├── LICENSE ├── README.md ├── __init__.py ├── configs ├── _base_ │ ├── datasets │ │ ├── cityscapes_detection.py │ │ ├── cityscapes_instance.py │ │ ├── coco_detection.py │ │ ├── coco_instance.py │ │ ├── coco_instance_semantic.py │ │ ├── coco_panoptic.py │ │ ├── deepfashion.py │ │ ├── lvis_v0.5_instance.py │ │ ├── lvis_v1_instance.py │ │ ├── objects365v1_detection.py │ │ ├── objects365v2_detection.py │ │ ├── openimages_detection.py │ │ ├── semi_coco_detection.py │ │ ├── voc0712.py │ │ └── wider_face.py │ ├── default_runtime.py │ ├── models │ │ ├── cascade-mask-rcnn_r50_fpn.py │ │ ├── cascade-rcnn_r50_fpn.py │ │ ├── fast-rcnn_r50_fpn.py │ │ ├── faster-rcnn_r50-caffe-c4.py │ │ ├── faster-rcnn_r50-caffe-dc5.py │ │ ├── faster-rcnn_r50_fpn.py │ │ ├── mask-rcnn_r50-caffe-c4.py │ │ ├── mask-rcnn_r50_fpn.py │ │ ├── retinanet_r50_fpn.py │ │ ├── rpn_r50-caffe-c4.py │ │ ├── rpn_r50_fpn.py │ │ └── ssd300.py │ └── schedules │ │ ├── schedule_1x.py │ │ ├── schedule_20e.py │ │ └── schedule_2x.py ├── albu_example │ ├── README.md │ ├── mask-rcnn_r50_fpn_albu-1x_coco.py │ └── metafile.yml ├── atss │ ├── README.md │ ├── atss_r101_fpn_1x_coco.py │ ├── atss_r101_fpn_8xb8-amp-lsj-200e_coco.py │ ├── atss_r18_fpn_8xb8-amp-lsj-200e_coco.py │ ├── atss_r50_fpn_1x_coco.py │ ├── atss_r50_fpn_8xb8-amp-lsj-200e_coco.py │ └── metafile.yml ├── autoassign │ ├── README.md │ ├── autoassign_r50-caffe_fpn_1x_coco.py │ └── metafile.yml ├── boxinst │ ├── README.md │ ├── boxinst_r101_fpn_ms-90k_coco.py │ ├── boxinst_r50_fpn_ms-90k_coco.py │ └── metafile.yml ├── carafe │ ├── README.md │ ├── faster-rcnn_r50_fpn-carafe_1x_coco.py │ ├── mask-rcnn_r50_fpn-carafe_1x_coco.py │ └── metafile.yml ├── cascade_rcnn │ ├── README.md │ ├── cascade-mask-rcnn_r101-caffe_fpn_1x_coco.py │ ├── cascade-mask-rcnn_r101-caffe_fpn_ms-3x_coco.py │ ├── cascade-mask-rcnn_r101_fpn_1x_coco.py │ ├── cascade-mask-rcnn_r101_fpn_20e_coco.py │ ├── cascade-mask-rcnn_r101_fpn_ms-3x_coco.py │ ├── cascade-mask-rcnn_r50-caffe_fpn_1x_coco.py │ ├── cascade-mask-rcnn_r50-caffe_fpn_ms-3x_coco.py │ ├── cascade-mask-rcnn_r50_fpn_1x_coco.py │ ├── cascade-mask-rcnn_r50_fpn_20e_coco.py │ ├── cascade-mask-rcnn_r50_fpn_ms-3x_coco.py │ ├── cascade-mask-rcnn_x101-32x4d_fpn_1x_coco.py │ ├── cascade-mask-rcnn_x101-32x4d_fpn_20e_coco.py │ ├── cascade-mask-rcnn_x101-32x4d_fpn_ms-3x_coco.py │ ├── cascade-mask-rcnn_x101-32x8d_fpn_ms-3x_coco.py │ ├── cascade-mask-rcnn_x101-64x4d_fpn_1x_coco.py │ ├── cascade-mask-rcnn_x101-64x4d_fpn_20e_coco.py │ ├── cascade-mask-rcnn_x101-64x4d_fpn_ms-3x_coco.py │ ├── cascade-rcnn_r101-caffe_fpn_1x_coco.py │ ├── cascade-rcnn_r101_fpn_1x_coco.py │ ├── cascade-rcnn_r101_fpn_20e_coco.py │ ├── cascade-rcnn_r101_fpn_8xb8-amp-lsj-200e_coco.py │ ├── cascade-rcnn_r18_fpn_8xb8-amp-lsj-200e_coco.py │ ├── cascade-rcnn_r50-caffe_fpn_1x_coco.py │ ├── cascade-rcnn_r50_fpn_1x_coco.py │ ├── cascade-rcnn_r50_fpn_20e_coco.py │ ├── cascade-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py │ ├── cascade-rcnn_x101-32x4d_fpn_1x_coco.py │ ├── cascade-rcnn_x101-32x4d_fpn_20e_coco.py │ ├── cascade-rcnn_x101-64x4d_fpn_1x_coco.py │ ├── cascade-rcnn_x101_64x4d_fpn_20e_coco.py │ └── metafile.yml ├── cascade_rpn │ ├── README.md │ ├── cascade-rpn_fast-rcnn_r50-caffe_fpn_1x_coco.py │ ├── cascade-rpn_faster-rcnn_r50-caffe_fpn_1x_coco.py │ ├── cascade-rpn_r50-caffe_fpn_1x_coco.py │ └── metafile.yml ├── centernet │ ├── README.md │ ├── centernet-update_r101_fpn_8xb8-amp-lsj-200e_coco.py │ ├── centernet-update_r18_fpn_8xb8-amp-lsj-200e_coco.py │ ├── centernet-update_r50-caffe_fpn_ms-1x_coco.py │ ├── centernet-update_r50_fpn_8xb8-amp-lsj-200e_coco.py │ ├── centernet_r18-dcnv2_8xb16-crop512-140e_coco.py │ ├── centernet_r18_8xb16-crop512-140e_coco.py │ ├── centernet_tta.py │ └── metafile.yml ├── centripetalnet │ ├── README.md │ ├── centripetalnet_hourglass104_16xb6-crop511-210e-mstest_coco.py │ └── metafile.yml ├── cityscapes │ ├── README.md │ ├── faster-rcnn_r50_fpn_1x_cityscapes.py │ └── mask-rcnn_r50_fpn_1x_cityscapes.py ├── common │ ├── lsj-100e_coco-detection.py │ ├── lsj-100e_coco-instance.py │ ├── lsj-200e_coco-detection.py │ ├── lsj-200e_coco-instance.py │ ├── ms-90k_coco.py │ ├── ms-poly-90k_coco-instance.py │ ├── ms-poly_3x_coco-instance.py │ ├── ms_3x_coco-instance.py │ ├── ms_3x_coco.py │ ├── ssj_270k_coco-instance.py │ └── ssj_scp_270k_coco-instance.py ├── condinst │ ├── README.md │ ├── condinst_r50_fpn_ms-poly-90k_coco_instance.py │ └── metafile.yml ├── conditional_detr │ ├── README.md │ ├── conditional-detr_r50_8xb2-50e_coco.py │ └── metafile.yml ├── convnext │ ├── README.md │ ├── cascade-mask-rcnn_convnext-s-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco.py │ ├── cascade-mask-rcnn_convnext-t-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco.py │ ├── mask-rcnn_convnext-t-p4-w7_fpn_amp-ms-crop-3x_coco.py │ └── metafile.yml ├── cornernet │ ├── README.md │ ├── cornernet_hourglass104_10xb5-crop511-210e-mstest_coco.py │ ├── cornernet_hourglass104_32xb3-210e-mstest_coco.py │ ├── cornernet_hourglass104_8xb6-210e-mstest_coco.py │ └── metafile.yml ├── crowddet │ ├── README.md │ ├── crowddet-rcnn_r50_fpn_8xb2-30e_crowdhuman.py │ ├── crowddet-rcnn_refine_r50_fpn_8xb2-30e_crowdhuman.py │ └── metafile.yml ├── dab_detr │ ├── README.md │ ├── dab-detr_r50_8xb2-50e_coco.py │ └── metafile.yml ├── dcn │ ├── README.md │ ├── cascade-mask-rcnn_r101-dconv-c3-c5_fpn_1x_coco.py │ ├── cascade-mask-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py │ ├── cascade-mask-rcnn_x101-32x4d-dconv-c3-c5_fpn_1x_coco.py │ ├── cascade-rcnn_r101-dconv-c3-c5_fpn_1x_coco.py │ ├── cascade-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py │ ├── faster-rcnn_r101-dconv-c3-c5_fpn_1x_coco.py │ ├── faster-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py │ ├── faster-rcnn_r50_fpn_dpool_1x_coco.py │ ├── faster-rcnn_x101-32x4d-dconv-c3-c5_fpn_1x_coco.py │ ├── mask-rcnn_r101-dconv-c3-c5_fpn_1x_coco.py │ ├── mask-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py │ ├── mask-rcnn_r50-dconv-c3-c5_fpn_amp-1x_coco.py │ └── metafile.yml ├── dcnv2 │ ├── README.md │ ├── faster-rcnn_r50-mdconv-c3-c5_fpn_1x_coco.py │ ├── faster-rcnn_r50-mdconv-group4-c3-c5_fpn_1x_coco.py │ ├── faster-rcnn_r50_fpn_mdpool_1x_coco.py │ ├── mask-rcnn_r50-mdconv-c3-c5_fpn_1x_coco.py │ ├── mask-rcnn_r50-mdconv-c3-c5_fpn_amp-1x_coco.py │ └── metafile.yml ├── ddod │ ├── README.md │ ├── ddod_r50_fpn_1x_coco.py │ └── metafile.yml ├── deepfashion │ ├── README.md │ └── mask-rcnn_r50_fpn_15e_deepfashion.py ├── deformable_detr │ ├── README.md │ ├── deformable-detr-refine-twostage_r50_16xb2-50e_coco.py │ ├── deformable-detr-refine_r50_16xb2-50e_coco.py │ ├── deformable-detr_r50_16xb2-50e_coco.py │ └── metafile.yml ├── detectors │ ├── README.md │ ├── cascade-rcnn_r50-rfp_1x_coco.py │ ├── cascade-rcnn_r50-sac_1x_coco.py │ ├── detectors_cascade-rcnn_r50_1x_coco.py │ ├── detectors_htc-r101_20e_coco.py │ ├── detectors_htc-r50_1x_coco.py │ ├── htc_r50-rfp_1x_coco.py │ ├── htc_r50-sac_1x_coco.py │ └── metafile.yml ├── detr │ ├── README.md │ ├── detr_r101_8xb2-500e_coco.py │ ├── detr_r18_8xb2-500e_coco.py │ ├── detr_r50_8xb2-150e_coco.py │ ├── detr_r50_8xb2-500e_coco.py │ └── metafile.yml ├── dino │ ├── README.md │ ├── dino-4scale_r50_8xb2-12e_coco.py │ ├── dino-4scale_r50_8xb2-24e_coco.py │ ├── dino-4scale_r50_8xb2-36e_coco.py │ ├── dino-5scale_swin-l_8xb2-12e_coco.py │ ├── dino-5scale_swin-l_8xb2-36e_coco.py │ └── metafile.yml ├── double_heads │ ├── README.md │ ├── dh-faster-rcnn_r50_fpn_1x_coco.py │ └── metafile.yml ├── dyhead │ ├── README.md │ ├── atss_r50-caffe_fpn_dyhead_1x_coco.py │ ├── atss_r50_fpn_dyhead_1x_coco.py │ ├── atss_swin-l-p4-w12_fpn_dyhead_ms-2x_coco.py │ └── metafile.yml ├── dynamic_rcnn │ ├── README.md │ ├── dynamic-rcnn_r50_fpn_1x_coco.py │ └── metafile.yml ├── efficientnet │ ├── README.md │ ├── metafile.yml │ └── retinanet_effb3_fpn_8xb4-crop896-1x_coco.py ├── empirical_attention │ ├── README.md │ ├── faster-rcnn_r50-attn0010-dcn_fpn_1x_coco.py │ ├── faster-rcnn_r50-attn0010_fpn_1x_coco.py │ ├── faster-rcnn_r50-attn1111-dcn_fpn_1x_coco.py │ ├── faster-rcnn_r50-attn1111_fpn_1x_coco.py │ └── metafile.yml ├── fast_rcnn │ ├── README.md │ ├── fast-rcnn_r101-caffe_fpn_1x_coco.py │ ├── fast-rcnn_r101_fpn_1x_coco.py │ ├── fast-rcnn_r101_fpn_2x_coco.py │ ├── fast-rcnn_r50-caffe_fpn_1x_coco.py │ ├── fast-rcnn_r50_fpn_1x_coco.py │ └── fast-rcnn_r50_fpn_2x_coco.py ├── faster_rcnn │ ├── README.md │ ├── faster-rcnn_r101-caffe_fpn_1x_coco.py │ ├── faster-rcnn_r101-caffe_fpn_ms-3x_coco.py │ ├── faster-rcnn_r101_fpn_1x_coco.py │ ├── faster-rcnn_r101_fpn_2x_coco.py │ ├── faster-rcnn_r101_fpn_8xb8-amp-lsj-200e_coco.py │ ├── faster-rcnn_r101_fpn_ms-3x_coco.py │ ├── faster-rcnn_r18_fpn_8xb8-amp-lsj-200e_coco.py │ ├── faster-rcnn_r50-caffe-c4_ms-1x_coco.py │ ├── faster-rcnn_r50-caffe-dc5_1x_coco.py │ ├── faster-rcnn_r50-caffe-dc5_ms-1x_coco.py │ ├── faster-rcnn_r50-caffe-dc5_ms-3x_coco.py │ ├── faster-rcnn_r50-caffe_c4-1x_coco.py │ ├── faster-rcnn_r50-caffe_fpn_1x_coco.py │ ├── faster-rcnn_r50-caffe_fpn_90k_coco.py │ ├── faster-rcnn_r50-caffe_fpn_ms-1x_coco-person-bicycle-car.py │ ├── faster-rcnn_r50-caffe_fpn_ms-1x_coco-person.py │ ├── faster-rcnn_r50-caffe_fpn_ms-1x_coco.py │ ├── faster-rcnn_r50-caffe_fpn_ms-2x_coco.py │ ├── faster-rcnn_r50-caffe_fpn_ms-3x_coco.py │ ├── faster-rcnn_r50-caffe_fpn_ms-90k_coco.py │ ├── faster-rcnn_r50-tnr-pre_fpn_1x_coco.py │ ├── faster-rcnn_r50_fpn_1x_coco.py │ ├── faster-rcnn_r50_fpn_2x_coco.py │ ├── faster-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py │ ├── faster-rcnn_r50_fpn_amp-1x_coco.py │ ├── faster-rcnn_r50_fpn_bounded-iou_1x_coco.py │ ├── faster-rcnn_r50_fpn_ciou_1x_coco.py │ ├── faster-rcnn_r50_fpn_fcos-rpn_1x_coco.py │ ├── faster-rcnn_r50_fpn_giou_1x_coco.py │ ├── faster-rcnn_r50_fpn_iou_1x_coco.py │ ├── faster-rcnn_r50_fpn_ms-3x_coco.py │ ├── faster-rcnn_r50_fpn_ohem_1x_coco.py │ ├── faster-rcnn_r50_fpn_soft-nms_1x_coco.py │ ├── faster-rcnn_x101-32x4d_fpn_1x_coco.py │ ├── faster-rcnn_x101-32x4d_fpn_2x_coco.py │ ├── faster-rcnn_x101-32x4d_fpn_ms-3x_coco.py │ ├── faster-rcnn_x101-32x8d_fpn_ms-3x_coco.py │ ├── faster-rcnn_x101-64x4d_fpn_1x_coco.py │ ├── faster-rcnn_x101-64x4d_fpn_2x_coco.py │ ├── faster-rcnn_x101-64x4d_fpn_ms-3x_coco.py │ └── metafile.yml ├── fcos │ ├── README.md │ ├── fcos_r101-caffe_fpn_gn-head-1x_coco.py │ ├── fcos_r101-caffe_fpn_gn-head_ms-640-800-2x_coco.py │ ├── fcos_r101_fpn_gn-head-center-normbbox-centeronreg-giou_8xb8-amp-lsj-200e_coco.py │ ├── fcos_r18_fpn_gn-head-center-normbbox-centeronreg-giou_8xb8-amp-lsj-200e_coco.py │ ├── fcos_r50-caffe_fpn_gn-head-center-normbbox-centeronreg-giou_1x_coco.py │ ├── fcos_r50-caffe_fpn_gn-head-center_1x_coco.py │ ├── fcos_r50-caffe_fpn_gn-head_1x_coco.py │ ├── fcos_r50-caffe_fpn_gn-head_4xb4-1x_coco.py │ ├── fcos_r50-caffe_fpn_gn-head_ms-640-800-2x_coco.py │ ├── fcos_r50-dcn-caffe_fpn_gn-head-center-normbbox-centeronreg-giou_1x_coco.py │ ├── fcos_r50_fpn_gn-head-center-normbbox-centeronreg-giou_8xb8-amp-lsj-200e_coco.py │ ├── fcos_x101-64x4d_fpn_gn-head_ms-640-800-2x_coco.py │ └── metafile.yml ├── foveabox │ ├── README.md │ ├── fovea_r101_fpn_4xb4-1x_coco.py │ ├── fovea_r101_fpn_4xb4-2x_coco.py │ ├── fovea_r101_fpn_gn-head-align_4xb4-2x_coco.py │ ├── fovea_r101_fpn_gn-head-align_ms-640-800-4xb4-2x_coco.py │ ├── fovea_r50_fpn_4xb4-1x_coco.py │ ├── fovea_r50_fpn_4xb4-2x_coco.py │ ├── fovea_r50_fpn_gn-head-align_4xb4-2x_coco.py │ ├── fovea_r50_fpn_gn-head-align_ms-640-800-4xb4-2x_coco.py │ └── metafile.yml ├── fpg │ ├── README.md │ ├── faster-rcnn_r50_fpg-chn128_crop640-50e_coco.py │ ├── faster-rcnn_r50_fpg_crop640-50e_coco.py │ ├── faster-rcnn_r50_fpn_crop640-50e_coco.py │ ├── mask-rcnn_r50_fpg-chn128_crop640-50e_coco.py │ ├── mask-rcnn_r50_fpg_crop640-50e_coco.py │ ├── mask-rcnn_r50_fpn_crop640-50e_coco.py │ ├── metafile.yml │ ├── retinanet_r50_fpg-chn128_crop640_50e_coco.py │ └── retinanet_r50_fpg_crop640_50e_coco.py ├── free_anchor │ ├── README.md │ ├── freeanchor_r101_fpn_1x_coco.py │ ├── freeanchor_r50_fpn_1x_coco.py │ ├── freeanchor_x101-32x4d_fpn_1x_coco.py │ └── metafile.yml ├── fsaf │ ├── README.md │ ├── fsaf_r101_fpn_1x_coco.py │ ├── fsaf_r50_fpn_1x_coco.py │ ├── fsaf_x101-64x4d_fpn_1x_coco.py │ └── metafile.yml ├── gcnet │ ├── README.md │ ├── cascade-mask-rcnn_x101-32x4d-syncbn-dconv-c3-c5-r16-gcb-c3-c5_fpn_1x_coco.py │ ├── cascade-mask-rcnn_x101-32x4d-syncbn-dconv-c3-c5-r4-gcb-c3-c5_fpn_1x_coco.py │ ├── cascade-mask-rcnn_x101-32x4d-syncbn-dconv-c3-c5_fpn_1x_coco.py │ ├── cascade-mask-rcnn_x101-32x4d-syncbn-r16-gcb-c3-c5_fpn_1x_coco.py │ ├── cascade-mask-rcnn_x101-32x4d-syncbn-r4-gcb-c3-c5_fpn_1x_coco.py │ ├── cascade-mask-rcnn_x101-32x4d-syncbn_fpn_1x_coco.py │ ├── mask-rcnn_r101-gcb-r16-c3-c5_fpn_1x_coco.py │ ├── mask-rcnn_r101-gcb-r4-c3-c5_fpn_1x_coco.py │ ├── mask-rcnn_r101-syncbn-gcb-r16-c3-c5_fpn_1x_coco.py │ ├── mask-rcnn_r101-syncbn-gcb-r4-c3-c5_fpn_1x_coco.py │ ├── mask-rcnn_r101-syncbn_fpn_1x_coco.py │ ├── mask-rcnn_r50-gcb-r16-c3-c5_fpn_1x_coco.py │ ├── mask-rcnn_r50-gcb-r4-c3-c5_fpn_1x_coco.py │ ├── mask-rcnn_r50-syncbn-gcb-r16-c3-c5_fpn_1x_coco.py │ ├── mask-rcnn_r50-syncbn-gcb-r4-c3-c5_fpn_1x_coco.py │ ├── mask-rcnn_r50-syncbn_fpn_1x_coco.py │ ├── mask-rcnn_x101-32x4d-syncbn-gcb-r16-c3-c5_fpn_1x_coco.py │ ├── mask-rcnn_x101-32x4d-syncbn-gcb-r4-c3-c5_fpn_1x_coco.py │ ├── mask-rcnn_x101-32x4d-syncbn_fpn_1x_coco.py │ └── metafile.yml ├── gfl │ ├── README.md │ ├── gfl_r101-dconv-c3-c5_fpn_ms-2x_coco.py │ ├── gfl_r101_fpn_ms-2x_coco.py │ ├── gfl_r50_fpn_1x_coco.py │ ├── gfl_r50_fpn_ms-2x_coco.py │ ├── gfl_x101-32x4d-dconv-c4-c5_fpn_ms-2x_coco.py │ ├── gfl_x101-32x4d_fpn_ms-2x_coco.py │ └── metafile.yml ├── ghm │ ├── README.md │ ├── metafile.yml │ ├── retinanet_r101_fpn_ghm-1x_coco.py │ ├── retinanet_r50_fpn_ghm-1x_coco.py │ ├── retinanet_x101-32x4d_fpn_ghm-1x_coco.py │ └── retinanet_x101-64x4d_fpn_ghm-1x_coco.py ├── gn+ws │ ├── README.md │ ├── faster-rcnn_r101_fpn_gn-ws-all_1x_coco.py │ ├── faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py │ ├── faster-rcnn_x101-32x4d_fpn_gn-ws-all_1x_coco.py │ ├── faster-rcnn_x50-32x4d_fpn_gn-ws-all_1x_coco.py │ ├── mask-rcnn_r101_fpn_gn-ws-all_20-23-24e_coco.py │ ├── mask-rcnn_r101_fpn_gn-ws-all_2x_coco.py │ ├── mask-rcnn_r50_fpn_gn-ws-all_20-23-24e_coco.py │ ├── mask-rcnn_r50_fpn_gn-ws-all_2x_coco.py │ ├── mask-rcnn_x101-32x4d_fpn_gn-ws-all_20-23-24e_coco.py │ ├── mask-rcnn_x101-32x4d_fpn_gn-ws-all_2x_coco.py │ ├── mask-rcnn_x50-32x4d_fpn_gn-ws-all_20-23-24e_coco.py │ ├── mask-rcnn_x50-32x4d_fpn_gn-ws-all_2x_coco.py │ └── metafile.yml ├── gn │ ├── README.md │ ├── mask-rcnn_r101_fpn_gn-all_2x_coco.py │ ├── mask-rcnn_r101_fpn_gn-all_3x_coco.py │ ├── mask-rcnn_r50-contrib_fpn_gn-all_2x_coco.py │ ├── mask-rcnn_r50-contrib_fpn_gn-all_3x_coco.py │ ├── mask-rcnn_r50_fpn_gn-all_2x_coco.py │ ├── mask-rcnn_r50_fpn_gn-all_3x_coco.py │ └── metafile.yml ├── grid_rcnn │ ├── README.md │ ├── grid-rcnn_r101_fpn_gn-head_2x_coco.py │ ├── grid-rcnn_r50_fpn_gn-head_1x_coco.py │ ├── grid-rcnn_r50_fpn_gn-head_2x_coco.py │ ├── grid-rcnn_x101-32x4d_fpn_gn-head_2x_coco.py │ ├── grid-rcnn_x101-64x4d_fpn_gn-head_2x_coco.py │ └── metafile.yml ├── groie │ ├── README.md │ ├── faste-rcnn_r50_fpn_groie_1x_coco.py │ ├── grid-rcnn_r50_fpn_gn-head-groie_1x_coco.py │ ├── mask-rcnn_r101_fpn_syncbn-r4-gcb_c3-c5-groie_1x_coco.py │ ├── mask-rcnn_r50_fpn_groie_1x_coco.py │ ├── mask-rcnn_r50_fpn_syncbn-r4-gcb-c3-c5-groie_1x_coco.py │ └── metafile.yml ├── guided_anchoring │ ├── README.md │ ├── ga-fast-rcnn_r50-caffe_fpn_1x_coco.py │ ├── ga-faster-rcnn_r101-caffe_fpn_1x_coco.py │ ├── ga-faster-rcnn_r50-caffe_fpn_1x_coco.py │ ├── ga-faster-rcnn_r50_fpn_1x_coco.py │ ├── ga-faster-rcnn_x101-32x4d_fpn_1x_coco.py │ ├── ga-faster-rcnn_x101-64x4d_fpn_1x_coco.py │ ├── ga-retinanet_r101-caffe_fpn_1x_coco.py │ ├── ga-retinanet_r101-caffe_fpn_ms-2x.py │ ├── ga-retinanet_r50-caffe_fpn_1x_coco.py │ ├── ga-retinanet_r50_fpn_1x_coco.py │ ├── ga-retinanet_x101-32x4d_fpn_1x_coco.py │ ├── ga-retinanet_x101-64x4d_fpn_1x_coco.py │ ├── ga-rpn_r101-caffe_fpn_1x_coco.py │ ├── ga-rpn_r50-caffe_fpn_1x_coco.py │ ├── ga-rpn_r50_fpn_1x_coco.py │ ├── ga-rpn_x101-32x4d_fpn_1x_coco.py │ ├── ga-rpn_x101-64x4d_fpn_1x_coco.py │ └── metafile.yml ├── hrnet │ ├── README.md │ ├── cascade-mask-rcnn_hrnetv2p-w18_20e_coco.py │ ├── cascade-mask-rcnn_hrnetv2p-w32_20e_coco.py │ ├── cascade-mask-rcnn_hrnetv2p-w40-20e_coco.py │ ├── cascade-rcnn_hrnetv2p-w18-20e_coco.py │ ├── cascade-rcnn_hrnetv2p-w32-20e_coco.py │ ├── cascade-rcnn_hrnetv2p-w40-20e_coco.py │ ├── faster-rcnn_hrnetv2p-w18-1x_coco.py │ ├── faster-rcnn_hrnetv2p-w18-2x_coco.py │ ├── faster-rcnn_hrnetv2p-w32-1x_coco.py │ ├── faster-rcnn_hrnetv2p-w32_2x_coco.py │ ├── faster-rcnn_hrnetv2p-w40-1x_coco.py │ ├── faster-rcnn_hrnetv2p-w40_2x_coco.py │ ├── fcos_hrnetv2p-w18-gn-head_4xb4-1x_coco.py │ ├── fcos_hrnetv2p-w18-gn-head_4xb4-2x_coco.py │ ├── fcos_hrnetv2p-w18-gn-head_ms-640-800-4xb4-2x_coco.py │ ├── fcos_hrnetv2p-w32-gn-head_4xb4-1x_coco.py │ ├── fcos_hrnetv2p-w32-gn-head_4xb4-2x_coco.py │ ├── fcos_hrnetv2p-w32-gn-head_ms-640-800-4xb4-2x_coco.py │ ├── fcos_hrnetv2p-w40-gn-head_ms-640-800-4xb4-2x_coco.py │ ├── htc_hrnetv2p-w18_20e_coco.py │ ├── htc_hrnetv2p-w32_20e_coco.py │ ├── htc_hrnetv2p-w40_20e_coco.py │ ├── htc_hrnetv2p-w40_28e_coco.py │ ├── htc_x101-64x4d_fpn_16xb1-28e_coco.py │ ├── mask-rcnn_hrnetv2p-w18-1x_coco.py │ ├── mask-rcnn_hrnetv2p-w18-2x_coco.py │ ├── mask-rcnn_hrnetv2p-w32-1x_coco.py │ ├── mask-rcnn_hrnetv2p-w32-2x_coco.py │ ├── mask-rcnn_hrnetv2p-w40-2x_coco.py │ ├── mask-rcnn_hrnetv2p-w40_1x_coco.py │ └── metafile.yml ├── htc │ ├── README.md │ ├── htc-without-semantic_r50_fpn_1x_coco.py │ ├── htc_r101_fpn_20e_coco.py │ ├── htc_r50_fpn_1x_coco.py │ ├── htc_r50_fpn_20e_coco.py │ ├── htc_x101-32x4d_fpn_16xb1-20e_coco.py │ ├── htc_x101-64x4d-dconv-c3-c5_fpn_ms-400-1400-16xb1-20e_coco.py │ ├── htc_x101-64x4d_fpn_16xb1-20e_coco.py │ └── metafile.yml ├── instaboost │ ├── README.md │ ├── cascade-mask-rcnn_r101_fpn_instaboost-4x_coco.py │ ├── cascade-mask-rcnn_r50_fpn_instaboost-4x_coco.py │ ├── cascade-mask-rcnn_x101-64x4d_fpn_instaboost-4x_coco.py │ ├── mask-rcnn_r101_fpn_instaboost-4x_coco.py │ ├── mask-rcnn_r50_fpn_instaboost-4x_coco.py │ ├── mask-rcnn_x101-64x4d_fpn_instaboost-4x_coco.py │ └── metafile.yml ├── lad │ ├── README.md │ ├── lad_r101-paa-r50_fpn_2xb8_coco_1x.py │ ├── lad_r50-paa-r101_fpn_2xb8_coco_1x.py │ └── metafile.yml ├── ld │ ├── README.md │ ├── ld_r101-gflv1-r101-dcn_fpn_2x_coco.py │ ├── ld_r18-gflv1-r101_fpn_1x_coco.py │ ├── ld_r34-gflv1-r101_fpn_1x_coco.py │ ├── ld_r50-gflv1-r101_fpn_1x_coco.py │ └── metafile.yml ├── legacy_1.x │ ├── README.md │ ├── cascade-mask-rcnn_r50_fpn_1x_coco_v1.py │ ├── faster-rcnn_r50_fpn_1x_coco_v1.py │ ├── mask-rcnn_r50_fpn_1x_coco_v1.py │ ├── retinanet_r50-caffe_fpn_1x_coco_v1.py │ ├── retinanet_r50_fpn_1x_coco_v1.py │ └── ssd300_coco_v1.py ├── libra_rcnn │ ├── README.md │ ├── libra-fast-rcnn_r50_fpn_1x_coco.py │ ├── libra-faster-rcnn_r101_fpn_1x_coco.py │ ├── libra-faster-rcnn_r50_fpn_1x_coco.py │ ├── libra-faster-rcnn_x101-64x4d_fpn_1x_coco.py │ ├── libra-retinanet_r50_fpn_1x_coco.py │ └── metafile.yml ├── lvis │ ├── README.md │ ├── mask-rcnn_r101_fpn_sample1e-3_ms-1x_lvis-v1.py │ ├── mask-rcnn_r101_fpn_sample1e-3_ms-2x_lvis-v0.5.py │ ├── mask-rcnn_r50_fpn_sample1e-3_ms-1x_lvis-v1.py │ ├── mask-rcnn_r50_fpn_sample1e-3_ms-2x_lvis-v0.5.py │ ├── mask-rcnn_x101-32x4d_fpn_sample1e-3_ms-1x_lvis-v1.py │ ├── mask-rcnn_x101-32x4d_fpn_sample1e-3_ms-2x_lvis-v0.5.py │ ├── mask-rcnn_x101-64x4d_fpn_sample1e-3_ms-1x_lvis-v1.py │ ├── mask-rcnn_x101-64x4d_fpn_sample1e-3_ms-2x_lvis-v0.5.py │ └── metafile.yml ├── mask2former │ ├── README.md │ ├── mask2former_r101_8xb2-lsj-50e_coco-panoptic.py │ ├── mask2former_r101_8xb2-lsj-50e_coco.py │ ├── mask2former_r50_8xb2-lsj-50e_coco-panoptic.py │ ├── mask2former_r50_8xb