512x512.py
│ ├── deeplabv3plus_r18-d8_4xb4-80k_potsdam-512x512.py
│ ├── deeplabv3plus_r18-d8_4xb4-80k_vaihingen-512x512.py
│ ├── deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-769x769.py
│ ├── deeplabv3plus_r50-d8_4xb2-300k_mapillay_v1_65-1280x1280.py
│ ├── deeplabv3plus_r50-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── deeplabv3plus_r50-d8_4xb2-40k_cityscapes-769x769.py
│ ├── deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py
│ ├── deeplabv3plus_r50-d8_4xb4-160k_ade20k-512x512.py
│ ├── deeplabv3plus_r50-d8_4xb4-20k_voc12aug-512x512.py
│ ├── deeplabv3plus_r50-d8_4xb4-40k_pascal-context-480x480.py
│ ├── deeplabv3plus_r50-d8_4xb4-40k_pascal-context-59-480x480.py
│ ├── deeplabv3plus_r50-d8_4xb4-40k_voc12aug-512x512.py
│ ├── deeplabv3plus_r50-d8_4xb4-80k_ade20k-512x512.py
│ ├── deeplabv3plus_r50-d8_4xb4-80k_isaid-896x896.py
│ ├── deeplabv3plus_r50-d8_4xb4-80k_loveda-512x512.py
│ ├── deeplabv3plus_r50-d8_4xb4-80k_pascal-context-480x480.py
│ ├── deeplabv3plus_r50-d8_4xb4-80k_pascal-context-59-480x480.py
│ ├── deeplabv3plus_r50-d8_4xb4-80k_potsdam-512x512.py
│ ├── deeplabv3plus_r50-d8_4xb4-80k_vaihingen-512x512.py
│ ├── deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-769x769.py
│ └── metafile.yaml
├── dmnet
│ ├── README.md
│ ├── dmnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── dmnet_r101-d8_4xb2-40k_cityscapes-769x769.py
│ ├── dmnet_r101-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── dmnet_r101-d8_4xb2-80k_cityscapes-769x769.py
│ ├── dmnet_r101-d8_4xb4-160k_ade20k-512x512.py
│ ├── dmnet_r101-d8_4xb4-80k_ade20k-512x512.py
│ ├── dmnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── dmnet_r50-d8_4xb2-40k_cityscapes-769x769.py
│ ├── dmnet_r50-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── dmnet_r50-d8_4xb2-80k_cityscapes-769x769.py
│ ├── dmnet_r50-d8_4xb4-160k_ade20k-512x512.py
│ ├── dmnet_r50-d8_4xb4-80k_ade20k-512x512.py
│ └── metafile.yaml
├── dnlnet
│ ├── README.md
│ ├── dnl_r101-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── dnl_r101-d8_4xb2-40k_cityscapes-769x769.py
│ ├── dnl_r101-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── dnl_r101-d8_4xb2-80k_cityscapes-769x769.py
│ ├── dnl_r101-d8_4xb4-160k_ade20k-512x512.py
│ ├── dnl_r101-d8_4xb4-80k_ade20k-512x512.py
│ ├── dnl_r50-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── dnl_r50-d8_4xb2-40k_cityscapes-769x769.py
│ ├── dnl_r50-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── dnl_r50-d8_4xb2-80k_cityscapes-769x769.py
│ ├── dnl_r50-d8_4xb4-160k_ade20k-512x512.py
│ ├── dnl_r50-d8_4xb4-80k_ade20k-512x512.py
│ └── metafile.yaml
├── dpt
│ ├── README.md
│ ├── dpt_vit-b16_8xb2-160k_ade20k-512x512.py
│ └── metafile.yaml
├── dsdl
│ ├── README.md
│ ├── cityscapes.py
│ └── voc.py
├── emanet
│ ├── README.md
│ ├── emanet_r101-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── emanet_r101-d8_4xb2-80k_cityscapes-769x769.py
│ ├── emanet_r50-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── emanet_r50-d8_4xb2-80k_cityscapes-769x769.py
│ └── metafile.yaml
├── encnet
│ ├── README.md
│ ├── encnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── encnet_r101-d8_4xb2-40k_cityscapes-769x769.py
│ ├── encnet_r101-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── encnet_r101-d8_4xb2-80k_cityscapes-769x769.py
│ ├── encnet_r101-d8_4xb4-160k_ade20k-512x512.py
│ ├── encnet_r101-d8_4xb4-20k_voc12aug-512x512.py
│ ├── encnet_r101-d8_4xb4-40k_voc12aug-512x512.py
│ ├── encnet_r101-d8_4xb4-80k_ade20k-512x512.py
│ ├── encnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── encnet_r50-d8_4xb2-40k_cityscapes-769x769.py
│ ├── encnet_r50-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── encnet_r50-d8_4xb2-80k_cityscapes-769x769.py
│ ├── encnet_r50-d8_4xb4-160k_ade20k-512x512.py
│ ├── encnet_r50-d8_4xb4-20k_voc12aug-512x512.py
│ ├── encnet_r50-d8_4xb4-40k_voc12aug-512x512.py
│ ├── encnet_r50-d8_4xb4-80k_ade20k-512x512.py
│ ├── encnet_r50s-d8_4xb4-80k_ade20k-512x512.py
│ └── metafile.yaml
├── erfnet
│ ├── README.md
│ ├── erfnet_fcn_4xb4-160k_cityscapes-512x1024.py
│ └── metafile.yaml
├── fastfcn
│ ├── README.md
│ ├── fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py
│ ├── fastfcn_r50-d32_jpu_aspp_4xb4-160k_ade20k-512x512.py
│ ├── fastfcn_r50-d32_jpu_aspp_4xb4-80k_ade20k-512x512.py
│ ├── fastfcn_r50-d32_jpu_aspp_4xb4-80k_cityscapes-512x1024.py
│ ├── fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024.py
│ ├── fastfcn_r50-d32_jpu_enc_4xb4-160k_ade20k-512x512.py
│ ├── fastfcn_r50-d32_jpu_enc_4xb4-80k_ade20k-512x512.py
│ ├── fastfcn_r50-d32_jpu_enc_4xb4-80k_cityscapes-512x1024.py
│ ├── fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py
│ ├── fastfcn_r50-d32_jpu_psp_4xb4-160k_ade20k-512x512.py
│ ├── fastfcn_r50-d32_jpu_psp_4xb4-80k_ade20k-512x512.py
│ ├── fastfcn_r50-d32_jpu_psp_4xb4-80k_cityscapes-512x1024.py
│ └── metafile.yaml
├── fastscnn
│ ├── README.md
│ ├── fast_scnn_8xb4-160k_cityscapes-512x1024.py
│ └── metafile.yaml
├── fcn
│ ├── README.md
│ ├── fcn-d6_r101-d16_4xb2-40k_cityscapes-512x1024.py
│ ├── fcn-d6_r101-d16_4xb2-40k_cityscapes-769x769.py
│ ├── fcn-d6_r101-d16_4xb2-80k_cityscapes-512x1024.py
│ ├── fcn-d6_r101-d16_4xb2-80k_cityscapes-769x769.py
│ ├── fcn-d6_r101b-d16_4xb2-80k_cityscapes-512x1024.py
│ ├── fcn-d6_r101b-d16_4xb2-80k_cityscapes-769x769.py
│ ├── fcn-d6_r50-d16_4xb2-40k_cityscapes-512x1024.py
│ ├── fcn-d6_r50-d16_4xb2-40k_cityscapes-769x769.py
│ ├── fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024.py
│ ├── fcn-d6_r50-d16_4xb2-80k_cityscapes-769x769.py
│ ├── fcn-d6_r50b-d16_4xb2-80k_cityscapes-512x1024.py
│ ├── fcn-d6_r50b-d16_4xb2-80k_cityscapes-769x769.py
│ ├── fcn_r101-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── fcn_r101-d8_4xb2-40k_cityscapes-769x769.py
│ ├── fcn_r101-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── fcn_r101-d8_4xb2-80k_cityscapes-769x769.py
│ ├── fcn_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py
│ ├── fcn_r101-d8_4xb4-160k_ade20k-512x512.py
│ ├── fcn_r101-d8_4xb4-20k_voc12aug-512x512.py
│ ├── fcn_r101-d8_4xb4-40k_pascal-context-480x480.py
│ ├── fcn_r101-d8_4xb4-40k_pascal-context-59-480x480.py
│ ├── fcn_r101-d8_4xb4-40k_voc12aug-512x512.py
│ ├── fcn_r101-d8_4xb4-80k_ade20k-512x512.py
│ ├── fcn_r101-d8_4xb4-80k_pascal-context-480x480.py
│ ├── fcn_r101-d8_4xb4-80k_pascal-context-59-480x480.py
│ ├── fcn_r101b-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── fcn_r101b-d8_4xb2-80k_cityscapes-769x769.py
│ ├── fcn_r18-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── fcn_r18-d8_4xb2-80k_cityscapes-769x769.py
│ ├── fcn_r18b-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── fcn_r18b-d8_4xb2-80k_cityscapes-769x769.py
│ ├── fcn_r50-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── fcn_r50-d8_4xb2-40k_cityscapes-769x769.py
│ ├── fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── fcn_r50-d8_4xb2-80k_cityscapes-769x769.py
│ ├── fcn_r50-d8_4xb4-160k_ade20k-512x512.py
│ ├── fcn_r50-d8_4xb4-20k_voc12aug-512x512.py
│ ├── fcn_r50-d8_4xb4-40k_pascal-context-480x480.py
│ ├── fcn_r50-d8_4xb4-40k_pascal-context-59-480x480.py
│ ├── fcn_r50-d8_4xb4-40k_voc12aug-512x512.py
│ ├── fcn_r50-d8_4xb4-80k_ade20k-512x512.py
│ ├── fcn_r50-d8_4xb4-80k_pascal-context-480x480.py
│ ├── fcn_r50-d8_4xb4-80k_pascal-context-59-480x480.py
│ ├── fcn_r50b-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── fcn_r50b-d8_4xb2-80k_cityscapes-769x769.py
│ └── metafile.yaml
├── gcnet
│ ├── README.md
│ ├── gcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── gcnet_r101-d8_4xb2-40k_cityscapes-769x769.py
│ ├── gcnet_r101-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── gcnet_r101-d8_4xb2-80k_cityscapes-769x769.py
│ ├── gcnet_r101-d8_4xb4-160k_ade20k-512x512.py
│ ├── gcnet_r101-d8_4xb4-20k_voc12aug-512x512.py
│ ├── gcnet_r101-d8_4xb4-40k_voc12aug-512x512.py
│ ├── gcnet_r101-d8_4xb4-80k_ade20k-512x512.py
│ ├── gcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── gcnet_r50-d8_4xb2-40k_cityscapes-769x769.py
│ ├── gcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── gcnet_r50-d8_4xb2-80k_cityscapes-769x769.py
│ ├── gcnet_r50-d8_4xb4-160k_ade20k-512x512.py
│ ├── gcnet_r50-d8_4xb4-20k_voc12aug-512x512.py
│ ├── gcnet_r50-d8_4xb4-40k_voc12aug-512x512.py
│ ├── gcnet_r50-d8_4xb4-80k_ade20k-512x512.py
│ └── metafile.yaml
├── hrnet
│ ├── README.md
│ ├── fcn_hr18_4xb2-160k_cityscapes-512x1024.py
│ ├── fcn_hr18_4xb2-40k_cityscapes-512x1024.py
│ ├── fcn_hr18_4xb2-80k_cityscapes-512x1024.py
│ ├── fcn_hr18_4xb4-160k_ade20k-512x512.py
│ ├── fcn_hr18_4xb4-20k_voc12aug-512x512.py
│ ├── fcn_hr18_4xb4-40k_pascal-context-480x480.py
│ ├── fcn_hr18_4xb4-40k_pascal-context-59-480x480.py
│ ├── fcn_hr18_4xb4-40k_voc12aug-512x512.py
│ ├── fcn_hr18_4xb4-80k_ade20k-512x512.py
│ ├── fcn_hr18_4xb4-80k_isaid-896x896.py
│ ├── fcn_hr18_4xb4-80k_loveda-512x512.py
│ ├── fcn_hr18_4xb4-80k_pascal-context-480x480.py
│ ├── fcn_hr18_4xb4-80k_pascal-context-59-480x480.py
│ ├── fcn_hr18_4xb4-80k_potsdam-512x512.py
│ ├── fcn_hr18_4xb4-80k_vaihingen-512x512.py
│ ├── fcn_hr18s_4xb2-160k_cityscapes-512x1024.py
│ ├── fcn_hr18s_4xb2-40k_cityscapes-512x1024.py
│ ├── fcn_hr18s_4xb2-80k_cityscapes-512x1024.py
│ ├── fcn_hr18s_4xb4-160k_ade20k-512x512.py
│ ├── fcn_hr18s_4xb4-20k_voc12aug-512x512.py
│ ├── fcn_hr18s_4xb4-40k_pascal-context-480x480.py
│ ├── fcn_hr18s_4xb4-40k_pascal-context-59-480x480.py
│ ├── fcn_hr18s_4xb4-40k_voc12aug-512x512.py
│ ├── fcn_hr18s_4xb4-80k_ade20k-512x512.py
│ ├── fcn_hr18s_4xb4-80k_isaid-896x896.py
│ ├── fcn_hr18s_4xb4-80k_loveda-512x512.py
│ ├── fcn_hr18s_4xb4-80k_pascal-context-480x480.py
│ ├── fcn_hr18s_4xb4-80k_pascal-context-59-480x480.py
│ ├── fcn_hr18s_4xb4-80k_potsdam-512x512.py
│ ├── fcn_hr18s_4xb4-80k_vaihingen-512x512.py
│ ├── fcn_hr48_4xb2-160k_cityscapes-512x1024.py
│ ├── fcn_hr48_4xb2-40k_cityscapes-512x1024.py
│ ├── fcn_hr48_4xb2-80k_cityscapes-512x1024.py
│ ├── fcn_hr48_4xb4-160k_ade20k-512x512.py
│ ├── fcn_hr48_4xb4-20k_voc12aug-512x512.py
│ ├── fcn_hr48_4xb4-40k_pascal-context-480x480.py
│ ├── fcn_hr48_4xb4-40k_pascal-context-59-480x480.py
│ ├── fcn_hr48_4xb4-40k_voc12aug-512x512.py
│ ├── fcn_hr48_4xb4-80k_ade20k-512x512.py
│ ├── fcn_hr48_4xb4-80k_isaid-896x896.py
│ ├── fcn_hr48_4xb4-80k_loveda-512x512.py
│ ├── fcn_hr48_4xb4-80k_pascal-context-480x480.py
│ ├── fcn_hr48_4xb4-80k_pascal-context-59-480x480.py
│ ├── fcn_hr48_4xb4-80k_potsdam-512x512.py
│ ├── fcn_hr48_4xb4-80k_vaihingen-512x512.py
│ └── metafile.yaml
├── icnet
│ ├── README.md
│ ├── icnet_r101-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py
│ ├── icnet_r101-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py
│ ├── icnet_r101-d8_4xb2-160k_cityscapes-832x832.py
│ ├── icnet_r101-d8_4xb2-80k_cityscapes-832x832.py
│ ├── icnet_r18-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py
│ ├── icnet_r18-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py
│ ├── icnet_r18-d8_4xb2-160k_cityscapes-832x832.py
│ ├── icnet_r18-d8_4xb2-80k_cityscapes-832x832.py
│ ├── icnet_r50-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py
│ ├── icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py
│ ├── icnet_r50-d8_4xb2-160k_cityscapes-832x832.py
│ ├── icnet_r50-d8_4xb2-80k_cityscapes-832x832.py
│ └── metafile.yaml
├── isanet
│ ├── README.md
│ ├── isanet_r101-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── isanet_r101-d8_4xb2-40k_cityscapes-769x769.py
│ ├── isanet_r101-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── isanet_r101-d8_4xb2-80k_cityscapes-769x769.py
│ ├── isanet_r101-d8_4xb4-160k_ade20k-512x512.py
│ ├── isanet_r101-d8_4xb4-20k_voc12aug-512x512.py
│ ├── isanet_r101-d8_4xb4-40k_voc12aug-512x512.py
│ ├── isanet_r101-d8_4xb4-80k_ade20k-512x512.py
│ ├── isanet_r50-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── isanet_r50-d8_4xb2-40k_cityscapes-769x769.py
│ ├── isanet_r50-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── isanet_r50-d8_4xb2-80k_cityscapes-769x769.py
│ ├── isanet_r50-d8_4xb4-160k_ade20k-512x512.py
│ ├── isanet_r50-d8_4xb4-20k_voc12aug-512x512.py
│ ├── isanet_r50-d8_4xb4-40k_voc12aug-512x512.py
│ ├── isanet_r50-d8_4xb4-80k_ade20k-512x512.py
│ └── metafile.yaml
├── knet
│ ├── README.md
│ ├── knet-s3_r50-d8_deeplabv3_8xb2-adamw-80k_ade20k-512x512.py
│ ├── knet-s3_r50-d8_fcn_8xb2-adamw-80k_ade20k-512x512.py
│ ├── knet-s3_r50-d8_pspnet_8xb2-adamw-80k_ade20k-512x512.py
│ ├── knet-s3_r50-d8_upernet_8xb2-adamw-80k_ade20k-512x512.py
│ ├── knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-512x512.py
│ ├── knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-640x640.py
│ ├── knet-s3_swin-t_upernet_8xb2-adamw-80k_ade20k-512x512.py
│ └── metafile.yaml
├── mae
│ ├── README.md
│ ├── mae-base_upernet_8xb2-amp-160k_ade20k-512x512-ms.py
│ ├── mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py
│ └── metafile.yaml
├── mask2former
│ ├── README.md
│ ├── mask2former_r101_8xb2-160k_ade20k-512x512.py
│ ├── mask2former_r101_8xb2-90k_cityscapes-512x1024.py
│ ├── mask2former_r50_8xb2-160k_ade20k-512x512.py
│ ├── mask2former_r50_8xb2-90k_cityscapes-512x1024.py
│ ├── mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640.py
│ ├── mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py
│ ├── mask2former_swin-b-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024.py
│ ├── mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py
│ ├── mask2former_swin-l-in22k-384x384-pre_8xb2-90k_cityscapes-512x1024.py
│ ├── mask2former_swin-s_8xb2-160k_ade20k-512x512.py
│ ├── mask2former_swin-s_8xb2-90k_cityscapes-512x1024.py
│ ├── mask2former_swin-t_8xb2-160k_ade20k-512x512.py
│ ├── mask2former_swin-t_8xb2-90k_cityscapes-512x1024.py
│ └── metafile.yaml
├── maskformer
│ ├── README.md
│ ├── maskformer_r101-d32_8xb2-160k_ade20k-512x512.py
│ ├── maskformer_r50-d32_8xb2-160k_ade20k-512x512.py
│ ├── maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512.py
│ ├── maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py
│ └── metafile.yaml
├── mobilenet_v2
│ ├── README.md
│ ├── metafile.yaml
│ ├── mobilenet-v2-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py
│ ├── mobilenet-v2-d8_deeplabv3_4xb4-160k_ade20k-512x512.py
│ ├── mobilenet-v2-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py
│ ├── mobilenet-v2-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py
│ ├── mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024.py
│ ├── mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512.py
│ ├── mobilenet-v2-d8_pspnet_4xb2-80k_cityscapes-512x1024.py
│ └── mobilenet-v2-d8_pspnet_4xb4-160k_ade20k-512x512.py
├── mobilenet_v3
│ ├── README.md
│ ├── metafile.yaml
│ ├── mobilenet-v3-d8-s_lraspp_4xb4-320k_cityscapes-512x1024.py
│ ├── mobilenet-v3-d8-scratch-s_lraspp_4xb4-320k_cityscapes-512x1024.py
│ ├── mobilenet-v3-d8-scratch_lraspp_4xb4-320k_cityscapes-512x1024.py
│ └── mobilenet-v3-d8_lraspp_4xb4-320k_cityscapes-512x1024.py
├── nonlocal_net
│ ├── README.md
│ ├── metafile.yaml
│ ├── nonlocal_r101-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── nonlocal_r101-d8_4xb2-40k_cityscapes-769x769.py
│ ├── nonlocal_r101-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── nonlocal_r101-d8_4xb2-80k_cityscapes-769x769.py
│ ├── nonlocal_r101-d8_4xb4-160k_ade20k-512x512.py
│ ├── nonlocal_r101-d8_4xb4-20k_voc12aug-512x512.py
│ ├── nonlocal_r101-d8_4xb4-40k_voc12aug-512x512.py
│ ├── nonlocal_r101-d8_4xb4-80k_ade20k-512x512.py
│ ├── nonlocal_r50-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── nonlocal_r50-d8_4xb2-40k_cityscapes-769x769.py
│ ├── nonlocal_r50-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── nonlocal_r50-d8_4xb2-80k_cityscapes-769x769.py
│ ├── nonlocal_r50-d8_4xb4-160k_ade20k-512x512.py
│ ├── nonlocal_r50-d8_4xb4-20k_voc12aug-512x512.py
│ ├── nonlocal_r50-d8_4xb4-40k_voc12aug-512x512.py
│ └── nonlocal_r50-d8_4xb4-80k_ade20k-512x512.py
├── ocrnet
│ ├── README.md
│ ├── metafile.yaml
│ ├── ocrnet_hr18_4xb2-160k_cityscapes-512x1024.py
│ ├── ocrnet_hr18_4xb2-40k_cityscapes-512x1024.py
│ ├── ocrnet_hr18_4xb2-80k_cityscapes-512x1024.py
│ ├── ocrnet_hr18_4xb4-160k_ade20k-512x512.py
│ ├── ocrnet_hr18_4xb4-20k_voc12aug-512x512.py
│ ├── ocrnet_hr18_4xb4-40k_voc12aug-512x512.py
│ ├── ocrnet_hr18_4xb4-80k_ade20k-512x512.py
│ ├── ocrnet_hr18s_4xb2-160k_cityscapes-512x1024.py
│ ├── ocrnet_hr18s_4xb2-40k_cityscapes-512x1024.py
│ ├── ocrnet_hr18s_4xb2-80k_cityscapes-512x1024.py
│ ├── ocrnet_hr18s_4xb4-160k_ade20k-512x512.py
│ ├── ocrnet_hr18s_4xb4-20k_voc12aug-512x512.py
│ ├── ocrnet_hr18s_4xb4-40k_voc12aug-512x512.py
│ ├── ocrnet_hr18s_4xb4-80k_ade20k-512x512.py
│ ├── ocrnet_hr48_4xb2-160k_cityscapes-512x1024.py
│ ├── ocrnet_hr48_4xb2-40k_cityscapes-512x1024.py
│ ├── ocrnet_hr48_4xb2-80k_cityscapes-512x1024.py
│ ├── ocrnet_hr48_4xb4-160k_ade20k-512x512.py
│ ├── ocrnet_hr48_4xb4-20k_voc12aug-512x512.py
│ ├── ocrnet_hr48_4xb4-40k_voc12aug-512x512.py
│ ├── ocrnet_hr48_4xb4-80k_ade20k-512x512.py
│ ├── ocrnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── ocrnet_r101-d8_8xb2-40k_cityscapes-512x1024.py
│ └── ocrnet_r101-d8_8xb2-80k_cityscapes-512x1024.py
├── pidnet
│ ├── README.md
│ ├── metafile.yaml
│ ├── pidnet-l_2xb6-120k_1024x1024-cityscapes.py
│ ├── pidnet-m_2xb6-120k_1024x1024-cityscapes.py
│ └── pidnet-s_2xb6-120k_1024x1024-cityscapes.py
├── point_rend
│ ├── README.md
│ ├── metafile.yaml
│ ├── pointrend_r101_4xb2-80k_cityscapes-512x1024.py
│ ├── pointrend_r101_4xb4-160k_ade20k-512x512.py
│ ├── pointrend_r50_4xb2-80k_cityscapes-512x1024.py
│ └── pointrend_r50_4xb4-160k_ade20k-512x512.py
├── poolformer
│ ├── README.md
│ ├── fpn_poolformer_m36_8xb4-40k_ade20k-512x512.py
│ ├── fpn_poolformer_m48_8xb4-40k_ade20k-512x512.py
│ ├── fpn_poolformer_s12_8xb4-40k_ade20k-512x512.py
│ ├── fpn_poolformer_s24_8xb4-40k_ade20k-512x512.py
│ ├── fpn_poolformer_s36_8x4_512x512_40k_ade20k.py
│ └── metafile.yaml
├── psanet
│ ├── README.md
│ ├── metafile.yaml
│ ├── psanet_r101-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── psanet_r101-d8_4xb2-40k_cityscapes-769x769.py
│ ├── psanet_r101-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── psanet_r101-d8_4xb2-80k_cityscapes-769x769.py
│ ├── psanet_r101-d8_4xb4-160k_ade20k-512x512.py
│ ├── psanet_r101-d8_4xb4-20k_voc12aug-512x512.py
│ ├── psanet_r101-d8_4xb4-40k_voc12aug-512x512.py
│ ├── psanet_r101-d8_4xb4-80k_ade20k-512x512.py
│ ├── psanet_r50-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── psanet_r50-d8_4xb2-40k_cityscapes-769x769.py
│ ├── psanet_r50-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── psanet_r50-d8_4xb2-80k_cityscapes-769x769.py
│ ├── psanet_r50-d8_4xb4-160k_ade20k-512x512.py
│ ├── psanet_r50-d8_4xb4-20k_voc12aug-512x512.py
│ ├── psanet_r50-d8_4xb4-40k_voc12aug-512x512.py
│ └── psanet_r50-d8_4xb4-80k_ade20k-512x512.py
├── pspnet
│ ├── README.md
│ ├── metafile.yaml
│ ├── pspnet_r101-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── pspnet_r101-d8_4xb2-40k_cityscapes-512x1024_dark-zurich-1920x1080.py
│ ├── pspnet_r101-d8_4xb2-40k_cityscapes-512x1024_night-driving-1920x1080.py
│ ├── pspnet_r101-d8_4xb2-40k_cityscapes-769x769.py
│ ├── pspnet_r101-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── pspnet_r101-d8_4xb2-80k_cityscapes-769x769.py
│ ├── pspnet_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py
│ ├── pspnet_r101-d8_4xb4-160k_ade20k-512x512.py
│ ├── pspnet_r101-d8_4xb4-160k_coco-stuff164k-512x512.py
│ ├── pspnet_r101-d8_4xb4-20k_coco-stuff10k-512x512.py
│ ├── pspnet_r101-d8_4xb4-20k_voc12aug-512x512.py
│ ├── pspnet_r101-d8_4xb4-320k_coco-stuff164k-512x512.py
│ ├── pspnet_r101-d8_4xb4-40k_coco-stuff10k-512x512.py
│ ├── pspnet_r101-d8_4xb4-40k_pascal-context-480x480.py
│ ├── pspnet_r101-d8_4xb4-40k_pascal-context-59-480x480.py
│ ├── pspnet_r101-d8_4xb4-40k_voc12aug-512x512.py
│ ├── pspnet_r101-d8_4xb4-80k_ade20k-512x512.py
│ ├── pspnet_r101-d8_4xb4-80k_coco-stuff164k-512x512.py
│ ├── pspnet_r101-d8_4xb4-80k_loveda-512x512.py
│ ├── pspnet_r101-d8_4xb4-80k_pascal-context-480x480.py
│ ├── pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480.py
│ ├── pspnet_r101-d8_4xb4-80k_potsdam-512x512.py
│ ├── pspnet_r101-d8_4xb4-80k_vaihingen-512x512.py
│ ├── pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024_dark-zurich-1920x1080.py
│ ├── pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024_night-driving-1920x1080.py
│ ├── pspnet_r101b-d8_4xb2-80k_cityscapes-769x769.py
│ ├── pspnet_r18-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── pspnet_r18-d8_4xb2-80k_cityscapes-769x769.py
│ ├── pspnet_r18-d8_4xb4-80k_isaid-896x896.py
│ ├── pspnet_r18-d8_4xb4-80k_loveda-512x512.py
│ ├── pspnet_r18-d8_4xb4-80k_potsdam-512x512.py
│ ├── pspnet_r18-d8_4xb4-80k_vaihingen-512x512.py
│ ├── pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── pspnet_r18b-d8_4xb2-80k_cityscapes-769x769.py
│ ├── pspnet_r50-d32_4xb2-80k_cityscapes-512x1024.py
│ ├── pspnet_r50-d32_rsb_4xb2-adamw-80k_cityscapes-512x1024.py
│ ├── pspnet_r50-d8-rsb_4xb2-adamw-80k_cityscapes-512x1024.py
│ ├── pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
│ ├── pspnet_r50-d8_4xb2-40k_cityscapes-512x1024_dark-zurich-1920x1080.py
│ ├── pspnet_r50-d8_4xb2-40k_cityscapes-512x1024_night-driving-1920x1080.py
│ ├── pspnet_r50-d8_4xb2-40k_cityscapes-769x769.py
│ ├── pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py
│ ├── pspnet_r50-d8_4xb2-80k_cityscapes-512x1024_dark-zurich-1920x1080.py
│ ├── pspnet_r50-d8_4xb2-80k_cityscapes-512x1024_night-driving-1920x1080.py
│ ├── pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py
│ ├── pspnet_r50-d8_4xb4-160k_ade20k-512x512.py
│ ├── pspnet_r50-d8_4xb4-160k_coco-stuff164k-512x512.py
│ ├── pspnet_r50-d8_4xb4-20k_coco-stuff10k-512x512.py
│ ├── pspnet_r50-d8_4xb4-20k_voc12aug-512x512.py
│ ├── pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py
│ ├── pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py
│ ├── pspnet_r50-d8_4xb4-40k_pascal-context-480x480.py
│ ├── pspnet_r50-d8_4xb4-40k_pascal-context-59-480x480.py
│ ├── pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py
│ ├── pspnet_r50-d8_4xb4-80k_ade20k-512x512.py
│ ├── pspnet_r50-d8_4xb4-80k_coco-stuff164k-512x512.py
│ ├── pspnet_r50-d8_4xb4-80k_isaid-896x896.py
│ ├── pspnet_r50-d8_4xb4-80k_loveda-512x512.py
│ ├── pspnet_r50-d8_4xb4-80k_pascal-context-480x480.py
│ ├── pspnet_r50-d8_4xb4-80k_pascal-context-59-480x480.py
│ ├── pspnet_r50-d8_4xb4-80k_potsdam-512x512.py
│ ├── pspnet_r50-d8_4xb4-80k_vaihingen-512x512.py
│ ├── pspnet_r50b-d32_4xb2-80k_cityscapes-512x1024.py
│ ├── pspnet_r50b-d8_4xb2-80k_cityscapes-512x1024.py
│ └── pspnet_r50b-d8_4xb2-80k_cityscapes-769x769.py
├── resnest
│ ├── README.md
│ ├── metafile.yaml
│ ├── resnest_s101-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py
│ ├── resnest_s101-d8_deeplabv3_4xb4-160k_ade20k-512x512.py
│ ├── resnest_s101-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py
│ ├── resnest_s101-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py
│ ├── resnest_s101-d8_fcn_4xb2-80k_cityscapes-512x1024.py
│ ├── resnest_s101-d8_fcn_4xb4-160k_ade20k-512x512.py
│ ├── resnest_s101-d8_pspnet_4xb2-80k_cityscapes512x1024.py
│ └── resnest_s101-d8_pspnet_4xb4-160k_ade20k-512x512.py
├── samba
│ ├── samba_upernet-15k_loveda-512x512_6e4.py
│ ├── samba_upernet-15k_potsdam-512x512_6e4.py
│ └── samba_upernet-15k_vaihingen-512x512_6e4.py
├── san
│ ├── README.md
│ ├── metafile.yaml
│ ├── san-vit-b16_coco-stuff164k-640x640.py
│ ├── san-vit-b16_pascal_context-640x640.py
│ ├── san-vit-b16_voc12aug-640x640.py
│ ├── san-vit-l14_coco-stuff164k-640x640.py
│ ├── san-vit-l14_pascal_context-640x640.py
│ └── san-vit-l14_voc12aug-640x640.py
├── segformer
│ ├── README.md
│ ├── metafile.yaml
│ ├── segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py
│ ├── segformer_mit-b0_8xb2-160k_ade20k-512x512.py
│ ├── segformer_mit-b1_8xb1-160k_cityscapes-1024x1024.py
│ ├── segformer_mit-b1_8xb2-160k_ade20k-512x512.py
│ ├── segformer_mit-b2_8xb1-160k_cityscapes-1024x1024.py
│ ├── segformer_mit-b2_8xb2-160k_ade20k-512x512.py
│ ├── segformer_mit-b3_8xb1-160k_cityscapes-1024x1024.py
│ ├── segformer_mit-b3_8xb2-160k_ade20k-512x512.py
│ ├── segformer_mit-b4_8xb1-160k_cityscapes-1024x1024.py
│ ├── segformer_mit-b4_8xb2-160k_ade20k-512x512.py
│ ├── segformer_mit-b5_8xb1-160k_cityscapes-1024x1024.py
│ ├── segformer_mit-b5_8xb2-160k_ade20k-512x512.py
│ └── segformer_mit-b5_8xb2-160k_ade20k-640x640.py
├── segmenter
│ ├── README.md
│ ├── metafile.yaml
│ ├── segmenter_vit-b_mask_8xb1-160k_ade20k-512x512.py
│ ├── segmenter_vit-l_mask_8xb1-160k_ade20k-512x512.py
│ ├── segmenter_vit-s_fcn_8xb1-160k_ade20k-512x512.py
│ ├── segmenter_vit-s_mask_8xb1-160k_ade20k-512x512.py
│ └── segmenter_vit-t_mask_8xb1-160k_ade20k-512x512.py
├── segnext
│ ├── README.md
│ ├── metafile.yaml
│ ├── segnext_mscan-b_1xb16-adamw-160k_ade20k-512x512.py
│ ├── segnext_mscan-l_1xb16-adamw-160k_ade20k-512x512.py
│ ├── segnext_mscan-s_1xb16-adamw-160k_ade20k-512x512.py
│ └── segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py
├── sem_fpn
│ ├── README.md
│ ├── fpn_r101_4xb2-80k_cityscapes-512x1024.py
│ ├── fpn_r101_4xb4-160k_ade20k-512x512.py
│ ├── fpn_r50_4xb2-80k_cityscapes-512x1024.py
│ ├── fpn_r50_4xb4-160k_ade20k-512x512.py
│ └── metafile.yaml
├── setr
│ ├── README.md
│ ├── metafile.yaml
│ ├── setr_vit-l-mla_8xb1-160k_ade20k-512x512.py
│ ├── setr_vit-l_mla_8xb1-80k_cityscapes-768x768.py
│ ├── setr_vit-l_mla_8xb2-160k_ade20k-512x512.py
│ ├── setr_vit-l_naive_8xb1-80k_cityscapes-768x768.py
│ ├── setr_vit-l_naive_8xb2-160k_ade20k-512x512.py
│ ├── setr_vit-l_pup_8xb1-80k_cityscapes-768x768.py
│ └── setr_vit-l_pup_8xb2-160k_ade20k-512x512.py
├── stdc
│ ├── README.md
│ ├── metafile.yaml
│ ├── stdc1_4xb12-80k_cityscapes-512x1024.py
│ ├── stdc1_in1k-pre_4xb12-80k_cityscapes-512x1024.py
│ ├── stdc2_4xb12-80k_cityscapes-512x1024.py
│ └── stdc2_in1k-pre_4xb12-80k_cityscapes-512x1024.py
├── swin
│ ├── README.md
│ ├── metafile.yaml
│ ├── swin-base-patch4-window12-in1k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py
│ ├── swin-base-patch4-window12-in22k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py
│ ├── swin-base-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py
│ ├── swin-base-patch4-window7-in22k-pre_upernet_8xb2-160k_ade20k-512x512.py
│ ├── swin-large-patch4-window12-in22k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py
│ ├── swin-large-patch4-window7-in22k-pre_upernet_8xb2-160k_ade20k-512x512.py
│ ├── swin-small-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py
│ ├── swin-tiny-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py
│ └── swin-tiny-patch4-window7_upernet_1xb8-20k_levir-256x256.py
├── twins
│ ├── README.md
│ ├── metafile.yaml
│ ├── twins_pcpvt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
│ ├── twins_pcpvt-b_uperhead_8xb2-160k_ade20k-512x512.py
│ ├── twins_pcpvt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
│ ├── twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512.py
│ ├── twins_pcpvt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
│ ├── twins_pcpvt-s_uperhead_8xb4-160k_ade20k-512x512.py
│ ├── twins_svt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
│ ├── twins_svt-b_uperhead_8xb2-160k_ade20k-512x512.py
│ ├── twins_svt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
│ ├── twins_svt-l_uperhead_8xb2-160k_ade20k-512x512.py
│ ├── twins_svt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py
│ └── twins_svt-s_uperhead_8xb2-160k_ade20k-512x512.py
├── unet
│ ├── README.md
│ ├── metafile.yaml
│ ├── unet-s5-d16_deeplabv3_4xb4-40k_drive-64x64.py
│ ├── unet-s5-d16_deeplabv3_4xb4-40k_hrf-256x256.py
│ ├── unet-s5-d16_deeplabv3_4xb4-40k_stare-128x128.py
│ ├── unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py
│ ├── unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py
│ ├── unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py
│ ├── unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py
│ ├── unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024.py
│ ├── unet-s5-d16_fcn_4xb4-160k_hsidrive-192x384.py
│ ├── unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128.py
│ ├── unet-s5-d16_fcn_4xb4-40k_drive-64x64.py
│ ├── unet-s5-d16_fcn_4xb4-40k_hrf-256x256.py
│ ├── unet-s5-d16_fcn_4xb4-40k_stare-128x128.py
│ ├── unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py
│ ├── unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py
│ ├── unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py
│ ├── unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py
│ ├── unet-s5-d16_pspnet_4xb4-40k_chase-db1-128x128.py
│ ├── unet-s5-d16_pspnet_4xb4-40k_drive-64x64.py
│ ├── unet-s5-d16_pspnet_4xb4-40k_hrf-256x256.py
│ ├── unet-s5-d16_pspnet_4xb4-40k_stare-128x128.py
│ ├── unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py
│ ├── unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py
│ ├── unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py
│ ├── unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py
│ └── unet_s5-d16_deeplabv3_4xb4-40k_chase-db1-128x128.py
├── upernet
│ ├── README.md
│ ├── metafile.yaml
│ ├── upernet_r101_4xb2-40k_cityscapes-512x1024.py
│ ├── upernet_r101_4xb2-40k_cityscapes-769x769.py
│ ├── upernet_r101_4xb2-80k_cityscapes-512x1024.py
│ ├── upernet_r101_4xb2-80k_cityscapes-769x769.py
│ ├── upernet_r101_4xb4-160k_ade20k-512x512.py
│ ├── upernet_r101_4xb4-20k_voc12aug-512x512.py
│ ├── upernet_r101_4xb4-40k_voc12aug-512x512.py
│ ├── upernet_r101_4xb4-80k_ade20k-512x512.py
│ ├── upernet_r18_4xb2-40k_cityscapes-512x1024.py
│ ├── upernet_r18_4xb2-80k_cityscapes-512x1024.py
│ ├── upernet_r18_4xb4-160k_ade20k-512x512.py
│ ├── upernet_r18_4xb4-20k_voc12aug-512x512.py
│ ├── upernet_r18_4xb4-40k_voc12aug-512x512.py
│ ├── upernet_r18_4xb4-80k_ade20k-512x512.py
│ ├── upernet_r50_4xb2-40k_cityscapes-512x1024.py
│ ├── upernet_r50_4xb2-40k_cityscapes-769x769.py
│ ├── upernet_r50_4xb2-80k_cityscapes-512x1024.py
│ ├── upernet_r50_4xb2-80k_cityscapes-769x769.py
│ ├── upernet_r50_4xb4-160k_ade20k-512x512.py
│ ├── upernet_r50_4xb4-20k_voc12aug-512x512.py
│ ├── upernet_r50_4xb4-40k_voc12aug-512x512.py
│ └── upernet_r50_4xb4-80k_ade20k-512x512.py
├── vit
│ ├── README.md
│ ├── metafile.yaml
│ ├── vit_deit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py
│ ├── vit_deit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py
│ ├── vit_deit-b16_upernet_8xb2-160k_ade20k-512x512.py
│ ├── vit_deit-b16_upernet_8xb2-80k_ade20k-512x512.py
│ ├── vit_deit-s16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py
│ ├── vit_deit-s16_mln_upernet_8xb2-160k_ade20k-512x512.py
│ ├── vit_deit-s16_upernet_8xb2-160k_ade20k-512x512.py
│ ├── vit_deit-s16_upernet_8xb2-80k_ade20k-512x512.py
│ ├── vit_vit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py
│ ├── vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py
│ └── vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py
└── vpd
│ ├── README.md
│ ├── metafile.yaml
│ ├── vpd_sd_4xb8-25k_nyu-480x480.py
│ └── vpd_sd_4xb8-25k_nyu-512x512.py
├── dataset-index.yml
├── demo
├── MMSegmentation_Tutorial.ipynb
├── classroom__rgb_00283.jpg
├── demo.png
├── image_demo.py
├── image_demo_with_inferencer.py
├── inference_demo.ipynb
├── rs_image_inference.py
└── video_demo.py
├── docker
├── Dockerfile
└── serve
│ ├── Dockerfile
│ ├── config.properties
│ └── entrypoint.sh
├── docs
├── en
│ ├── .readthedocs.yaml
│ ├── Makefile
│ ├── _static
│ │ ├── css
│ │ │ └── readthedocs.css
│ │ └── images
│ │ │ └── mmsegmentation.png
│ ├── advanced_guides
│ │ ├── add_datasets.md
│ │ ├── add_metrics.md
│ │ ├── add_models.md
│ │ ├── add_transforms.md
│ │ ├── customize_runtime.md
│ │ ├── data_flow.md
│ │ ├── datasets.md
│ │ ├── engine.md
│ │ ├── evaluation.md
│ │ ├── index.rst
│ │ ├── models.md
│ │ ├── structures.md
│ │ ├── training_tricks.md
│ │ └── transforms.md
│ ├── api.rst
│ ├── conf.py
│ ├── device
│ │ └── npu.md
│ ├── get_started.md
│ ├── index.rst
│ ├── make.bat
│ ├── migration
│ │ ├── index.rst
│ │ ├── interface.md
│ │ └── package.md
│ ├── model_zoo.md
│ ├── modelzoo_statistics.md
│ ├── notes
│ │ ├── changelog.md
│ │ ├── changelog_v0.x.md
│ │ └── faq.md
│ ├── overview.md
│ ├── stat.py
│ ├── switch_language.md
│ └── user_guides
│ │ ├── 1_config.md
│ │ ├── 2_dataset_prepare.md
│ │ ├── 3_inference.md
│ │ ├── 4_train_test.md
│ │ ├── 5_deployment.md
│ │ ├── index.rst
│ │ ├── useful_tools.md
│ │ ├── visualization.md
│ │ └── visualization_feature_map.md
└── zh_cn
│ ├── .readthedocs.yaml
│ ├── Makefile
│ ├── _static
│ ├── css
│ │ └── readthedocs.css
│ └── images
│ │ └── mmsegmentation.png
│ ├── advanced_guides
│ ├── add_datasets.md
│ ├── add_metrics.md
│ ├── add_models.md
│ ├── add_transforms.md
│ ├── contribute_dataset.md
│ ├── customize_runtime.md
│ ├── data_flow.md
│ ├── datasets.md
│ ├── engine.md
│ ├── evaluation.md
│ ├── index.rst
│ ├── models.md
│ ├── structures.md
│ ├── training_tricks.md
│ └── transforms.md
│ ├── api.rst
│ ├── conf.py
│ ├── device
│ └── npu.md
│ ├── get_started.md
│ ├── imgs
│ └── zhihu_qrcode.jpg
│ ├── index.rst
│ ├── make.bat
│ ├── migration
│ ├── index.rst
│ ├── interface.md
│ └── package.md
│ ├── model_zoo.md
│ ├── modelzoo_statistics.md
│ ├── notes
│ └── faq.md
│ ├── overview.md
│ ├── stat.py
│ ├── switch_language.md
│ └── user_guides
│ ├── 1_config.md
│ ├── 2_dataset_prepare.md
│ ├── 3_inference.md
│ ├── 4_train_test.md
│ ├── 5_deployment.md
│ ├── deploy_jetson.md
│ ├── index.rst
│ ├── useful_tools.md
│ ├── visualization.md
│ └── visualization_feature_map.md
├── mmseg
├── __init__.py
├── apis
│ ├── __init__.py
│ ├── inference.py
│ ├── mmseg_inferencer.py
│ ├── remote_sense_inferencer.py
│ └── utils.py
├── configs
│ └── _base_
│ │ ├── datasets
│ │ ├── loveda.py
│ │ └── potsdam.py
│ │ ├── default_runtime.py
│ │ └── schedules
│ │ ├── schedule_160k.py
│ │ ├── schedule_20k.py
│ │ ├── schedule_240k.py
│ │ ├── schedule_25k.py
│ │ ├── schedule_320k.py
│ │ ├── schedule_40k.py
│ │ └── schedule_80k.py
├── datasets
│ ├── __init__.py
│ ├── ade.py
│ ├── basesegdataset.py
│ ├── bdd100k.py
│ ├── chase_db1.py
│ ├── cityscapes.py
│ ├── coco_stuff.py
│ ├── dark_zurich.py
│ ├── dataset_wrappers.py
│ ├── decathlon.py
│ ├── drive.py
│ ├── dsdl.py
│ ├── hrf.py
│ ├── hsi_drive.py
│ ├── isaid.py
│ ├── isprs.py
│ ├── levir.py
│ ├── lip.py
│ ├── loveda.py
│ ├── mapillary.py
│ ├── night_driving.py
│ ├── nyu.py
│ ├── pascal_context.py
│ ├── potsdam.py
│ ├── refuge.py
│ ├── stare.py
│ ├── synapse.py
│ ├── transforms
│ │ ├── __init__.py
│ │ ├── formatting.py
│ │ ├── loading.py
│ │ └── transforms.py
│ └── voc.py
├── engine
│ ├── __init__.py
│ ├── hooks
│ │ ├── __init__.py
│ │ └── visualization_hook.py
│ ├── optimizers
│ │ ├── __init__.py
│ │ ├── force_default_constructor.py
│ │ └── layer_decay_optimizer_constructor.py
│ └── schedulers
│ │ ├── __init__.py
│ │ └── poly_ratio_scheduler.py
├── evaluation
│ ├── __init__.py
│ └── metrics
│ │ ├── __init__.py
│ │ ├── citys_metric.py
│ │ ├── depth_metric.py
│ │ └── iou_metric.py
├── models
│ ├── __init__.py
│ ├── assigners
│ │ ├── __init__.py
│ │ ├── base_assigner.py
│ │ ├── hungarian_assigner.py
│ │ └── match_cost.py
│ ├── backbones
│ │ ├── Samba.py
│ │ ├── __init__.py
│ │ ├── beit.py
│ │ ├── bisenetv1.py
│ │ ├── bisenetv2.py
│ │ ├── cgnet.py
│ │ ├── ddrnet.py
│ │ ├── erfnet.py
│ │ ├── fast_scnn.py
│ │ ├── hrnet.py
│ │ ├── icnet.py
│ │ ├── mae.py
│ │ ├── mit.py
│ │ ├── mobilenet_v2.py
│ │ ├── mobilenet_v3.py
│ │ ├── mscan.py
│ │ ├── pidnet.py
│ │ ├── resnest.py
│ │ ├── resnet.py
│ │ ├── resnext.py
│ │ ├── stdc.py
│ │ ├── swin.py
│ │ ├── timm_backbone.py
│ │ ├── twins.py
│ │ ├── unet.py
│ │ ├── vit.py
│ │ └── vpd.py
│ ├── builder.py
│ ├── data_preprocessor.py
│ ├── decode_heads
│ │ ├── __init__.py
│ │ ├── ann_head.py
│ │ ├── apc_head.py
│ │ ├── aspp_head.py
│ │ ├── cascade_decode_head.py
│ │ ├── cc_head.py
│ │ ├── da_head.py
│ │ ├── ddr_head.py
│ │ ├── decode_head.py
│ │ ├── dm_head.py
│ │ ├── dnl_head.py
│ │ ├── dpt_head.py
│ │ ├── ema_head.py
│ │ ├── enc_head.py
│ │ ├── fcn_head.py
│ │ ├── fpn_head.py
│ │ ├── gc_head.py
│ │ ├── ham_head.py
│ │ ├── isa_head.py
│ │ ├── knet_head.py
│ │ ├── lraspp_head.py
│ │ ├── mask2former_head.py
│ │ ├── maskformer_head.py
│ │ ├── nl_head.py
│ │ ├── ocr_head.py
│ │ ├── pid_head.py
│ │ ├── point_head.py
│ │ ├── psa_head.py
│ │ ├── psp_head.py
│ │ ├── san_head.py
│ │ ├── segformer_head.py
│ │ ├── segmenter_mask_head.py
│ │ ├── sep_aspp_head.py
│ │ ├── sep_fcn_head.py
│ │ ├── setr_mla_head.py
│ │ ├── setr_up_head.py
│ │ ├── stdc_head.py
│ │ ├── uper_head.py
│ │ └── vpd_depth_head.py
│ ├── losses
│ │ ├── __init__.py
│ │ ├── accuracy.py
│ │ ├── boundary_loss.py
│ │ ├── cross_entropy_loss.py
│ │ ├── dice_loss.py
│ │ ├── focal_loss.py
│ │ ├── huasdorff_distance_loss.py
│ │ ├── kldiv_loss.py
│ │ ├── lovasz_loss.py
│ │ ├── ohem_cross_entropy_loss.py
│ │ ├── silog_loss.py
│ │ ├── tversky_loss.py
│ │ └── utils.py
│ ├── necks
│ │ ├── __init__.py
│ │ ├── featurepyramid.py
│ │ ├── fpn.py
│ │ ├── ic_neck.py
│ │ ├── jpu.py
│ │ ├── mla_neck.py
│ │ └── multilevel_neck.py
│ ├── segmentors
│ │ ├── __init__.py
│ │ ├── base.py
│ │ ├── cascade_encoder_decoder.py
│ │ ├── depth_estimator.py
│ │ ├── encoder_decoder.py
│ │ ├── multimodal_encoder_decoder.py
│ │ └── seg_tta.py
│ ├── text_encoder
│ │ ├── __init__.py
│ │ └── clip_text_encoder.py
│ └── utils
│ │ ├── __init__.py
│ │ ├── basic_block.py
│ │ ├── embed.py
│ │ ├── encoding.py
│ │ ├── inverted_residual.py
│ │ ├── make_divisible.py
│ │ ├── point_sample.py
│ │ ├── ppm.py
│ │ ├── res_layer.py
│ │ ├── san_layers.py
│ │ ├── se_layer.py
│ │ ├── self_attention_block.py
│ │ ├── shape_convert.py
│ │ ├── up_conv_block.py
│ │ └── wrappers.py
├── registry
│ ├── __init__.py
│ └── registry.py
├── structures
│ ├── __init__.py
│ ├── sampler
│ │ ├── __init__.py
│ │ ├── base_pixel_sampler.py
│ │ ├── builder.py
│ │ └── ohem_pixel_sampler.py
│ └── seg_data_sample.py
├── utils
│ ├── __init__.py
│ ├── bpe_simple_vocab_16e6.txt.gz
│ ├── class_names.py
│ ├── collect_env.py
│ ├── get_templates.py
│ ├── io.py
│ ├── mask_classification.py
│ ├── misc.py
│ ├── set_env.py
│ ├── tokenizer.py
│ └── typing_utils.py
├── version.py
└── visualization
│ ├── __init__.py
│ └── local_visualizer.py
├── model-index.yml
├── projects
├── Adabins
│ ├── README.md
│ ├── backbones
│ │ ├── __init__.py
│ │ └── adabins_backbone.py
│ ├── configs
│ │ ├── _base_
│ │ │ ├── datasets
│ │ │ │ └── nyu.py
│ │ │ ├── default_runtime.py
│ │ │ └── models
│ │ │ │ └── Adabins.py
│ │ └── adabins
│ │ │ ├── adabins_efficient_b5_4x16_25e_NYU_416x544.py
│ │ │ └── adabins_efficient_b5_4x16_25e_kitti_352x704.py
│ └── decode_head
│ │ ├── __init__.py
│ │ └── adabins_head.py
├── CAT-Seg
│ ├── README.md
│ ├── cat_seg
│ │ ├── __init__.py
│ │ ├── models
│ │ │ ├── __init__.py
│ │ │ ├── cat_aggregator.py
│ │ │ ├── cat_head.py
│ │ │ └── clip_ovseg.py
│ │ └── utils
│ │ │ ├── __init__.py
│ │ │ ├── bpe_vocab
│ │ │ └── bpe_simple_vocab_16e6.txt.gz
│ │ │ ├── clip_model.py
│ │ │ ├── clip_templates.py
│ │ │ ├── clip_wrapper.py
│ │ │ ├── self_attention_block.py
│ │ │ └── tokenizer.py
│ ├── configs
│ │ ├── _base_
│ │ │ ├── datasets
│ │ │ │ ├── ade20k_384x384.py
│ │ │ │ ├── coco-stuff164k_384x384.py
│ │ │ │ └── pascal_context_59_384x384.py
│ │ │ ├── default_runtime.py
│ │ │ └── schedules
│ │ │ │ └── schedule_80k.py
│ │ └── cat_seg
│ │ │ ├── catseg_vitb-r101_4xb1-warmcoslr2e-4-adamw-80k_ade20k-384x384.py
│ │ │ ├── catseg_vitb-r101_4xb1-warmcoslr2e-4-adamw-80k_pascal-context-59-384x384.py
│ │ │ ├── catseg_vitb-r101_4xb2-warmcoslr2e-4-adamw-80k_coco-stuff164k-384x384.py
│ │ │ ├── catseg_vitg-swin-b_4xb1-warmcoslr2e-4_adamw-80k_coco-stuff164k_384x384.py
│ │ │ ├── catseg_vith-swin-b_4xb1-warmcoslr2e-4_adamw-80k_coco-stuff164k_384x384.py
│ │ │ └── catseg_vitl-swin-b_4xb1-warmcoslr2e-4_adamw-80k_coco-stuff164k_384x384.py
│ └── utils
│ │ └── __init__.py
├── README.md
├── XDecoder
│ └── README.md
├── bdd100k_dataset
│ ├── README.md
│ ├── configs
│ │ ├── _base_
│ │ │ └── datasets
│ │ │ │ └── bdd100k.py
│ │ └── pspnet_r50-d8_4xb2-80k_bdd100k-512x1024.py
│ ├── docs
│ │ ├── en
│ │ │ └── user_guides
│ │ │ │ └── 2_dataset_prepare.md
│ │ └── zh_cn
│ │ │ └── user_guides
│ │ │ └── 2_dataset_prepare.md
│ └── mmseg
│ │ └── datasets
│ │ └── bdd100k.py
├── example_project
│ ├── README.md
│ ├── configs
│ │ └── fcn_dummy-r50-d8_4xb2-40k_cityscapes-512x1024.py
│ └── dummy
│ │ ├── __init__.py
│ │ └── dummy_resnet.py
├── faq.md
├── gid_dataset
│ ├── configs
│ │ ├── _base_
│ │ │ └── datasets
│ │ │ │ └── gid.py
│ │ └── deeplabv3plus_r101-d8_4xb2-240k_gid-256x256.py
│ ├── mmseg
│ │ └── datasets
│ │ │ └── gid.py
│ ├── tools
│ │ └── dataset_converters
│ │ │ ├── gid.py
│ │ │ └── gid_select15imgFromAll.py
│ └── user_guides
│ │ └── 2_dataset_prepare.md
├── hsidrive20_dataset
│ ├── README.md
│ ├── configs
│ │ ├── _base_
│ │ │ └── datasets
│ │ │ │ └── hsi_drive.py
│ │ └── unet-s5-d16_fcn_4xb4-160k_hsidrive-192x384.py
│ ├── docs
│ │ ├── en
│ │ │ └── user_guides
│ │ │ │ └── 2_dataset_prepare.md
│ │ └── zh_cn
│ │ │ └── user_guides
│ │ │ └── 2_dataset_prepare.md
│ └── mmseg
│ │ └── datasets
│ │ └── hsi_drive.py
├── hssn
│ ├── README.md
│ ├── configs
│ │ ├── _base_
│ │ │ ├── datasets
│ │ │ │ └── cityscapes.py
│ │ │ ├── default_runtime.py
│ │ │ ├── models
│ │ │ │ └── deeplabv3plus_r50-d8_vd_contrast.py
│ │ │ └── schedules
│ │ │ │ └── schedule_80k.py
│ │ └── hssn
│ │ │ └── hieraseg_deeplabv3plus_r101-d8_4xb2-80l_cityscapes-512x1024.py
│ ├── decode_head
│ │ ├── __init__.py
│ │ └── sep_aspp_contrast_head.py
│ └── losses
│ │ ├── __init__.py
│ │ ├── hiera_triplet_loss_cityscape.py
│ │ └── tree_triplet_loss.py
├── isnet
│ ├── README.md
│ ├── configs
│ │ └── isnet_r50-d8_8xb2-160k_cityscapes-512x1024.py
│ └── decode_heads
│ │ ├── __init__.py
│ │ └── isnet_head.py
├── mapillary_dataset
│ ├── README.md
│ ├── configs
│ │ ├── _base_
│ │ │ └── datasets
│ │ │ │ ├── mapillary_v1.py
│ │ │ │ ├── mapillary_v1_65.py
│ │ │ │ └── mapillary_v2.py
│ │ ├── deeplabv3plus_r101-d8_4xb2-240k_mapillay_v1-512x1024.py
│ │ ├── deeplabv3plus_r101-d8_4xb2-240k_mapillay_v2-512x1024.py
│ │ ├── pspnet_r101-d8_4xb2-240k_mapillay_v1-512x1024.py
│ │ └── pspnet_r101-d8_4xb2-240k_mapillay_v2-512x1024.py
│ ├── docs
│ │ └── en
│ │ │ └── user_guides
│ │ │ └── 2_dataset_prepare.md
│ └── mmseg
│ │ └── datasets
│ │ └── mapillary.py
├── medical
│ └── 2d_image
│ │ ├── ct
│ │ └── cranium
│ │ │ ├── README.md
│ │ │ ├── configs
│ │ │ ├── cranium_512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet-{use-sigmoid}_1xb16-0.01-20k_cranium-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_cranium-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_cranium-512x512.py
│ │ │ └── fcn-unet-s5-d16_unet_1xb16-0.01-20k_cranium-512x512.py
│ │ │ ├── datasets
│ │ │ └── cranium_dataset.py
│ │ │ └── tools
│ │ │ └── prepare_dataset.py
│ │ ├── dermoscopy
│ │ ├── isic2016_task1
│ │ │ ├── README.md
│ │ │ ├── configs
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_isic2016-task1-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_isic2016-task1-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.01-20k_isic2016-task1-512x512.py
│ │ │ │ └── isic2016-task1_512x512.py
│ │ │ ├── datasets
│ │ │ │ └── isic2016-task1_dataset.py
│ │ │ └── tools
│ │ │ │ └── prepare_dataset.py
│ │ └── isic2017_task1
│ │ │ ├── README.md
│ │ │ ├── configs
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_isic2017-task1-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_isic2017-task1-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.01-20k_isic2017-task1-512x512.py
│ │ │ └── isic2017-task1_512x512.py
│ │ │ ├── datasets
│ │ │ └── isic2017-task1_dataset.py
│ │ │ └── tools
│ │ │ └── prepare_dataset.py
│ │ ├── endoscopy
│ │ ├── kvasir_seg
│ │ │ ├── README.md
│ │ │ ├── configs
│ │ │ │ ├── fcn-unet-s5-d16_unet-{use-sigmoid}_1xb16-0.01-20k_kvasir-seg-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_kvasir-seg-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_kvasir-seg-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.01-20k_kvasir-seg-512x512.py
│ │ │ │ └── kvasir-seg_512x512.py
│ │ │ ├── datasets
│ │ │ │ └── kvasir-seg_dataset.py
│ │ │ └── tools
│ │ │ │ └── prepare_dataset.py
│ │ └── kvasir_seg_aliyun
│ │ │ ├── README.md
│ │ │ ├── configs
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_kvasir-seg-aliyun-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_kvasir-seg-aliyun-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.01-20k_kvasir-seg-aliyun-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.01lr-sigmoid-20k_kvasir-seg-aliyun-512x512.py
│ │ │ └── kvasir-seg-aliyun_512x512.py
│ │ │ ├── datasets
│ │ │ └── kvasir-seg-aliyun_dataset.py
│ │ │ └── tools
│ │ │ └── prepare_dataset.py
│ │ ├── fluorescein_angriogram
│ │ └── vampire
│ │ │ ├── README.md
│ │ │ ├── configs
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_vampire-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_vampire-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.01-20k_vampire-512x512.py
│ │ │ └── vampire_512x512.py
│ │ │ ├── datasets
│ │ │ ├── __init__.py
│ │ │ └── vampire_dataset.py
│ │ │ └── tools
│ │ │ └── prepare_dataset.py
│ │ ├── fundus_photography
│ │ ├── dr_hagis
│ │ │ ├── README.md
│ │ │ ├── configs
│ │ │ │ ├── dr-hagis_512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_dr-hagis-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_dr-hagis-512x512.py
│ │ │ │ └── fcn-unet-s5-d16_unet_1xb16-0.01-20k_dr-hagis-512x512.py
│ │ │ ├── datasets
│ │ │ │ └── dr-hagis_dataset.py
│ │ │ └── tools
│ │ │ │ └── prepare_dataset.py
│ │ ├── gamma3
│ │ │ ├── README.md
│ │ │ ├── configs
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_gamma3-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_gamma3-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.01-20k_gamma3-512x512.py
│ │ │ │ └── gamma3_512x512.py
│ │ │ ├── datasets
│ │ │ │ └── gamma3_dataset.py
│ │ │ └── tools
│ │ │ │ └── prepare_dataset.py
│ │ ├── orvs
│ │ │ ├── README.md
│ │ │ ├── configs
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_orvs-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_orvs-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.01-20k_orvs-512x512.py
│ │ │ │ └── orvs_512x512.py
│ │ │ ├── datasets
│ │ │ │ └── orvs_dataset.py
│ │ │ └── tools
│ │ │ │ └── prepare_dataset.py
│ │ └── rite
│ │ │ ├── README.md
│ │ │ ├── configs
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_rite-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_rite-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.01-20k_rite-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.01lr-sigmoid-20k_rite-512x512.py
│ │ │ └── rite_512x512.py
│ │ │ ├── datasets
│ │ │ └── rite_dataset.py
│ │ │ └── tools
│ │ │ └── prepare_dataset.py
│ │ ├── histopathology
│ │ ├── breastCancerCellSegmentation
│ │ │ ├── README.md
│ │ │ ├── configs
│ │ │ │ ├── breastCancerCellSegmentation_512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_breastCancerCellSegmentation-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_breastCancerCellSegmentation-512x512.py
│ │ │ │ └── fcn-unet-s5-d16_unet_1xb16-0.01-20k_breastCancerCellSegmentation-512x512.py
│ │ │ ├── datasets
│ │ │ │ └── breastCancerCellSegmentation_dataset.py
│ │ │ └── tools
│ │ │ │ └── prepare_dataset.py
│ │ ├── breast_cancer_cell_seg
│ │ │ ├── README.md
│ │ │ ├── configs
│ │ │ │ ├── breast-cancer-cell-seg_512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_breast-cancer-cell-seg-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_breast-cancer-cell-seg-512x512.py
│ │ │ │ └── fcn-unet-s5-d16_unet_1xb16-0.01-20k_breast-cancer-cell-seg-512x512.py
│ │ │ ├── datasets
│ │ │ │ └── breast-cancer-cell-seg_dataset.py
│ │ │ └── tools
│ │ │ │ └── prepare_dataset.py
│ │ ├── conic2022_seg
│ │ │ ├── README.md
│ │ │ ├── configs
│ │ │ │ ├── conic2022-seg_512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_conic2022-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_conic2022-512x512.py
│ │ │ │ └── fcn-unet-s5-d16_unet_1xb16-0.01-20k_conic2022-512x512.py
│ │ │ ├── conic2022_seg_dataset.png
│ │ │ ├── datasets
│ │ │ │ └── conic2022-seg_dataset.py
│ │ │ └── tools
│ │ │ │ └── prepare_dataset.py
│ │ ├── consep
│ │ │ ├── README.md
│ │ │ ├── configs
│ │ │ │ ├── consep_512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_consep-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_consep-512x512.py
│ │ │ │ └── fcn-unet-s5-d16_unet_1xb16-0.01-20k_consep-512x512.py
│ │ │ ├── datasets
│ │ │ │ └── consep_dataset.py
│ │ │ └── tools
│ │ │ │ └── prepare_dataset.py
│ │ ├── fusc2021
│ │ │ ├── README.md
│ │ │ ├── configs
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_fusc2021-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_fusc2021-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.01-20k_fusc2021-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.01lr-sigmoid-20k_fusc2021-512x512.py
│ │ │ │ └── fusc2021_512x512.py
│ │ │ ├── datasets
│ │ │ │ └── fusc2021_dataset.py
│ │ │ └── tools
│ │ │ │ └── prepare_dataset.py
│ │ ├── pannuke
│ │ │ ├── README.md
│ │ │ ├── configs
│ │ │ │ ├── fcn-unet-s5-d16_unet-{use-sigmoid}_1xb16-0.01-20k_bactteria-detection-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_pannuke-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_pannuke-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.01-20k_pannuke-512x512.py
│ │ │ │ └── pannuke_512x512.py
│ │ │ ├── datasets
│ │ │ │ └── pannuke_dataset.py
│ │ │ └── tools
│ │ │ │ └── prepare_dataset.py
│ │ └── pcam
│ │ │ ├── README.md
│ │ │ ├── configs
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_pcam-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_pcam-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.01-20k_pcam-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.01lr-sigmoid-20k_pcam-512x512.py
│ │ │ └── pcam_512x512.py
│ │ │ ├── datasets
│ │ │ └── pcam_dataset.py
│ │ │ └── tools
│ │ │ └── prepare_dataset.py
│ │ ├── infrared_reflectance_imaging
│ │ └── ravir
│ │ │ ├── README.md
│ │ │ ├── configs
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_ravir-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_ravir-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.01-20k_ravir-512x512.py
│ │ │ └── ravir_512x512.py
│ │ │ ├── datasets
│ │ │ ├── __init__.py
│ │ │ └── ravir_dataset.py
│ │ │ └── tools
│ │ │ └── prepare_dataset.py
│ │ ├── microscopy_images
│ │ ├── 2pm_vessel
│ │ │ ├── README.md
│ │ │ ├── configs
│ │ │ │ ├── 2pm-vessel_512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_2pm-vessel-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_2pm-vessel-512x512.py
│ │ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.01-20k_2pm-vessel-512x512.py
│ │ │ │ └── fcn-unet-s5-d16_unet_1xb16-0.01lr-sigmoid-20k_bactteria-detection-512x512.py
│ │ │ ├── datasets
│ │ │ │ └── 2pm-vessel_dataset.py
│ │ │ └── tools
│ │ │ │ └── prepare_dataset.py
│ │ └── bactteria_detection
│ │ │ ├── README.md
│ │ │ ├── configs
│ │ │ ├── bactteria-detection_512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_bactteria-detection-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_bactteria-detection-512x512.py
│ │ │ └── fcn-unet-s5-d16_unet_1xb16-0.01-20k_bactteria-detection-512x512.py
│ │ │ ├── datasets
│ │ │ └── bactteria-detection_dataset.py
│ │ │ └── tools
│ │ │ └── prepare_dataset.py
│ │ ├── tools
│ │ └── split_seg_dataset.py
│ │ └── x_ray
│ │ ├── chest_image_pneum
│ │ ├── README.md
│ │ ├── configs
│ │ │ ├── chest-image-pneum_512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_chest-image-pneum-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_chest-image-pneum-512x512.py
│ │ │ └── fcn-unet-s5-d16_unet_1xb16-0.01-20k_chest-image-pneum-512x512.py
│ │ ├── datasets
│ │ │ └── chest-image-pneum_dataset.py
│ │ └── tools
│ │ │ └── prepare_dataset.py
│ │ ├── chest_x_ray_images_with_pneumothorax_masks
│ │ ├── README.md
│ │ ├── configs
│ │ │ ├── chest-x-ray-images-with-pneumothorax-masks_512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet-{use-sigmoid}_1xb16-0.01-20k_chest-x-ray-images-with-pneumothorax-masks-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_chest-x-ray-images-with-pneumothorax-masks-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_chest-x-ray-images-with-pneumothorax-masks-512x512.py
│ │ │ └── fcn-unet-s5-d16_unet_1xb16-0.01-20k_chest-x-ray-images-with-pneumothorax-masks-512x512.py
│ │ ├── datasets
│ │ │ └── chest-x-ray-images-with-pneumothorax-masks_dataset.py
│ │ └── tools
│ │ │ └── prepare_dataset.py
│ │ ├── covid_19_ct_cxr
│ │ ├── README.md
│ │ ├── configs
│ │ │ ├── covid-19-ct-cxr_512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet-{use-sigmoid}_1xb16-0.01-20k_covid-19-ct-cxr-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_covid-19-ct-cxr-512x512.py
│ │ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_covid-19-ct-cxr-512x512.py
│ │ │ └── fcn-unet-s5-d16_unet_1xb16-0.01-20k_covid-19-ct-cxr-512x512.py
│ │ ├── datasets
│ │ │ └── covid-19-ct-cxr_dataset.py
│ │ └── tools
│ │ │ └── prepare_dataset.py
│ │ └── crass
│ │ ├── README.md
│ │ ├── configs
│ │ ├── crass_512x512.py
│ │ ├── fcn-unet-s5-d16_unet_1xb16-0.0001-20k_crass-512x512.py
│ │ ├── fcn-unet-s5-d16_unet_1xb16-0.001-20k_crass-512x512.py
│ │ ├── fcn-unet-s5-d16_unet_1xb16-0.01-20k_crass-512x512.py
│ │ └── fcn-unet-s5-d16_unet_1xb16-lr0.01-sigmoid-20k_crass-512x512.py
│ │ ├── datasets
│ │ └── crass_dataset.py
│ │ └── tools
│ │ └── prepare_dataset.py
├── nvidia_jetson
│ └── README.md
├── pp_mobileseg
│ ├── README.md
│ ├── backbones
│ │ ├── __init__.py
│ │ └── strideformer.py
│ ├── configs
│ │ ├── _base_
│ │ │ ├── datasets
│ │ │ │ └── ade20k.py
│ │ │ ├── default_runtime.py
│ │ │ ├── models
│ │ │ │ └── pp_mobile.py
│ │ │ └── schedules
│ │ │ │ └── schedule_80k.py
│ │ └── pp_mobileseg
│ │ │ ├── pp_mobileseg_mobilenetv3_2x16_80k_ade20k_512x512_base.py
│ │ │ └── pp_mobileseg_mobilenetv3_2x16_80k_ade20k_512x512_tiny.py
│ ├── decode_head
│ │ ├── __init__.py
│ │ └── pp_mobileseg_head.py
│ └── inference_onnx.py
├── sam_inference_demo
│ ├── README.md
│ ├── sam
│ │ ├── __init__.py
│ │ ├── modeling
│ │ │ ├── __init__.py
│ │ │ ├── common.py
│ │ │ ├── mask_decoder.py
│ │ │ ├── prompt_encoder.py
│ │ │ ├── sam.py
│ │ │ └── transformer.py
│ │ ├── sam_inferencer.py
│ │ └── utils
│ │ │ ├── __init__.py
│ │ │ ├── amg.py
│ │ │ └── transforms.py
│ └── sam_image_demo.ipynb
└── van
│ ├── README.md
│ ├── backbones
│ ├── __init__.py
│ └── van.py
│ └── configs
│ ├── _base_
│ ├── datasets
│ │ └── ade20k.py
│ └── models
│ │ ├── van_fpn.py
│ │ └── van_upernet.py
│ └── van
│ ├── van-b0_fpn_8xb4-40k_ade20k-512x512.py
│ ├── van-b1_fpn_8xb4-40k_ade20k-512x512.py
│ ├── van-b2_fpn_8xb4-40k_ade20k-512x512.py
│ ├── van-b2_upernet_4xb2-160k_ade20k-512x512.py
│ ├── van-b3_fpn_8xb4-40k_ade20k-512x512.py
│ ├── van-b3_upernet_4xb2-160k_ade20k-512x512.py
│ ├── van-b4-in22kpre_upernet_4xb4-160k_ade20k-512x512.py
│ ├── van-b4_upernet_4xb4-160k_ade20k-512x512.py
│ ├── van-b5-in22kpre_upernet_4xb2-160k_ade20k-512x512.py
│ └── van-b6-in22kpre_upernet_4xb2-160k_ade20k-512x512.py
├── requirements.txt
├── requirements
├── albu.txt
├── docs.txt
├── mminstall.txt
├── multimodal.txt
├── optional.txt
├── readthedocs.txt
├── runtime.txt
└── tests.txt
├── resources
├── 3dogs.jpg
├── 3dogs_mask.png
├── cascade_encoder_decoder_dataflow.png
├── encoder_decoder_dataflow.png
├── miaomiao_qrcode.jpg
├── mmseg-logo.png
├── seg_demo.gif
├── test_step.png
└── train_step.png
├── setup.cfg
├── setup.py
├── tests
├── __init__.py
├── test_apis
│ ├── test_inferencer.py
│ ├── test_rs_inferencer.py
│ └── utils.py
├── test_config.py
├── test_datasets
│ ├── test_dataset.py
│ ├── test_dataset_builder.py
│ ├── test_formatting.py
│ ├── test_loading.py
│ ├── test_transform.py
│ └── test_tta.py
├── test_digit_version.py
├── test_engine
│ ├── test_layer_decay_optimizer_constructor.py
│ ├── test_optimizer.py
│ └── test_visualization_hook.py
├── test_evaluation
│ └── test_metrics
│ │ ├── test_citys_metric.py
│ │ ├── test_depth_metric.py
│ │ └── test_iou_metric.py
├── test_models
│ ├── __init__.py
│ ├── test_assigners
│ │ └── test_hungarian_assigner.py
│ ├── test_backbones
│ │ ├── __init__.py
│ │ ├── test_beit.py
│ │ ├── test_bisenetv1.py
│ │ ├── test_bisenetv2.py
│ │ ├── test_blocks.py
│ │ ├── test_cgnet.py
│ │ ├── test_clip_text_encoder.py
│ │ ├── test_erfnet.py
│ │ ├── test_fast_scnn.py
│ │ ├── test_hrnet.py
│ │ ├── test_icnet.py
│ │ ├── test_mae.py
│ │ ├── test_mit.py
│ │ ├── test_mobilenet_v3.py
│ │ ├── test_mscan.py
│ │ ├── test_pidnet.py
│ │ ├── test_resnest.py
│ │ ├── test_resnet.py
│ │ ├── test_resnext.py
│ │ ├── test_stdc.py
│ │ ├── test_swin.py
│ │ ├── test_timm_backbone.py
│ │ ├── test_twins.py
│ │ ├── test_unet.py
│ │ ├── test_vit.py
│ │ ├── test_vpd.py
│ │ └── utils.py
│ ├── test_data_preprocessor.py
│ ├── test_forward.py
│ ├── test_heads
│ │ ├── __init__.py
│ │ ├── test_ann_head.py
│ │ ├── test_apc_head.py
│ │ ├── test_aspp_head.py
│ │ ├── test_cc_head.py
│ │ ├── test_decode_head.py
│ │ ├── test_dm_head.py
│ │ ├── test_dnl_head.py
│ │ ├── test_dpt_head.py
│ │ ├── test_ema_head.py
│ │ ├── test_fcn_head.py
│ │ ├── test_gc_head.py
│ │ ├── test_ham_head.py
│ │ ├── test_isa_head.py
│ │ ├── test_lraspp_head.py
│ │ ├── test_mask2former_head.py
│ │ ├── test_maskformer_head.py
│ │ ├── test_nl_head.py
│ │ ├── test_ocr_head.py
│ │ ├── test_pidnet_head.py
│ │ ├── test_psa_head.py
│ │ ├── test_psp_head.py
│ │ ├── test_san_head.py
│ │ ├── test_segformer_head.py
│ │ ├── test_segmenter_mask_head.py
│ │ ├── test_setr_mla_head.py
│ │ ├── test_setr_up_head.py
│ │ ├── test_uper_head.py
│ │ ├── test_vpd_depth_head.py
│ │ └── utils.py
│ ├── test_losses
│ │ ├── test_cross_entropy_loss.py
│ │ ├── test_dice_loss.py
│ │ ├── test_huasdorff_distance_loss.py
│ │ ├── test_kldiv_loss.py
│ │ ├── test_silog_loss.py
│ │ └── test_tversky_loss.py
│ ├── test_necks
│ │ ├── __init__.py
│ │ ├── test_feature2pyramid.py
│ │ ├── test_fpn.py
│ │ ├── test_ic_neck.py
│ │ ├── test_jpu.py
│ │ ├── test_mla_neck.py
│ │ └── test_multilevel_neck.py
│ ├── test_segmentors
│ │ ├── __init__.py
│ │ ├── test_cascade_encoder_decoder.py
│ │ ├── test_depth_estimator.py
│ │ ├── test_encoder_decoder.py
│ │ ├── test_multimodal_encoder_decoder.py
│ │ ├── test_seg_tta_model.py
│ │ └── utils.py
│ └── test_utils
│ │ ├── __init__.py
│ │ ├── test_embed.py
│ │ └── test_shape_convert.py
├── test_sampler.py
├── test_structures
│ └── test_seg_data_sample.py
├── test_utils
│ ├── test_io.py
│ └── test_set_env.py
└── test_visualization
│ └── test_local_visualizer.py
└── tools
├── analysis_tools
├── analyze_logs.py
├── benchmark.py
├── browse_dataset.py
├── confusion_matrix.py
├── get_flops.py
└── visualization_cam.py
├── dataset_converters
├── chase_db1.py
├── cityscapes.py
├── coco_stuff10k.py
├── coco_stuff164k.py
├── drive.py
├── hrf.py
├── isaid.py
├── levircd.py
├── loveda.py
├── nyu.py
├── pascal_context.py
├── potsdam.py
├── refuge.py
├── stare.py
├── synapse.py
├── vaihingen.py
└── voc_aug.py
├── deployment
└── pytorch2torchscript.py
├── dist_test.sh
├── dist_train.sh
├── misc
├── browse_dataset.py
├── print_config.py
└── publish_model.py
├── model_converters
├── beit2mmseg.py
├── clip2mmseg.py
├── mit2mmseg.py
├── san2mmseg.py
├── stdc2mmseg.py
├── swin2mmseg.py
├── twins2mmseg.py
├── vit2mmseg.py
└── vitjax2mmseg.py
├── slurm_test.sh
├── slurm_train.sh
├── test.py
├── torchserve
├── mmseg2torchserve.py
├── mmseg_handler.py
└── test_torchserve.py
└── train.py
/.dev_scripts/benchmark_options.py:
--------------------------------------------------------------------------------
1 | third_part_libs = [
2 | 'pip install mmengine',
3 | 'pip install mmcv>=2.0.0',
4 | 'pip install mmcls==1.0.0rc6',
5 | 'pip install mmdet==3.0.0',
6 | 'pip install -r requirements.txt',
7 | 'pip install timm',
8 | ]
9 |
10 | default_floating_range = 0.5
11 |
--------------------------------------------------------------------------------
/.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 answered.
7 |
--------------------------------------------------------------------------------
/.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 |
--------------------------------------------------------------------------------
/.owners.yml:
--------------------------------------------------------------------------------
1 | assign:
2 | strategy:
3 | # random
4 | # round-robin
5 | daily-shift-based
6 | assignees:
7 | - xiexinch
8 |
--------------------------------------------------------------------------------
/CITATION.cff:
--------------------------------------------------------------------------------
1 | cff-version: 1.2.0
2 | message: "If you use this software, please cite it as below."
3 | authors:
4 | - name: "MMSegmentation Contributors"
5 | title: "OpenMMLab Semantic Segmentation Toolbox and Benchmark"
6 | date-released: 2020-07-10
7 | url: "https://github.com/open-mmlab/mmsegmentation"
8 | license: Apache-2.0
9 |
--------------------------------------------------------------------------------
/MANIFEST.in:
--------------------------------------------------------------------------------
1 | include requirements/*.txt
2 | include mmseg/.mim/model-index.yml
3 | include mmseg/utils/bpe_simple_vocab_16e6.txt.gz
4 | recursive-include mmseg/.mim/configs *.py *.yaml
5 | recursive-include mmseg/.mim/tools *.py *.sh
6 |
--------------------------------------------------------------------------------
/configs/ann/ann_r101-d8_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './ann_r50-d8_4xb2-40k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/ann/ann_r101-d8_4xb2-40k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './ann_r50-d8_4xb2-40k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/ann/ann_r101-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './ann_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/ann/ann_r101-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './ann_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/ann/ann_r101-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './ann_r50-d8_4xb4-160k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/ann/ann_r101-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './ann_r50-d8_4xb4-20k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/ann/ann_r101-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './ann_r50-d8_4xb4-20k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/ann/ann_r101-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './ann_r50-d8_4xb4-80k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/ann/ann_r50-d8_4xb2-40k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/ann/ann_r50-d8_4xb2-80k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/ann/ann_r50-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/ann_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/ann/ann_r50-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/ann_r50-d8.py', '../_base_/datasets/pascal_voc12_aug.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=21),
10 | auxiliary_head=dict(num_classes=21))
11 |
--------------------------------------------------------------------------------
/configs/ann/ann_r50-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/ann_r50-d8.py', '../_base_/datasets/pascal_voc12_aug.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=21),
10 | auxiliary_head=dict(num_classes=21))
11 |
--------------------------------------------------------------------------------
/configs/ann/ann_r50-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/ann_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/apcnet/apcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './apcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/apcnet/apcnet_r101-d8_4xb2-40k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './apcnet_r50-d8_4xb2-40k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/apcnet/apcnet_r101-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './apcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/apcnet/apcnet_r101-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './apcnet_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/apcnet/apcnet_r101-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './apcnet_r50-d8_4xb4-160k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/apcnet/apcnet_r101-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './apcnet_r50-d8_4xb4-80k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/apcnet/apcnet_r50-d8_4xb2-40k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/apcnet/apcnet_r50-d8_4xb2-80k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/apcnet/apcnet_r50-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/apcnet_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/apcnet/apcnet_r50-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/apcnet_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/bisenetv1/bisenetv1_r101-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './bisenetv1_r101-d32_4xb4-160k_coco-stuff164k-512x512.py'
2 | model = dict(
3 | backbone=dict(
4 | backbone_cfg=dict(
5 | init_cfg=dict(
6 | type='Pretrained', checkpoint='open-mmlab://resnet101_v1c'))))
7 |
--------------------------------------------------------------------------------
/configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './bisenetv1_r18-d32_4xb4-160k_coco-stuff164k-512x512.py'
2 | crop_size = (512, 512)
3 | data_preprocessor = dict(size=crop_size)
4 | model = dict(
5 | data_preprocessor=data_preprocessor,
6 | backbone=dict(
7 | backbone_cfg=dict(
8 | init_cfg=dict(
9 | type='Pretrained', checkpoint='open-mmlab://resnet18_v1c'))),
10 | )
11 |
--------------------------------------------------------------------------------
/configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb8-160k_cityscapes-1024x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './bisenetv1_r18-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py'
2 | train_dataloader = dict(batch_size=8, num_workers=4)
3 | val_dataloader = dict(batch_size=1, num_workers=4)
4 | test_dataloader = val_dataloader
5 |
--------------------------------------------------------------------------------
/configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './bisenetv1_r50-d32_4xb4-160k_cityscapes-1024x1024.py'
2 | model = dict(
3 | type='EncoderDecoder',
4 | backbone=dict(
5 | backbone_cfg=dict(
6 | init_cfg=dict(
7 | type='Pretrained', checkpoint='open-mmlab://resnet50_v1c'))))
8 |
--------------------------------------------------------------------------------
/configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './bisenetv1_r50-d32_4xb4-160k_coco-stuff164k-512x512.py'
2 |
3 | model = dict(
4 | backbone=dict(
5 | backbone_cfg=dict(
6 | init_cfg=dict(
7 | type='Pretrained', checkpoint='open-mmlab://resnet50_v1c'))))
8 |
--------------------------------------------------------------------------------
/configs/bisenetv2/bisenetv2_fcn_4xb4-amp-160k_cityscapes-1024x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './bisenetv2_fcn_4xb4-160k_cityscapes-1024x1024.py'
2 | optim_wrapper = dict(
3 | _delete_=True,
4 | type='AmpOptimWrapper',
5 | optimizer=dict(type='SGD', lr=0.05, momentum=0.9, weight_decay=0.0005),
6 | loss_scale=512.)
7 |
--------------------------------------------------------------------------------
/configs/ccnet/ccnet_r101-d8_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/ccnet/ccnet_r101-d8_4xb2-40k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './ccnet_r50-d8_4xb2-40k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/ccnet/ccnet_r101-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './ccnet_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/ccnet/ccnet_r101-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './ccnet_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/ccnet/ccnet_r101-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './ccnet_r50-d8_4xb4-160k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/ccnet/ccnet_r101-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './ccnet_r50-d8_4xb4-20k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/ccnet/ccnet_r101-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './ccnet_r50-d8_4xb4-40k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/ccnet/ccnet_r101-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './ccnet_r50-d8_4xb4-80k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/ccnet/ccnet_r50-d8_4xb2-80k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/ccnet/ccnet_r50-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/ccnet_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/ccnet/ccnet_r50-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/ccnet_r50-d8.py',
3 | '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_20k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=21),
11 | auxiliary_head=dict(num_classes=21))
12 |
--------------------------------------------------------------------------------
/configs/ccnet/ccnet_r50-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/ccnet_r50-d8.py',
3 | '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_40k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=21),
11 | auxiliary_head=dict(num_classes=21))
12 |
--------------------------------------------------------------------------------
/configs/ccnet/ccnet_r50-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/ccnet_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/danet/danet_r101-d8_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './danet_r50-d8_4xb2-40k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/danet/danet_r101-d8_4xb2-40k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './danet_r50-d8_4xb2-40k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/danet/danet_r101-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './danet_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/danet/danet_r101-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './danet_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/danet/danet_r101-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './danet_r50-d8_4xb4-160k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/danet/danet_r101-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './danet_r50-d8_4xb4-20k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/danet/danet_r101-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './danet_r50-d8_4xb4-40k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/danet/danet_r101-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './danet_r50-d8_4xb4-80k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/danet/danet_r50-d8_4xb2-40k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/danet/danet_r50-d8_4xb2-80k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/danet/danet_r50-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/danet_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/danet/danet_r50-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/danet_r50-d8.py',
3 | '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_20k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=21),
11 | auxiliary_head=dict(num_classes=21))
12 |
--------------------------------------------------------------------------------
/configs/danet/danet_r50-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/danet_r50-d8.py',
3 | '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_40k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=21),
11 | auxiliary_head=dict(num_classes=21))
12 |
--------------------------------------------------------------------------------
/configs/danet/danet_r50-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/danet_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r101-d8_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r101-d8_4xb2-40k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb2-40k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r101-d8_4xb2-40k_cityscapes-512x1024.py'
2 | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
3 | optim_wrapper = dict(
4 | _delete_=True,
5 | type='AmpOptimWrapper',
6 | optimizer=optimizer,
7 | loss_scale=512.)
8 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r101-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb4-160k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r101-d8_4xb4-160k_coco-stuff164k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb4-160k_coco-stuff164k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_coco-stuff10k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb4-20k_coco-stuff10k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb4-20k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r101-d8_4xb4-320k_coco-stuff164k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_coco-stuff10k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-480x480.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb4-40k_pascal-context-480x480.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb4-40k_pascal-context-59-480x480.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb4-40k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb4-80k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_coco-stuff164k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb4-80k_coco-stuff164k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_pascal-context-480x480.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb4-80k_pascal-context-480x480.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r101-d8_4xb4-80k_pascal-context-59-480x480.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb4-40k_pascal-context-59-480x480.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r101b-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='torchvision://resnet101',
4 | backbone=dict(type='ResNet', depth=101))
5 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r101b-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(
3 | pretrained='torchvision://resnet101',
4 | backbone=dict(type='ResNet', depth=101))
5 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnet18_v1c',
4 | backbone=dict(depth=18),
5 | decode_head=dict(
6 | in_channels=512,
7 | channels=128,
8 | ),
9 | auxiliary_head=dict(in_channels=256, channels=64))
10 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnet18_v1c',
4 | backbone=dict(depth=18),
5 | decode_head=dict(
6 | in_channels=512,
7 | channels=128,
8 | ),
9 | auxiliary_head=dict(in_channels=256, channels=64))
10 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='torchvision://resnet18',
4 | backbone=dict(type='ResNet', depth=18),
5 | decode_head=dict(
6 | in_channels=512,
7 | channels=128,
8 | ),
9 | auxiliary_head=dict(in_channels=256, channels=64))
10 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(
3 | pretrained='torchvision://resnet18',
4 | backbone=dict(type='ResNet', depth=18),
5 | decode_head=dict(
6 | in_channels=512,
7 | channels=128,
8 | ),
9 | auxiliary_head=dict(in_channels=256, channels=64))
10 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r50-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r50-d8_4xb4-20k_coco-stuff10k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/deeplabv3_r50-d8.py',
3 | '../_base_/datasets/coco-stuff10k.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_20k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=171),
11 | auxiliary_head=dict(num_classes=171))
12 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r50-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/deeplabv3_r50-d8.py',
3 | '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_20k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=21),
11 | auxiliary_head=dict(num_classes=21))
12 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/deeplabv3_r50-d8.py',
3 | '../_base_/datasets/coco-stuff10k.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_40k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=171),
11 | auxiliary_head=dict(num_classes=171))
12 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/deeplabv3_r50-d8.py',
3 | '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_40k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=21),
11 | auxiliary_head=dict(num_classes=21))
12 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r50-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r50b-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3/deeplabv3_r50b-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb2-40k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb2-40k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024.py'
2 | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
3 | optim_wrapper = dict(
4 | _delete_=True,
5 | type='AmpOptimWrapper',
6 | optimizer=optimizer,
7 | loss_scale=512.)
8 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb4-160k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb4-20k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_pascal-context-480x480.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb4-40k_pascal-context-480x480.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_pascal-context-59-480x480.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb4-40k_pascal-context-59-480x480.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb4-40k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb4-80k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_loveda-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb4-80k_loveda-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_pascal-context-480x480.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb4-80k_pascal-context-480x480.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_pascal-context-59-480x480.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb4-80k_pascal-context-59-480x480.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_potsdam-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb4-80k_potsdam-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r101-d8_4xb4-80k_vaihingen-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb4-80k_vaihingen-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='torchvision://resnet101',
4 | backbone=dict(type='ResNet', depth=101))
5 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r101b-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(
3 | pretrained='torchvision://resnet101',
4 | backbone=dict(type='ResNet', depth=101))
5 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnet18_v1c',
4 | backbone=dict(depth=18),
5 | decode_head=dict(
6 | c1_in_channels=64,
7 | c1_channels=12,
8 | in_channels=512,
9 | channels=128,
10 | ),
11 | auxiliary_head=dict(in_channels=256, channels=64))
12 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnet18_v1c',
4 | backbone=dict(depth=18),
5 | decode_head=dict(
6 | c1_in_channels=64,
7 | c1_channels=12,
8 | in_channels=512,
9 | channels=128,
10 | ),
11 | auxiliary_head=dict(in_channels=256, channels=64))
12 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_isaid-896x896.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb4-80k_isaid-896x896.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnet18_v1c',
4 | backbone=dict(depth=18),
5 | decode_head=dict(
6 | c1_in_channels=64,
7 | c1_channels=12,
8 | in_channels=512,
9 | channels=128,
10 | ),
11 | auxiliary_head=dict(in_channels=256, channels=64))
12 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_loveda-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb4-80k_loveda-512x512.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnet18_v1c',
4 | backbone=dict(depth=18),
5 | decode_head=dict(
6 | c1_in_channels=64,
7 | c1_channels=12,
8 | in_channels=512,
9 | channels=128,
10 | ),
11 | auxiliary_head=dict(in_channels=256, channels=64))
12 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_potsdam-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb4-80k_potsdam-512x512.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnet18_v1c',
4 | backbone=dict(depth=18),
5 | decode_head=dict(
6 | c1_in_channels=64,
7 | c1_channels=12,
8 | in_channels=512,
9 | channels=128,
10 | ),
11 | auxiliary_head=dict(in_channels=256, channels=64))
12 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb4-80k_vaihingen-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb4-80k_vaihingen-512x512.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnet18_v1c',
4 | backbone=dict(depth=18),
5 | decode_head=dict(
6 | c1_in_channels=64,
7 | c1_channels=12,
8 | in_channels=512,
9 | channels=128,
10 | ),
11 | auxiliary_head=dict(in_channels=256, channels=64))
12 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='torchvision://resnet18',
4 | backbone=dict(type='ResNet', depth=18),
5 | decode_head=dict(
6 | c1_in_channels=64,
7 | c1_channels=12,
8 | in_channels=512,
9 | channels=128,
10 | ),
11 | auxiliary_head=dict(in_channels=256, channels=64))
12 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb2-40k_cityscapes-769x769.py'
2 | model = dict(
3 | pretrained='torchvision://resnet18',
4 | backbone=dict(type='ResNet', depth=18),
5 | decode_head=dict(
6 | c1_in_channels=64,
7 | c1_channels=12,
8 | in_channels=512,
9 | channels=128,
10 | ),
11 | auxiliary_head=dict(in_channels=256, channels=64))
12 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_cityscapes-512x1024.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 | crop_size = (512, 1024)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(data_preprocessor=data_preprocessor)
9 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.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 | crop_size = (512, 1024)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(data_preprocessor=data_preprocessor)
9 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_isaid-896x896.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/isaid.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (896, 896)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=16),
10 | auxiliary_head=dict(num_classes=16))
11 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_loveda-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/loveda.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=7),
10 | auxiliary_head=dict(num_classes=7))
11 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_potsdam-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/deeplabv3plus_r50-d8.py',
3 | '../_base_/datasets/potsdam.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_80k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=6),
11 | auxiliary_head=dict(num_classes=6))
12 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_vaihingen-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/deeplabv3plus_r50-d8.py',
3 | '../_base_/datasets/vaihingen.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_80k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=6),
11 | auxiliary_head=dict(num_classes=6))
12 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
3 |
--------------------------------------------------------------------------------
/configs/deeplabv3plus/deeplabv3plus_r50b-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './deeplabv3plus_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
3 |
--------------------------------------------------------------------------------
/configs/dmnet/dmnet_r101-d8_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './dmnet_r50-d8_4xb2-40k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/dmnet/dmnet_r101-d8_4xb2-40k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './dmnet_r50-d8_4xb2-40k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './dmnet_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/dmnet/dmnet_r101-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './dmnet_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/dmnet/dmnet_r101-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './dmnet_r50-d8_4xb4-160k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/dmnet/dmnet_r101-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './dmnet_r50-d8_4xb4-80k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/dmnet/dmnet_r50-d8_4xb2-40k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/dmnet/dmnet_r50-d8_4xb2-80k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/dmnet/dmnet_r50-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/dmnet_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/dmnet/dmnet_r50-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/dmnet_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/dnlnet/dnl_r101-d8_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './dnl_r50-d8_4xb2-40k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/dnlnet/dnl_r101-d8_4xb2-40k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './dnl_r50-d8_4xb2-40k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/dnlnet/dnl_r101-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './dnl_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/dnlnet/dnl_r101-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './dnl_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/dnlnet/dnl_r101-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './dnl_r50-d8_4xb4-160k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/dnlnet/dnl_r101-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './dnl_r50-d8_4xb4-80k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/dnlnet/dnl_r50-d8_4xb2-40k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/dnlnet/dnl_r50-d8_4xb2-80k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/dnlnet/dnl_r50-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/dnl_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/dnlnet/dnl_r50-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/dnl_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/emanet/emanet_r101-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './emanet_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/emanet/emanet_r101-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './emanet_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/emanet/emanet_r50-d8_4xb2-80k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './encnet_r50-d8_4xb2-40k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/encnet/encnet_r101-d8_4xb2-40k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './encnet_r50-d8_4xb2-40k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './encnet_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/encnet/encnet_r101-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './encnet_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/encnet/encnet_r101-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './encnet_r50-d8_4xb4-160k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/encnet/encnet_r101-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './encnet_r50-d8_4xb4-20k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/encnet/encnet_r101-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './encnet_r50-d8_4xb4-40k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/encnet/encnet_r101-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './encnet_r50-d8_4xb4-80k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/encnet/encnet_r50-d8_4xb2-40k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/encnet/encnet_r50-d8_4xb2-80k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/encnet/encnet_r50-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/encnet_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/encnet/encnet_r50-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/encnet_r50-d8.py',
3 | '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_20k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=21),
11 | auxiliary_head=dict(num_classes=21))
12 |
--------------------------------------------------------------------------------
/configs/encnet/encnet_r50-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/encnet_r50-d8.py',
3 | '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_40k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=21),
11 | auxiliary_head=dict(num_classes=21))
12 |
--------------------------------------------------------------------------------
/configs/encnet/encnet_r50-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/encnet_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb4-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | _base_ = './fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py'
3 | train_dataloader = dict(batch_size=4, num_workers=4)
4 | val_dataloader = dict(batch_size=1, num_workers=4)
5 | test_dataloader = val_dataloader
6 |
--------------------------------------------------------------------------------
/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4xb4-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | # model settings
2 | _base_ = './fastfcn_r50-d32_jpu_enc_4xb2-80k_cityscapes-512x1024.py'
3 | train_dataloader = dict(batch_size=4, num_workers=4)
4 | val_dataloader = dict(batch_size=1, num_workers=4)
5 | test_dataloader = val_dataloader
6 |
--------------------------------------------------------------------------------
/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/fastfcn_r50-d32_jpu_psp.py',
3 | '../_base_/datasets/cityscapes.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_80k.py'
5 | ]
6 | crop_size = (512, 1024)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(data_preprocessor=data_preprocessor)
9 |
--------------------------------------------------------------------------------
/configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn-d6_r50-d16_4xb2-40k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn-d6_r50-d16_4xb2-40k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn-d6_r50-d16_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='torchvision://resnet101',
4 | backbone=dict(type='ResNet', depth=101))
5 |
--------------------------------------------------------------------------------
/configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn-d6_r50b-d16_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(
3 | pretrained='torchvision://resnet101',
4 | backbone=dict(type='ResNet', depth=101))
5 |
--------------------------------------------------------------------------------
/configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
3 |
--------------------------------------------------------------------------------
/configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn-d6_r50-d16_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
3 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r101-d8_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_r50-d8_4xb2-40k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r101-d8_4xb2-40k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_r50-d8_4xb2-40k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r101-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r101-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_r101-d8_4xb2-80k_cityscapes-512x1024.py'
2 | optim_wrapper = dict(
3 | _delete_=True,
4 | type='AmpOptimWrapper',
5 | optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005),
6 | loss_scale=512.)
7 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r101-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_r50-d8_4xb4-160k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r101-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_r50-d8_4xb4-20k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-480x480.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_r50-d8_4xb4-40k_pascal-context-480x480.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-59-480x480.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_r50-d8_4xb4-40k_pascal-context-59-480x480.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r101-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_r50-d8_4xb4-40k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r101-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_r50-d8_4xb4-80k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-480x480.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_r50-d8_4xb4-40k_pascal-context-480x480.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-59-480x480.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_r50-d8_4xb4-80k_pascal-context-59-480x480.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='torchvision://resnet101',
4 | backbone=dict(type='ResNet', depth=101))
5 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(
3 | pretrained='torchvision://resnet101',
4 | backbone=dict(type='ResNet', depth=101))
5 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r18-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnet18_v1c',
4 | backbone=dict(depth=18),
5 | decode_head=dict(
6 | in_channels=512,
7 | channels=128,
8 | ),
9 | auxiliary_head=dict(in_channels=256, channels=64))
10 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r18-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnet18_v1c',
4 | backbone=dict(depth=18),
5 | decode_head=dict(
6 | in_channels=512,
7 | channels=128,
8 | ),
9 | auxiliary_head=dict(in_channels=256, channels=64))
10 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r18b-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='torchvision://resnet18',
4 | backbone=dict(type='ResNet', depth=18),
5 | decode_head=dict(
6 | in_channels=512,
7 | channels=128,
8 | ),
9 | auxiliary_head=dict(in_channels=256, channels=64))
10 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r18b-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(
3 | pretrained='torchvision://resnet18',
4 | backbone=dict(type='ResNet', depth=18),
5 | decode_head=dict(
6 | in_channels=512,
7 | channels=128,
8 | ),
9 | auxiliary_head=dict(in_channels=256, channels=64))
10 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r50-d8_4xb2-40k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r50-d8_4xb2-80k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r50-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r50-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/pascal_voc12_aug.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=21),
10 | auxiliary_head=dict(num_classes=21))
11 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r50-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/pascal_voc12_aug.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=21),
10 | auxiliary_head=dict(num_classes=21))
11 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r50-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r50b-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
3 |
--------------------------------------------------------------------------------
/configs/fcn/fcn_r50b-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
3 |
--------------------------------------------------------------------------------
/configs/gcnet/gcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './gcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/gcnet/gcnet_r101-d8_4xb2-40k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './gcnet_r50-d8_4xb2-40k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/gcnet/gcnet_r101-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './gcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/gcnet/gcnet_r101-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './gcnet_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/gcnet/gcnet_r101-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './gcnet_r50-d8_4xb4-160k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/gcnet/gcnet_r101-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './gcnet_r50-d8_4xb4-20k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/gcnet/gcnet_r101-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './gcnet_r50-d8_4xb4-40k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/gcnet/gcnet_r101-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './gcnet_r50-d8_4xb4-80k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/gcnet/gcnet_r50-d8_4xb2-40k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/gcnet/gcnet_r50-d8_4xb2-80k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/gcnet/gcnet_r50-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/gcnet_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/gcnet/gcnet_r50-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/gcnet_r50-d8.py',
3 | '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_20k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=21),
11 | auxiliary_head=dict(num_classes=21))
12 |
--------------------------------------------------------------------------------
/configs/gcnet/gcnet_r50-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/gcnet_r50-d8.py',
3 | '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_40k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=21),
11 | auxiliary_head=dict(num_classes=21))
12 |
--------------------------------------------------------------------------------
/configs/gcnet/gcnet_r50-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/gcnet_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18_4xb2-160k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18_4xb2-40k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18_4xb2-80k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18_4xb4-160k_ade20k-512x512.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 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor, decode_head=dict(num_classes=150))
9 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18_4xb4-20k_voc12aug-512x512.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 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor, decode_head=dict(num_classes=21))
9 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18_4xb4-40k_voc12aug-512x512.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 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor, decode_head=dict(num_classes=21))
9 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18_4xb4-80k_ade20k-512x512.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 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor, decode_head=dict(num_classes=150))
9 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18_4xb4-80k_isaid-896x896.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/isaid.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (896, 896)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor, decode_head=dict(num_classes=16))
9 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18_4xb4-80k_loveda-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/loveda.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor, decode_head=dict(num_classes=7))
9 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18_4xb4-80k_potsdam-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/potsdam.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor, decode_head=dict(num_classes=6))
9 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18_4xb4-80k_vaihingen-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/fcn_hr18.py', '../_base_/datasets/vaihingen.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor, decode_head=dict(num_classes=6))
9 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18s_4xb2-160k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_hr18_4xb2-160k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18s_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_hr18_4xb2-40k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18s_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_hr18_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18s_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_hr18_4xb4-160k_ade20k-512x512.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18s_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_hr18_4xb4-20k_voc12aug-512x512.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18s_4xb4-40k_pascal-context-480x480.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_hr18_4xb4-40k_pascal-context-480x480.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18s_4xb4-40k_pascal-context-59-480x480.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_hr18_4xb4-40k_pascal-context-59-480x480.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18s_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_hr18_4xb4-40k_voc12aug-512x512.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18s_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_hr18_4xb4-80k_ade20k-512x512.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18s_4xb4-80k_isaid-896x896.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_hr18_4xb4-80k_isaid-896x896.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18s_4xb4-80k_loveda-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_hr18_4xb4-80k_loveda-512x512.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18s_4xb4-80k_pascal-context-480x480.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_hr18_4xb4-80k_pascal-context-480x480.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18s_4xb4-80k_pascal-context-59-480x480.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_hr18_4xb4-80k_pascal-context-59-480x480.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18s_4xb4-80k_potsdam-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_hr18_4xb4-80k_potsdam-512x512.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/hrnet/fcn_hr18s_4xb4-80k_vaihingen-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './fcn_hr18_4xb4-80k_vaihingen-512x512.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/icnet/icnet_r101-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py:
--------------------------------------------------------------------------------
1 | _base_ = './icnet_r50-d8_4xb2-160k_cityscapes-832x832.py'
2 | model = dict(
3 | backbone=dict(
4 | backbone_cfg=dict(
5 | depth=101,
6 | init_cfg=dict(
7 | type='Pretrained', checkpoint='open-mmlab://resnet101_v1c'))))
8 |
--------------------------------------------------------------------------------
/configs/icnet/icnet_r101-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py:
--------------------------------------------------------------------------------
1 | _base_ = './icnet_r50-d8_4xb2-80k_cityscapes-832x832.py'
2 | model = dict(
3 | backbone=dict(
4 | backbone_cfg=dict(
5 | depth=101,
6 | init_cfg=dict(
7 | type='Pretrained', checkpoint='open-mmlab://resnet101_v1c'))))
8 |
--------------------------------------------------------------------------------
/configs/icnet/icnet_r101-d8_4xb2-160k_cityscapes-832x832.py:
--------------------------------------------------------------------------------
1 | _base_ = './icnet_r50-d8_4xb2-160k_cityscapes-832x832.py'
2 | model = dict(backbone=dict(backbone_cfg=dict(depth=101)))
3 |
--------------------------------------------------------------------------------
/configs/icnet/icnet_r101-d8_4xb2-80k_cityscapes-832x832.py:
--------------------------------------------------------------------------------
1 | _base_ = './icnet_r50-d8_4xb2-80k_cityscapes-832x832.py'
2 | model = dict(backbone=dict(backbone_cfg=dict(depth=101)))
3 |
--------------------------------------------------------------------------------
/configs/icnet/icnet_r18-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py:
--------------------------------------------------------------------------------
1 | _base_ = './icnet_r50-d8_4xb2-160k_cityscapes-832x832.py'
2 | model = dict(
3 | backbone=dict(
4 | layer_channels=(128, 512),
5 | backbone_cfg=dict(
6 | depth=18,
7 | init_cfg=dict(
8 | type='Pretrained', checkpoint='open-mmlab://resnet18_v1c'))))
9 |
--------------------------------------------------------------------------------
/configs/icnet/icnet_r18-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py:
--------------------------------------------------------------------------------
1 | _base_ = './icnet_r50-d8_4xb2-80k_cityscapes-832x832.py'
2 | model = dict(
3 | backbone=dict(
4 | layer_channels=(128, 512),
5 | backbone_cfg=dict(
6 | depth=18,
7 | init_cfg=dict(
8 | type='Pretrained', checkpoint='open-mmlab://resnet18_v1c'))))
9 |
--------------------------------------------------------------------------------
/configs/icnet/icnet_r18-d8_4xb2-160k_cityscapes-832x832.py:
--------------------------------------------------------------------------------
1 | _base_ = './icnet_r50-d8_4xb2-160k_cityscapes-832x832.py'
2 | model = dict(
3 | backbone=dict(layer_channels=(128, 512), backbone_cfg=dict(depth=18)))
4 |
--------------------------------------------------------------------------------
/configs/icnet/icnet_r18-d8_4xb2-80k_cityscapes-832x832.py:
--------------------------------------------------------------------------------
1 | _base_ = './icnet_r50-d8_4xb2-80k_cityscapes-832x832.py'
2 | model = dict(
3 | backbone=dict(layer_channels=(128, 512), backbone_cfg=dict(depth=18)))
4 |
--------------------------------------------------------------------------------
/configs/icnet/icnet_r50-d8-in1k-pre_4xb2-160k_cityscapes-832x832.py:
--------------------------------------------------------------------------------
1 | _base_ = './icnet_r50-d8_4xb2-160k_cityscapes-832x832.py'
2 | model = dict(
3 | backbone=dict(
4 | backbone_cfg=dict(
5 | init_cfg=dict(
6 | type='Pretrained', checkpoint='open-mmlab://resnet50_v1c'))))
7 |
--------------------------------------------------------------------------------
/configs/icnet/icnet_r50-d8-in1k-pre_4xb2-80k_cityscapes-832x832.py:
--------------------------------------------------------------------------------
1 | _base_ = './icnet_r50-d8_4xb2-80k_cityscapes-832x832.py'
2 | model = dict(
3 | backbone=dict(
4 | backbone_cfg=dict(
5 | init_cfg=dict(
6 | type='Pretrained', checkpoint='open-mmlab://resnet50_v1c'))))
7 |
--------------------------------------------------------------------------------
/configs/icnet/icnet_r50-d8_4xb2-160k_cityscapes-832x832.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/icnet_r50-d8.py',
3 | '../_base_/datasets/cityscapes_832x832.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_160k.py'
5 | ]
6 | crop_size = (832, 832)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(data_preprocessor=data_preprocessor)
9 |
--------------------------------------------------------------------------------
/configs/icnet/icnet_r50-d8_4xb2-80k_cityscapes-832x832.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/icnet_r50-d8.py',
3 | '../_base_/datasets/cityscapes_832x832.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_80k.py'
5 | ]
6 | crop_size = (832, 832)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(data_preprocessor=data_preprocessor)
9 |
--------------------------------------------------------------------------------
/configs/isanet/isanet_r101-d8_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './isanet_r50-d8_4xb2-40k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/isanet/isanet_r101-d8_4xb2-40k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './isanet_r50-d8_4xb2-40k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/isanet/isanet_r101-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './isanet_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/isanet/isanet_r101-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './isanet_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/isanet/isanet_r101-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './isanet_r50-d8_4xb4-160k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/isanet/isanet_r101-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './isanet_r50-d8_4xb4-20k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/isanet/isanet_r101-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './isanet_r50-d8_4xb4-40k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/isanet/isanet_r101-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './isanet_r50-d8_4xb4-80k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/isanet/isanet_r50-d8_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/isanet_r50-d8.py', '../_base_/datasets/cityscapes.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
4 | ]
5 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/isanet/isanet_r50-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/isanet_r50-d8.py', '../_base_/datasets/cityscapes.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/isanet/isanet_r50-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/isanet_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/isanet/isanet_r50-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/isanet_r50-d8.py',
3 | '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_20k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=21),
11 | auxiliary_head=dict(num_classes=21))
12 |
--------------------------------------------------------------------------------
/configs/isanet/isanet_r50-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/isanet_r50-d8.py',
3 | '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_40k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=21),
11 | auxiliary_head=dict(num_classes=21))
12 |
--------------------------------------------------------------------------------
/configs/isanet/isanet_r50-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/isanet_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/mask2former/mask2former_r101_8xb2-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./mask2former_r50_8xb2-160k_ade20k-512x512.py']
2 |
3 | model = dict(
4 | backbone=dict(
5 | depth=101,
6 | init_cfg=dict(type='Pretrained',
7 | checkpoint='torchvision://resnet101')))
8 |
--------------------------------------------------------------------------------
/configs/mask2former/mask2former_r101_8xb2-90k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./mask2former_r50_8xb2-90k_cityscapes-512x1024.py']
2 |
3 | model = dict(
4 | backbone=dict(
5 | depth=101,
6 | init_cfg=dict(type='Pretrained',
7 | checkpoint='torchvision://resnet101')))
8 |
--------------------------------------------------------------------------------
/configs/mask2former/mask2former_swin-b-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640.py']
2 |
3 | pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window12_384_22k_20220317-e5c09f74.pth' # noqa
4 | model = dict(
5 | backbone=dict(init_cfg=dict(type='Pretrained', checkpoint=pretrained)))
6 |
--------------------------------------------------------------------------------
/configs/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './maskformer_r50-d32_8xb2-160k_ade20k-512x512.py'
2 |
3 | model = dict(
4 | backbone=dict(
5 | depth=101,
6 | init_cfg=dict(type='Pretrained',
7 | checkpoint='torchvision://resnet101')))
8 |
--------------------------------------------------------------------------------
/configs/nonlocal_net/nonlocal_r101-d8_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './nonlocal_r50-d8_4xb2-40k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/nonlocal_net/nonlocal_r101-d8_4xb2-40k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './nonlocal_r50-d8_4xb2-40k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/nonlocal_net/nonlocal_r101-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './nonlocal_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/nonlocal_net/nonlocal_r101-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './nonlocal_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/nonlocal_net/nonlocal_r101-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './nonlocal_r50-d8_4xb4-160k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/nonlocal_net/nonlocal_r101-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './nonlocal_r50-d8_4xb4-20k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/nonlocal_net/nonlocal_r101-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './nonlocal_r50-d8_4xb4-40k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/nonlocal_net/nonlocal_r101-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './nonlocal_r50-d8_4xb4-80k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/nonlocal_net/nonlocal_r50-d8_4xb2-40k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/nonlocal_net/nonlocal_r50-d8_4xb2-80k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/nonlocal_net/nonlocal_r50-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/nonlocal_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/nonlocal_net/nonlocal_r50-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/nonlocal_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/ocrnet/ocrnet_hr18_4xb2-160k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/ocrnet/ocrnet_hr18_4xb2-40k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/ocrnet/ocrnet_hr18_4xb2-80k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/ocrnet/ocrnet_hr18s_4xb2-160k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './ocrnet_hr18_4xb2-160k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/ocrnet/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './ocrnet_hr18_4xb2-40k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/ocrnet/ocrnet_hr18s_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './ocrnet_hr18_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/ocrnet/ocrnet_hr18s_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './ocrnet_hr18_4xb4-160k_ade20k-512x512.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/ocrnet/ocrnet_hr18s_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './ocrnet_hr18_4xb4-20k_voc12aug-512x512.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/ocrnet/ocrnet_hr18s_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './ocrnet_hr18_4xb4-40k_voc12aug-512x512.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/ocrnet/ocrnet_hr18s_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './ocrnet_hr18_4xb4-80k_ade20k-512x512.py'
2 | model = dict(
3 | pretrained='open-mmlab://msra/hrnetv2_w18_small',
4 | backbone=dict(
5 | extra=dict(
6 | stage1=dict(num_blocks=(2, )),
7 | stage2=dict(num_blocks=(2, 2)),
8 | stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
9 | stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
10 |
--------------------------------------------------------------------------------
/configs/ocrnet/ocrnet_r101-d8_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/ocrnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
4 | ]
5 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | pretrained='open-mmlab://resnet101_v1c',
10 | backbone=dict(depth=101))
11 |
--------------------------------------------------------------------------------
/configs/pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes.py:
--------------------------------------------------------------------------------
1 | _base_ = './pidnet-s_2xb6-120k_1024x1024-cityscapes.py'
2 | checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/pidnet/pidnet-m_imagenet1k_20230306-39893c52.pth' # noqa
3 | model = dict(
4 | backbone=dict(channels=64, init_cfg=dict(checkpoint=checkpoint_file)),
5 | decode_head=dict(in_channels=256))
6 |
--------------------------------------------------------------------------------
/configs/point_rend/pointrend_r101_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './pointrend_r50_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/point_rend/pointrend_r101_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './pointrend_r50_4xb4-160k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/psanet/psanet_r101-d8_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './psanet_r50-d8_4xb2-40k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/psanet/psanet_r101-d8_4xb2-40k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './psanet_r50-d8_4xb2-40k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/psanet/psanet_r101-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './psanet_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/psanet/psanet_r101-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './psanet_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/psanet/psanet_r101-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './psanet_r50-d8_4xb4-160k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/psanet/psanet_r101-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './psanet_r50-d8_4xb4-20k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/psanet/psanet_r101-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './psanet_r50-d8_4xb4-40k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/psanet/psanet_r101-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './psanet_r50-d8_4xb4-80k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/psanet/psanet_r50-d8_4xb2-40k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/psanet/psanet_r50-d8_4xb2-80k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/psanet/psanet_r50-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/psanet_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(mask_size=(66, 66), num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/psanet/psanet_r50-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/psanet_r50-d8.py',
3 | '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_20k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=21),
11 | auxiliary_head=dict(num_classes=21))
12 |
--------------------------------------------------------------------------------
/configs/psanet/psanet_r50-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/psanet_r50-d8.py',
3 | '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_40k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=21),
11 | auxiliary_head=dict(num_classes=21))
12 |
--------------------------------------------------------------------------------
/configs/psanet/psanet_r50-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/psanet_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(mask_size=(66, 66), num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024_dark-zurich-1920x1080.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb2-40k_cityscapes-512x1024_dark-zurich-1920x1080.py' # noqa
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-512x1024_night-driving-1920x1080.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb2-40k_cityscapes-512x1024_night-driving-1920x1080.py' # noqa
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb2-40k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb2-40k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r101-d8_4xb2-80k_cityscapes-512x1024.py'
2 | optim_wrapper = dict(
3 | _delete_=True,
4 | type='AmpOptimWrapper',
5 | optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005),
6 | loss_scale=512.)
7 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb4-160k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb4-160k_coco-stuff164k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb4-160k_coco-stuff164k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb4-20k_coco-stuff10k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb4-20k_coco-stuff10k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb4-20k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb4-320k_coco-stuff164k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb4-40k_coco-stuff10k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-480x480.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb4-40k_pascal-context-480x480.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb4-40k_pascal-context-59-480x480.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb4-40k_pascal-context-59-480x480.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb4-80k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb4-80k_coco-stuff164k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb4-80k_coco-stuff164k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb4-80k_loveda-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb4-80k_loveda-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-480x480.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb4-80k_pascal-context-480x480.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb4-80k_pascal-context-59-480x480.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb4-80k_potsdam-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb4-80k_potsdam-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101-d8_4xb4-80k_vaihingen-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb4-80k_vaihingen-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='torchvision://resnet101',
4 | backbone=dict(type='ResNet', depth=101))
5 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024_dark-zurich-1920x1080.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024_dark-zurich-1920x1080.py' # noqa
2 | model = dict(
3 | pretrained='torchvision://resnet101',
4 | backbone=dict(type='ResNet', depth=101))
5 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-512x1024_night-driving-1920x1080.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024_night-driving-1920x1080.py' # noqa
2 | model = dict(
3 | pretrained='torchvision://resnet101',
4 | backbone=dict(type='ResNet', depth=101))
5 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r101b-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(
3 | pretrained='torchvision://resnet101',
4 | backbone=dict(type='ResNet', depth=101))
5 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnet18_v1c',
4 | backbone=dict(depth=18),
5 | decode_head=dict(
6 | in_channels=512,
7 | channels=128,
8 | ),
9 | auxiliary_head=dict(in_channels=256, channels=64))
10 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r18-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnet18_v1c',
4 | backbone=dict(depth=18),
5 | decode_head=dict(
6 | in_channels=512,
7 | channels=128,
8 | ),
9 | auxiliary_head=dict(in_channels=256, channels=64))
10 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r18-d8_4xb4-80k_isaid-896x896.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb4-80k_isaid-896x896.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnet18_v1c',
4 | backbone=dict(depth=18),
5 | decode_head=dict(
6 | in_channels=512,
7 | channels=128,
8 | ),
9 | auxiliary_head=dict(in_channels=256, channels=64))
10 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r18-d8_4xb4-80k_loveda-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb4-80k_loveda-512x512.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnet18_v1c',
4 | backbone=dict(depth=18),
5 | decode_head=dict(
6 | in_channels=512,
7 | channels=128,
8 | ),
9 | auxiliary_head=dict(in_channels=256, channels=64))
10 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r18-d8_4xb4-80k_potsdam-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb4-80k_potsdam-512x512.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnet18_v1c',
4 | backbone=dict(depth=18),
5 | decode_head=dict(
6 | in_channels=512,
7 | channels=128,
8 | ),
9 | auxiliary_head=dict(in_channels=256, channels=64))
10 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r18-d8_4xb4-80k_vaihingen-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb4-80k_vaihingen-512x512.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnet18_v1c',
4 | backbone=dict(depth=18),
5 | decode_head=dict(
6 | in_channels=512,
7 | channels=128,
8 | ),
9 | auxiliary_head=dict(in_channels=256, channels=64))
10 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='torchvision://resnet18',
4 | backbone=dict(type='ResNet', depth=18),
5 | decode_head=dict(
6 | in_channels=512,
7 | channels=128,
8 | ),
9 | auxiliary_head=dict(in_channels=256, channels=64))
10 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(
3 | pretrained='torchvision://resnet18',
4 | backbone=dict(type='ResNet', depth=18),
5 | decode_head=dict(
6 | in_channels=512,
7 | channels=128,
8 | ),
9 | auxiliary_head=dict(in_channels=256, channels=64))
10 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r50-d32_4xb2-80k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | backbone=dict(dilations=(1, 1, 2, 4), strides=(1, 2, 2, 2)))
10 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r50-d8_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r50-d8_4xb4-160k_coco-stuff164k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/pspnet_r50-d8.py',
3 | '../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_160k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=171),
11 | auxiliary_head=dict(num_classes=171))
12 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r50-d8_4xb4-20k_coco-stuff10k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/coco-stuff10k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=171),
10 | auxiliary_head=dict(num_classes=171))
11 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r50-d8_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/pspnet_r50-d8.py',
3 | '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_20k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=21),
11 | auxiliary_head=dict(num_classes=21))
12 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/pspnet_r50-d8.py',
3 | '../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_320k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=171),
11 | auxiliary_head=dict(num_classes=171))
12 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/coco-stuff10k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=171),
10 | auxiliary_head=dict(num_classes=171))
11 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/pspnet_r50-d8.py',
3 | '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_40k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=21),
11 | auxiliary_head=dict(num_classes=21))
12 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r50-d8_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r50-d8_4xb4-80k_coco-stuff164k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/pspnet_r50-d8.py',
3 | '../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_80k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=171),
11 | auxiliary_head=dict(num_classes=171))
12 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r50-d8_4xb4-80k_isaid-896x896.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/isaid.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (896, 896)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=16),
10 | auxiliary_head=dict(num_classes=16))
11 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r50-d8_4xb4-80k_loveda-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/loveda.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=7),
10 | auxiliary_head=dict(num_classes=7))
11 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r50-d8_4xb4-80k_potsdam-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/potsdam.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=6),
10 | auxiliary_head=dict(num_classes=6))
11 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r50-d8_4xb4-80k_vaihingen-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/vaihingen.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=6),
10 | auxiliary_head=dict(num_classes=6))
11 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
3 |
--------------------------------------------------------------------------------
/configs/pspnet/pspnet_r50b-d8_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './pspnet_r50-d8_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
3 |
--------------------------------------------------------------------------------
/configs/resnest/resnest_s101-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = '../deeplabv3/deeplabv3_r101-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnest101',
4 | backbone=dict(
5 | type='ResNeSt',
6 | stem_channels=128,
7 | radix=2,
8 | reduction_factor=4,
9 | avg_down_stride=True))
10 |
--------------------------------------------------------------------------------
/configs/resnest/resnest_s101-d8_deeplabv3_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = '../deeplabv3/deeplabv3_r101-d8_4xb4-160k_ade20k-512x512.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnest101',
4 | backbone=dict(
5 | type='ResNeSt',
6 | stem_channels=128,
7 | radix=2,
8 | reduction_factor=4,
9 | avg_down_stride=True))
10 |
--------------------------------------------------------------------------------
/configs/resnest/resnest_s101-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024.py' # noqa
2 | model = dict(
3 | pretrained='open-mmlab://resnest101',
4 | backbone=dict(
5 | type='ResNeSt',
6 | stem_channels=128,
7 | radix=2,
8 | reduction_factor=4,
9 | avg_down_stride=True))
10 |
--------------------------------------------------------------------------------
/configs/resnest/resnest_s101-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_4xb4-160k_ade20k-512x512.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnest101',
4 | backbone=dict(
5 | type='ResNeSt',
6 | stem_channels=128,
7 | radix=2,
8 | reduction_factor=4,
9 | avg_down_stride=True))
10 |
--------------------------------------------------------------------------------
/configs/resnest/resnest_s101-d8_fcn_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = '../fcn/fcn_r101-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnest101',
4 | backbone=dict(
5 | type='ResNeSt',
6 | stem_channels=128,
7 | radix=2,
8 | reduction_factor=4,
9 | avg_down_stride=True))
10 |
--------------------------------------------------------------------------------
/configs/resnest/resnest_s101-d8_fcn_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = '../fcn/fcn_r101-d8_4xb4-160k_ade20k-512x512.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnest101',
4 | backbone=dict(
5 | type='ResNeSt',
6 | stem_channels=128,
7 | radix=2,
8 | reduction_factor=4,
9 | avg_down_stride=True))
10 |
--------------------------------------------------------------------------------
/configs/resnest/resnest_s101-d8_pspnet_4xb2-80k_cityscapes512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = '../pspnet/pspnet_r101-d8_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnest101',
4 | backbone=dict(
5 | type='ResNeSt',
6 | stem_channels=128,
7 | radix=2,
8 | reduction_factor=4,
9 | avg_down_stride=True))
10 |
--------------------------------------------------------------------------------
/configs/resnest/resnest_s101-d8_pspnet_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = '../pspnet/pspnet_r101-d8_4xb4-160k_ade20k-512x512.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnest101',
4 | backbone=dict(
5 | type='ResNeSt',
6 | stem_channels=128,
7 | radix=2,
8 | reduction_factor=4,
9 | avg_down_stride=True))
10 |
--------------------------------------------------------------------------------
/configs/segformer/segformer_mit-b1_8xb1-160k_cityscapes-1024x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py']
2 |
3 | checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b1_20220624-02e5a6a1.pth' # noqa
4 |
5 | model = dict(
6 | backbone=dict(
7 | init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
8 | embed_dims=64),
9 | decode_head=dict(in_channels=[64, 128, 320, 512]))
10 |
--------------------------------------------------------------------------------
/configs/sem_fpn/fpn_r101_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './fpn_r50_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/sem_fpn/fpn_r101_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './fpn_r50_4xb4-160k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 | crop_size = (512, 512)
4 | data_preprocessor = dict(size=crop_size)
5 | model = dict(data_preprocessor=data_preprocessor)
6 |
--------------------------------------------------------------------------------
/configs/sem_fpn/fpn_r50_4xb2-80k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/sem_fpn/fpn_r50_4xb4-160k_ade20k-512x512.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 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor, decode_head=dict(num_classes=150))
9 |
--------------------------------------------------------------------------------
/configs/setr/setr_vit-l_mla_8xb2-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./setr_vit-l-mla_8xb1-160k_ade20k-512x512.py']
2 |
3 | # num_gpus: 8 -> batch_size: 16
4 | train_dataloader = dict(batch_size=2)
5 | val_dataloader = dict(batch_size=1)
6 | test_dataloader = val_dataloader
7 |
--------------------------------------------------------------------------------
/configs/stdc/stdc1_in1k-pre_4xb12-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/stdc/stdc1_20220308-5368626c.pth' # noqa
2 | _base_ = './stdc1_4xb12-80k_cityscapes-512x1024.py'
3 | model = dict(
4 | backbone=dict(
5 | backbone_cfg=dict(
6 | init_cfg=dict(type='Pretrained', checkpoint=checkpoint))))
7 |
--------------------------------------------------------------------------------
/configs/stdc/stdc2_4xb12-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './stdc1_4xb12-80k_cityscapes-512x1024.py'
2 | model = dict(backbone=dict(backbone_cfg=dict(stdc_type='STDCNet2')))
3 |
--------------------------------------------------------------------------------
/configs/stdc/stdc2_in1k-pre_4xb12-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/stdc/stdc2_20220308-7dbd9127.pth' # noqa
2 | _base_ = './stdc2_4xb12-80k_cityscapes-512x1024.py'
3 | model = dict(
4 | backbone=dict(
5 | backbone_cfg=dict(
6 | init_cfg=dict(type='Pretrained', checkpoint=checkpoint))))
7 |
--------------------------------------------------------------------------------
/configs/swin/swin-base-patch4-window12-in22k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | './swin-base-patch4-window12-in1k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py' # noqa
3 | ]
4 | checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window12_384_22k_20220317-e5c09f74.pth' # noqa
5 | model = dict(
6 | backbone=dict(
7 | init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file)))
8 |
--------------------------------------------------------------------------------
/configs/swin/swin-base-patch4-window7-in22k-pre_upernet_8xb2-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | './swin-base-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512.py'
3 | ]
4 | checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window7_224_22k_20220317-4f79f7c0.pth' # noqa
5 | model = dict(
6 | backbone=dict(
7 | init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file)))
8 |
--------------------------------------------------------------------------------
/configs/twins/twins_pcpvt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./twins_pcpvt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py']
2 |
3 | checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_base_20220308-0621964c.pth' # noqa
4 |
5 | model = dict(
6 | backbone=dict(
7 | init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
8 | depths=[3, 4, 18, 3]), )
9 |
--------------------------------------------------------------------------------
/configs/twins/twins_pcpvt-l_fpn_fpnhead_8xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = ['./twins_pcpvt-s_fpn_fpnhead_8xb4-80k_ade20k-512x512.py']
2 |
3 | checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/twins/pcpvt_large_20220308-37579dc6.pth' # noqa
4 |
5 | model = dict(
6 | backbone=dict(
7 | init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
8 | depths=[3, 8, 27, 3]))
9 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_drive-64x64.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/deeplabv3_unet_s5-d16.py', '../_base_/datasets/drive.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
4 | ]
5 | crop_size = (64, 64)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | test_cfg=dict(crop_size=(64, 64), stride=(42, 42)))
10 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_hrf-256x256.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/deeplabv3_unet_s5-d16.py', '../_base_/datasets/hrf.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
4 | ]
5 | crop_size = (256, 256)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | test_cfg=dict(crop_size=(256, 256), stride=(170, 170)))
10 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_deeplabv3_4xb4-40k_stare-128x128.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/deeplabv3_unet_s5-d16.py', '../_base_/datasets/stare.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
4 | ]
5 | crop_size = (128, 128)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
10 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py:
--------------------------------------------------------------------------------
1 | _base_ = './unet_s5-d16_deeplabv3_4xb4-40k_chase-db1-128x128.py'
2 | model = dict(
3 | decode_head=dict(loss_decode=[
4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
6 | ]))
7 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py:
--------------------------------------------------------------------------------
1 | _base_ = './unet-s5-d16_deeplabv3_4xb4-40k_drive-64x64.py'
2 | model = dict(
3 | decode_head=dict(loss_decode=[
4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
6 | ]))
7 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py:
--------------------------------------------------------------------------------
1 | _base_ = './unet-s5-d16_deeplabv3_4xb4-40k_hrf-256x256.py'
2 | model = dict(
3 | decode_head=dict(loss_decode=[
4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
6 | ]))
7 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_deeplabv3_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py:
--------------------------------------------------------------------------------
1 | _base_ = './unet-s5-d16_deeplabv3_4xb4-40k_stare-128x128.py'
2 | model = dict(
3 | decode_head=dict(loss_decode=[
4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
6 | ]))
7 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/chase_db1.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
4 | ]
5 | crop_size = (128, 128)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
10 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_fcn_4xb4-40k_drive-64x64.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/drive.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
4 | ]
5 | crop_size = (64, 64)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | test_cfg=dict(crop_size=(64, 64), stride=(42, 42)))
10 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_fcn_4xb4-40k_hrf-256x256.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/hrf.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
4 | ]
5 | crop_size = (256, 256)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | test_cfg=dict(crop_size=(256, 256), stride=(170, 170)))
10 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_fcn_4xb4-40k_stare-128x128.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/stare.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
4 | ]
5 | crop_size = (128, 128)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
10 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py:
--------------------------------------------------------------------------------
1 | _base_ = './unet-s5-d16_fcn_4xb4-40k_chase-db1-128x128.py'
2 | model = dict(
3 | decode_head=dict(loss_decode=[
4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
6 | ]))
7 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py:
--------------------------------------------------------------------------------
1 | _base_ = './unet-s5-d16_fcn_4xb4-40k_drive-64x64.py'
2 | model = dict(
3 | decode_head=dict(loss_decode=[
4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
6 | ]))
7 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py:
--------------------------------------------------------------------------------
1 | _base_ = './unet-s5-d16_fcn_4xb4-40k_hrf-256x256.py'
2 | model = dict(
3 | decode_head=dict(loss_decode=[
4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
6 | ]))
7 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_fcn_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py:
--------------------------------------------------------------------------------
1 | _base_ = './unet-s5-d16_fcn_4xb4-40k_stare-128x128.py'
2 | model = dict(
3 | decode_head=dict(loss_decode=[
4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
6 | ]))
7 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_pspnet_4xb4-40k_chase-db1-128x128.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/pspnet_unet_s5-d16.py',
3 | '../_base_/datasets/chase_db1.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_40k.py'
5 | ]
6 | crop_size = (128, 128)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
11 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_pspnet_4xb4-40k_drive-64x64.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/pspnet_unet_s5-d16.py', '../_base_/datasets/drive.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
4 | ]
5 | crop_size = (64, 64)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | test_cfg=dict(crop_size=(64, 64), stride=(42, 42)))
10 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_pspnet_4xb4-40k_hrf-256x256.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/pspnet_unet_s5-d16.py', '../_base_/datasets/hrf.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
4 | ]
5 | crop_size = (256, 256)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | test_cfg=dict(crop_size=(256, 256), stride=(170, 170)))
10 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_pspnet_4xb4-40k_stare-128x128.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/pspnet_unet_s5-d16.py', '../_base_/datasets/stare.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
4 | ]
5 | crop_size = (128, 128)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
10 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_chase-db1-128x128.py:
--------------------------------------------------------------------------------
1 | _base_ = './unet-s5-d16_pspnet_4xb4-40k_chase-db1-128x128.py'
2 | model = dict(
3 | decode_head=dict(loss_decode=[
4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
6 | ]))
7 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_drive-64x64.py:
--------------------------------------------------------------------------------
1 | _base_ = './unet-s5-d16_pspnet_4xb4-40k_drive-64x64.py'
2 | model = dict(
3 | decode_head=dict(loss_decode=[
4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
6 | ]))
7 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_hrf-256x256.py:
--------------------------------------------------------------------------------
1 | _base_ = './unet-s5-d16_pspnet_4xb4-40k_hrf-256x256.py'
2 | model = dict(
3 | decode_head=dict(loss_decode=[
4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
6 | ]))
7 |
--------------------------------------------------------------------------------
/configs/unet/unet-s5-d16_pspnet_4xb4-ce-1.0-dice-3.0-40k_stare-128x128.py:
--------------------------------------------------------------------------------
1 | _base_ = './unet-s5-d16_pspnet_4xb4-40k_stare-128x128.py'
2 | model = dict(
3 | decode_head=dict(loss_decode=[
4 | dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
5 | dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
6 | ]))
7 |
--------------------------------------------------------------------------------
/configs/unet/unet_s5-d16_deeplabv3_4xb4-40k_chase-db1-128x128.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/deeplabv3_unet_s5-d16.py',
3 | '../_base_/datasets/chase_db1.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_40k.py'
5 | ]
6 | crop_size = (128, 128)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
11 |
--------------------------------------------------------------------------------
/configs/upernet/upernet_r101_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './upernet_r50_4xb2-40k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/upernet/upernet_r101_4xb2-40k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './upernet_r50_4xb2-40k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/upernet/upernet_r101_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './upernet_r50_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/upernet/upernet_r101_4xb2-80k_cityscapes-769x769.py:
--------------------------------------------------------------------------------
1 | _base_ = './upernet_r50_4xb2-80k_cityscapes-769x769.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/upernet/upernet_r101_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './upernet_r50_4xb4-160k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/upernet/upernet_r101_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './upernet_r50_4xb4-20k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/upernet/upernet_r101_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './upernet_r50_4xb4-40k_voc12aug-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/upernet/upernet_r101_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './upernet_r50_4xb4-80k_ade20k-512x512.py'
2 | model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
3 |
--------------------------------------------------------------------------------
/configs/upernet/upernet_r18_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './upernet_r50_4xb2-40k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnet18_v1c',
4 | backbone=dict(depth=18),
5 | decode_head=dict(in_channels=[64, 128, 256, 512]),
6 | auxiliary_head=dict(in_channels=256))
7 |
--------------------------------------------------------------------------------
/configs/upernet/upernet_r18_4xb2-80k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = './upernet_r50_4xb2-80k_cityscapes-512x1024.py'
2 | model = dict(
3 | pretrained='open-mmlab://resnet18_v1c',
4 | backbone=dict(depth=18),
5 | decode_head=dict(in_channels=[64, 128, 256, 512]),
6 | auxiliary_head=dict(in_channels=256))
7 |
--------------------------------------------------------------------------------
/configs/upernet/upernet_r18_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/upernet_r50.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
4 | ]
5 | model = dict(
6 | pretrained='open-mmlab://resnet18_v1c',
7 | backbone=dict(depth=18),
8 | decode_head=dict(in_channels=[64, 128, 256, 512], num_classes=150),
9 | auxiliary_head=dict(in_channels=256, num_classes=150))
10 |
--------------------------------------------------------------------------------
/configs/upernet/upernet_r18_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/upernet_r50.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | model = dict(
6 | pretrained='open-mmlab://resnet18_v1c',
7 | backbone=dict(depth=18),
8 | decode_head=dict(in_channels=[64, 128, 256, 512], num_classes=150),
9 | auxiliary_head=dict(in_channels=256, num_classes=150))
10 |
--------------------------------------------------------------------------------
/configs/upernet/upernet_r50_4xb2-40k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/upernet/upernet_r50_4xb2-80k_cityscapes-512x1024.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 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(data_preprocessor=data_preprocessor)
8 |
--------------------------------------------------------------------------------
/configs/upernet/upernet_r50_4xb4-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/upernet_r50.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/upernet/upernet_r50_4xb4-20k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/upernet_r50.py',
3 | '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_20k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=21),
11 | auxiliary_head=dict(num_classes=21))
12 |
--------------------------------------------------------------------------------
/configs/upernet/upernet_r50_4xb4-40k_voc12aug-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/upernet_r50.py',
3 | '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
4 | '../_base_/schedules/schedule_40k.py'
5 | ]
6 | crop_size = (512, 512)
7 | data_preprocessor = dict(size=crop_size)
8 | model = dict(
9 | data_preprocessor=data_preprocessor,
10 | decode_head=dict(num_classes=21),
11 | auxiliary_head=dict(num_classes=21))
12 |
--------------------------------------------------------------------------------
/configs/upernet/upernet_r50_4xb4-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = [
2 | '../_base_/models/upernet_r50.py', '../_base_/datasets/ade20k.py',
3 | '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
4 | ]
5 | crop_size = (512, 512)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor,
9 | decode_head=dict(num_classes=150),
10 | auxiliary_head=dict(num_classes=150))
11 |
--------------------------------------------------------------------------------
/configs/vit/vit_deit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py'
2 |
3 | model = dict(
4 | pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth',
5 | backbone=dict(drop_path_rate=0.1, final_norm=True))
6 |
--------------------------------------------------------------------------------
/configs/vit/vit_deit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py'
2 |
3 | model = dict(
4 | pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth',
5 | backbone=dict(drop_path_rate=0.1),
6 | )
7 |
--------------------------------------------------------------------------------
/configs/vit/vit_deit-b16_upernet_8xb2-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py'
2 |
3 | model = dict(
4 | pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth',
5 | backbone=dict(drop_path_rate=0.1),
6 | neck=None)
7 |
--------------------------------------------------------------------------------
/configs/vit/vit_deit-b16_upernet_8xb2-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py'
2 |
3 | model = dict(
4 | pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth',
5 | backbone=dict(drop_path_rate=0.1),
6 | neck=None)
7 |
--------------------------------------------------------------------------------
/configs/vit/vit_deit-s16_upernet_8xb2-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py'
2 |
3 | model = dict(
4 | pretrained='pretrain/deit_small_patch16_224-cd65a155.pth',
5 | backbone=dict(num_heads=6, embed_dims=384, drop_path_rate=0.1),
6 | decode_head=dict(num_classes=150, in_channels=[384, 384, 384, 384]),
7 | neck=None,
8 | auxiliary_head=dict(num_classes=150, in_channels=384))
9 |
--------------------------------------------------------------------------------
/configs/vit/vit_deit-s16_upernet_8xb2-80k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py'
2 |
3 | model = dict(
4 | pretrained='pretrain/deit_small_patch16_224-cd65a155.pth',
5 | backbone=dict(num_heads=6, embed_dims=384, drop_path_rate=0.1),
6 | decode_head=dict(num_classes=150, in_channels=[384, 384, 384, 384]),
7 | neck=None,
8 | auxiliary_head=dict(num_classes=150, in_channels=384))
9 |
--------------------------------------------------------------------------------
/demo/classroom__rgb_00283.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/zhuqinfeng1999/Samba/229687b62ef4ddbda5835e5f3f0b1182e8ab4a49/demo/classroom__rgb_00283.jpg
--------------------------------------------------------------------------------
/demo/demo.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/zhuqinfeng1999/Samba/229687b62ef4ddbda5835e5f3f0b1182e8ab4a49/demo/demo.png
--------------------------------------------------------------------------------
/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 |
--------------------------------------------------------------------------------
/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 |
--------------------------------------------------------------------------------
/docs/en/.readthedocs.yaml:
--------------------------------------------------------------------------------
1 | version: 2
2 |
3 | build:
4 | os: ubuntu-22.04
5 | tools:
6 | python: "3.8"
7 |
8 | formats:
9 | - epub
10 |
11 | sphinx:
12 | configuration: docs/en/conf.py
13 |
14 | python:
15 | install:
16 | - requirements: requirements/docs.txt
17 | - requirements: requirements/readthedocs.txt
18 |
--------------------------------------------------------------------------------
/docs/en/_static/css/readthedocs.css:
--------------------------------------------------------------------------------
1 | .header-logo {
2 | background-image: url("../images/mmsegmentation.png");
3 | background-size: 201px 40px;
4 | height: 40px;
5 | width: 201px;
6 | }
7 |
--------------------------------------------------------------------------------
/docs/en/_static/images/mmsegmentation.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/zhuqinfeng1999/Samba/229687b62ef4ddbda5835e5f3f0b1182e8ab4a49/docs/en/_static/images/mmsegmentation.png
--------------------------------------------------------------------------------
/docs/en/migration/index.rst:
--------------------------------------------------------------------------------
1 | Migration
2 | ***************
3 |
4 | .. toctree::
5 | :maxdepth: 1
6 |
7 | interface.md
8 | package.md
9 |
--------------------------------------------------------------------------------
/docs/en/switch_language.md:
--------------------------------------------------------------------------------
1 | ## English
2 |
3 | ## 简体中文
4 |
--------------------------------------------------------------------------------
/docs/en/user_guides/index.rst:
--------------------------------------------------------------------------------
1 | Train & Test
2 | **************
3 |
4 | .. toctree::
5 | :maxdepth: 1
6 |
7 | 1_config.md
8 | 2_dataset_prepare.md
9 | 3_inference.md
10 | 4_train_test.md
11 |
12 | Useful Tools
13 | *************
14 |
15 | .. toctree::
16 | :maxdepth: 2
17 |
18 | visualization.md
19 | useful_tools.md
20 | deployment.md
21 | visualization_feature_map.md
22 |
--------------------------------------------------------------------------------
/docs/zh_cn/.readthedocs.yaml:
--------------------------------------------------------------------------------
1 | version: 2
2 |
3 | build:
4 | os: ubuntu-22.04
5 | tools:
6 | python: "3.8"
7 |
8 | formats:
9 | - epub
10 |
11 | sphinx:
12 | configuration: docs/zh_cn/conf.py
13 |
14 | python:
15 | install:
16 | - requirements: requirements/docs.txt
17 | - requirements: requirements/readthedocs.txt
18 |
--------------------------------------------------------------------------------
/docs/zh_cn/_static/css/readthedocs.css:
--------------------------------------------------------------------------------
1 | .header-logo {
2 | background-image: url("../images/mmsegmentation.png");
3 | background-size: 201px 40px;
4 | height: 40px;
5 | width: 201px;
6 | }
7 |
--------------------------------------------------------------------------------
/docs/zh_cn/_static/images/mmsegmentation.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/zhuqinfeng1999/Samba/229687b62ef4ddbda5835e5f3f0b1182e8ab4a49/docs/zh_cn/_static/images/mmsegmentation.png
--------------------------------------------------------------------------------
/docs/zh_cn/imgs/zhihu_qrcode.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/zhuqinfeng1999/Samba/229687b62ef4ddbda5835e5f3f0b1182e8ab4a49/docs/zh_cn/imgs/zhihu_qrcode.jpg
--------------------------------------------------------------------------------
/docs/zh_cn/migration/index.rst:
--------------------------------------------------------------------------------
1 | 迁移
2 | ***************
3 |
4 | .. toctree::
5 | :maxdepth: 1
6 |
7 | interface.md
8 | package.md
9 |
--------------------------------------------------------------------------------
/docs/zh_cn/switch_language.md:
--------------------------------------------------------------------------------
1 | ## English
2 |
3 | ## 简体中文
4 |
--------------------------------------------------------------------------------
/docs/zh_cn/user_guides/index.rst:
--------------------------------------------------------------------------------
1 | 训练 & 测试
2 | **************
3 |
4 | .. toctree::
5 | :maxdepth: 1
6 |
7 | 1_config.md
8 | 2_dataset_prepare.md
9 | 3_inference.md
10 | 4_train_test.md
11 |
12 | 实用工具
13 | *************
14 |
15 | .. toctree::
16 | :maxdepth: 2
17 |
18 | visualization.md
19 | useful_tools.md
20 | deployment.md
21 | visualization_feature_map.md
22 |
--------------------------------------------------------------------------------
/mmseg/apis/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .inference import inference_model, init_model, show_result_pyplot
3 | from .mmseg_inferencer import MMSegInferencer
4 | from .remote_sense_inferencer import RSImage, RSInferencer
5 |
6 | __all__ = [
7 | 'init_model', 'inference_model', 'show_result_pyplot', 'MMSegInferencer',
8 | 'RSInferencer', 'RSImage'
9 | ]
10 |
--------------------------------------------------------------------------------
/mmseg/engine/hooks/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .visualization_hook import SegVisualizationHook
3 |
4 | __all__ = ['SegVisualizationHook']
5 |
--------------------------------------------------------------------------------
/mmseg/engine/optimizers/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .force_default_constructor import ForceDefaultOptimWrapperConstructor
3 | from .layer_decay_optimizer_constructor import (
4 | LayerDecayOptimizerConstructor, LearningRateDecayOptimizerConstructor)
5 |
6 | __all__ = [
7 | 'LearningRateDecayOptimizerConstructor', 'LayerDecayOptimizerConstructor',
8 | 'ForceDefaultOptimWrapperConstructor'
9 | ]
10 |
--------------------------------------------------------------------------------
/mmseg/engine/schedulers/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .poly_ratio_scheduler import PolyLRRatio
3 |
4 | __all__ = ['PolyLRRatio']
5 |
--------------------------------------------------------------------------------
/mmseg/evaluation/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .metrics import CityscapesMetric, DepthMetric, IoUMetric
3 |
4 | __all__ = ['IoUMetric', 'CityscapesMetric', 'DepthMetric']
5 |
--------------------------------------------------------------------------------
/mmseg/evaluation/metrics/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .citys_metric import CityscapesMetric
3 | from .depth_metric import DepthMetric
4 | from .iou_metric import IoUMetric
5 |
6 | __all__ = ['IoUMetric', 'CityscapesMetric', 'DepthMetric']
7 |
--------------------------------------------------------------------------------
/mmseg/models/assigners/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .base_assigner import BaseAssigner
3 | from .hungarian_assigner import HungarianAssigner
4 | from .match_cost import ClassificationCost, CrossEntropyLossCost, DiceCost
5 |
6 | __all__ = [
7 | 'BaseAssigner',
8 | 'HungarianAssigner',
9 | 'ClassificationCost',
10 | 'CrossEntropyLossCost',
11 | 'DiceCost',
12 | ]
13 |
--------------------------------------------------------------------------------
/mmseg/models/necks/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .featurepyramid import Feature2Pyramid
3 | from .fpn import FPN
4 | from .ic_neck import ICNeck
5 | from .jpu import JPU
6 | from .mla_neck import MLANeck
7 | from .multilevel_neck import MultiLevelNeck
8 |
9 | __all__ = [
10 | 'FPN', 'MultiLevelNeck', 'MLANeck', 'ICNeck', 'JPU', 'Feature2Pyramid'
11 | ]
12 |
--------------------------------------------------------------------------------
/mmseg/models/text_encoder/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .clip_text_encoder import CLIPTextEncoder
3 |
4 | __all__ = ['CLIPTextEncoder']
5 |
--------------------------------------------------------------------------------
/mmseg/structures/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .sampler import BasePixelSampler, OHEMPixelSampler, build_pixel_sampler
3 | from .seg_data_sample import SegDataSample
4 |
5 | __all__ = [
6 | 'SegDataSample', 'BasePixelSampler', 'OHEMPixelSampler',
7 | 'build_pixel_sampler'
8 | ]
9 |
--------------------------------------------------------------------------------
/mmseg/structures/sampler/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .base_pixel_sampler import BasePixelSampler
3 | from .builder import build_pixel_sampler
4 | from .ohem_pixel_sampler import OHEMPixelSampler
5 |
6 | __all__ = ['build_pixel_sampler', 'BasePixelSampler', 'OHEMPixelSampler']
7 |
--------------------------------------------------------------------------------
/mmseg/structures/sampler/base_pixel_sampler.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from abc import ABCMeta, abstractmethod
3 |
4 |
5 | class BasePixelSampler(metaclass=ABCMeta):
6 | """Base class of pixel sampler."""
7 |
8 | def __init__(self, **kwargs):
9 | pass
10 |
11 | @abstractmethod
12 | def sample(self, seg_logit, seg_label):
13 | """Placeholder for sample function."""
14 |
--------------------------------------------------------------------------------
/mmseg/utils/bpe_simple_vocab_16e6.txt.gz:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/zhuqinfeng1999/Samba/229687b62ef4ddbda5835e5f3f0b1182e8ab4a49/mmseg/utils/bpe_simple_vocab_16e6.txt.gz
--------------------------------------------------------------------------------
/mmseg/visualization/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .local_visualizer import SegLocalVisualizer
3 |
4 | __all__ = ['SegLocalVisualizer']
5 |
--------------------------------------------------------------------------------
/projects/Adabins/backbones/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .adabins_backbone import AdabinsBackbone
3 |
4 | __all__ = ['AdabinsBackbone']
5 |
--------------------------------------------------------------------------------
/projects/Adabins/decode_head/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .adabins_head import AdabinsHead
3 |
4 | __all__ = ['AdabinsHead']
5 |
--------------------------------------------------------------------------------
/projects/CAT-Seg/cat_seg/__init__.py:
--------------------------------------------------------------------------------
1 | from .models import * # noqa: F401,F403
2 | from .utils import * # noqa: F401,F403
3 |
--------------------------------------------------------------------------------
/projects/CAT-Seg/cat_seg/models/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .cat_aggregator import (AggregatorLayer, CATSegAggregator,
3 | ClassAggregateLayer, SpatialAggregateLayer)
4 | from .cat_head import CATSegHead
5 | from .clip_ovseg import CLIPOVCATSeg
6 |
7 | __all__ = [
8 | 'AggregatorLayer', 'CATSegAggregator', 'ClassAggregateLayer',
9 | 'SpatialAggregateLayer', 'CATSegHead', 'CLIPOVCATSeg'
10 | ]
11 |
--------------------------------------------------------------------------------
/projects/CAT-Seg/cat_seg/utils/bpe_vocab/bpe_simple_vocab_16e6.txt.gz:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/zhuqinfeng1999/Samba/229687b62ef4ddbda5835e5f3f0b1182e8ab4a49/projects/CAT-Seg/cat_seg/utils/bpe_vocab/bpe_simple_vocab_16e6.txt.gz
--------------------------------------------------------------------------------
/projects/CAT-Seg/utils/__init__.py:
--------------------------------------------------------------------------------
1 | from .clip_templates import (IMAGENET_TEMPLATES, IMAGENET_TEMPLATES_SELECT,
2 | IMAGENET_TEMPLATES_SELECT_CLIP, ViLD_templates)
3 |
4 | __all__ = [
5 | 'IMAGENET_TEMPLATES', 'IMAGENET_TEMPLATES_SELECT',
6 | 'IMAGENET_TEMPLATES_SELECT_CLIP', 'ViLD_templates'
7 | ]
8 |
--------------------------------------------------------------------------------
/projects/example_project/configs/fcn_dummy-r50-d8_4xb2-40k_cityscapes-512x1024.py:
--------------------------------------------------------------------------------
1 | _base_ = ['mmseg::fcn/fcn_r50-d8_4xb2-40k_cityscapes-512x1024.py']
2 |
3 | custom_imports = dict(imports=['dummy'])
4 |
5 | crop_size = (512, 1024)
6 | data_preprocessor = dict(size=crop_size)
7 | model = dict(
8 | data_preprocessor=data_preprocessor, backbone=dict(type='DummyResNet'))
9 |
--------------------------------------------------------------------------------
/projects/example_project/dummy/__init__.py:
--------------------------------------------------------------------------------
1 | from .dummy_resnet import DummyResNet
2 |
3 | __all__ = ['DummyResNet']
4 |
--------------------------------------------------------------------------------
/projects/hssn/decode_head/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .sep_aspp_contrast_head import DepthwiseSeparableASPPContrastHead
3 |
4 | __all__ = ['DepthwiseSeparableASPPContrastHead']
5 |
--------------------------------------------------------------------------------
/projects/hssn/losses/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .hiera_triplet_loss_cityscape import HieraTripletLossCityscape
3 |
4 | __all__ = ['HieraTripletLossCityscape']
5 |
--------------------------------------------------------------------------------
/projects/isnet/decode_heads/__init__.py:
--------------------------------------------------------------------------------
1 | from .isnet_head import ISNetHead
2 |
3 | __all__ = ['ISNetHead']
4 |
--------------------------------------------------------------------------------
/projects/medical/2d_image/fluorescein_angriogram/vampire/datasets/__init__.py:
--------------------------------------------------------------------------------
1 | from .vampire_dataset import VampireDataset
2 |
3 | __all__ = ['VampireDataset']
4 |
--------------------------------------------------------------------------------
/projects/medical/2d_image/histopathology/conic2022_seg/conic2022_seg_dataset.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/zhuqinfeng1999/Samba/229687b62ef4ddbda5835e5f3f0b1182e8ab4a49/projects/medical/2d_image/histopathology/conic2022_seg/conic2022_seg_dataset.png
--------------------------------------------------------------------------------
/projects/medical/2d_image/infrared_reflectance_imaging/ravir/datasets/__init__.py:
--------------------------------------------------------------------------------
1 | from .ravir_dataset import RAVIRDataset
2 |
3 | __all__ = ['RAVIRDataset']
4 |
--------------------------------------------------------------------------------
/projects/pp_mobileseg/backbones/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .strideformer import StrideFormer
3 |
4 | __all__ = ['StrideFormer']
5 |
--------------------------------------------------------------------------------
/projects/pp_mobileseg/decode_head/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .pp_mobileseg_head import PPMobileSegHead
3 |
4 | __all__ = [
5 | 'PPMobileSegHead',
6 | ]
7 |
--------------------------------------------------------------------------------
/projects/sam_inference_demo/sam/__init__.py:
--------------------------------------------------------------------------------
1 | from .modeling import * # noqa
2 | from .utils import * # noqa
3 |
--------------------------------------------------------------------------------
/projects/sam_inference_demo/sam/utils/__init__.py:
--------------------------------------------------------------------------------
1 | from .amg import * # noqa: F403 F401
2 | from .transforms import ResizeLongestSide # noqa: F403 F401
3 |
--------------------------------------------------------------------------------
/projects/van/backbones/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 | from .van import VAN
3 |
4 | __all__ = ['VAN']
5 |
--------------------------------------------------------------------------------
/projects/van/configs/van/van-b0_fpn_8xb4-40k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './van-b2_fpn_8xb4-40k_ade20k-512x512.py'
2 | ckpt_path = 'https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b0_3rdparty_20230522-956f5e0d.pth' # noqa
3 | model = dict(
4 | backbone=dict(
5 | embed_dims=[32, 64, 160, 256],
6 | depths=[3, 3, 5, 2],
7 | init_cfg=dict(type='Pretrained', checkpoint=ckpt_path)),
8 | neck=dict(in_channels=[32, 64, 160, 256]))
9 |
--------------------------------------------------------------------------------
/projects/van/configs/van/van-b1_fpn_8xb4-40k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './van-b2_fpn_8xb4-40k_ade20k-512x512.py'
2 | ckpt_path = 'https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b1_3rdparty_20230522-3adb117f.pth' # noqa
3 | model = dict(
4 | backbone=dict(
5 | depths=[2, 2, 4, 2],
6 | init_cfg=dict(type='Pretrained', checkpoint=ckpt_path)))
7 |
--------------------------------------------------------------------------------
/projects/van/configs/van/van-b3_upernet_4xb2-160k_ade20k-512x512.py:
--------------------------------------------------------------------------------
1 | _base_ = './van-b2_upernet_4xb2-160k_ade20k-512x512.py'
2 | ckpt_path = 'https://download.openmmlab.com/mmsegmentation/v0.5/van_3rdparty/van-b3_3rdparty_20230522-a184e051.pth' # noqa
3 | model = dict(
4 | type='EncoderDecoder',
5 | backbone=dict(
6 | depths=[3, 5, 27, 3],
7 | init_cfg=dict(type='Pretrained', checkpoint=ckpt_path),
8 | drop_path_rate=0.3))
9 |
--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
1 | -r requirements/optional.txt
2 | -r requirements/runtime.txt
3 | -r requirements/tests.txt
4 | -r requirements/multimodal.txt
5 |
--------------------------------------------------------------------------------
/requirements/albu.txt:
--------------------------------------------------------------------------------
1 | albumentations>=0.3.2 --no-binary qudida,albumentations
2 |
--------------------------------------------------------------------------------
/requirements/docs.txt:
--------------------------------------------------------------------------------
1 | docutils==0.16.0
2 | myst-parser
3 | -e git+https://github.com/open-mmlab/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
4 | sphinx==4.0.2
5 | sphinx_copybutton
6 | sphinx_markdown_tables
7 | urllib3<2.0.0
8 |
--------------------------------------------------------------------------------
/requirements/mminstall.txt:
--------------------------------------------------------------------------------
1 | mmcv>=2.0.0rc4,<2.2.0
2 | mmengine>=0.5.0,<1.0.0
3 |
--------------------------------------------------------------------------------
/requirements/multimodal.txt:
--------------------------------------------------------------------------------
1 | ftfy
2 | regex
3 |
--------------------------------------------------------------------------------
/requirements/readthedocs.txt:
--------------------------------------------------------------------------------
1 | mmcv>=2.0.0rc1,<2.1.0
2 | mmengine>=0.4.0,<1.0.0
3 | prettytable
4 | scipy
5 | torch
6 | torchvision
7 |
--------------------------------------------------------------------------------
/requirements/runtime.txt:
--------------------------------------------------------------------------------
1 | matplotlib
2 | numpy
3 | packaging
4 | prettytable
5 | scipy
6 |
--------------------------------------------------------------------------------
/requirements/tests.txt:
--------------------------------------------------------------------------------
1 | codecov
2 | flake8
3 | ftfy
4 | interrogate
5 | pytest
6 | regex
7 | xdoctest>=0.10.0
8 | yapf
9 |
--------------------------------------------------------------------------------
/resources/3dogs.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/zhuqinfeng1999/Samba/229687b62ef4ddbda5835e5f3f0b1182e8ab4a49/resources/3dogs.jpg
--------------------------------------------------------------------------------
/resources/3dogs_mask.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/zhuqinfeng1999/Samba/229687b62ef4ddbda5835e5f3f0b1182e8ab4a49/resources/3dogs_mask.png
--------------------------------------------------------------------------------
/resources/cascade_encoder_decoder_dataflow.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/zhuqinfeng1999/Samba/229687b62ef4ddbda5835e5f3f0b1182e8ab4a49/resources/cascade_encoder_decoder_dataflow.png
--------------------------------------------------------------------------------
/resources/encoder_decoder_dataflow.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/zhuqinfeng1999/Samba/229687b62ef4ddbda5835e5f3f0b1182e8ab4a49/resources/encoder_decoder_dataflow.png
--------------------------------------------------------------------------------
/resources/miaomiao_qrcode.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/zhuqinfeng1999/Samba/229687b62ef4ddbda5835e5f3f0b1182e8ab4a49/resources/miaomiao_qrcode.jpg
--------------------------------------------------------------------------------
/resources/mmseg-logo.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/zhuqinfeng1999/Samba/229687b62ef4ddbda5835e5f3f0b1182e8ab4a49/resources/mmseg-logo.png
--------------------------------------------------------------------------------
/resources/seg_demo.gif:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/zhuqinfeng1999/Samba/229687b62ef4ddbda5835e5f3f0b1182e8ab4a49/resources/seg_demo.gif
--------------------------------------------------------------------------------
/resources/test_step.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/zhuqinfeng1999/Samba/229687b62ef4ddbda5835e5f3f0b1182e8ab4a49/resources/test_step.png
--------------------------------------------------------------------------------
/resources/train_step.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/zhuqinfeng1999/Samba/229687b62ef4ddbda5835e5f3f0b1182e8ab4a49/resources/train_step.png
--------------------------------------------------------------------------------
/tests/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 |
--------------------------------------------------------------------------------
/tests/test_models/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 |
--------------------------------------------------------------------------------
/tests/test_models/test_backbones/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 |
--------------------------------------------------------------------------------
/tests/test_models/test_heads/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 |
--------------------------------------------------------------------------------
/tests/test_models/test_necks/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 |
--------------------------------------------------------------------------------
/tests/test_models/test_segmentors/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 |
--------------------------------------------------------------------------------
/tests/test_models/test_utils/__init__.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) OpenMMLab. All rights reserved.
2 |
--------------------------------------------------------------------------------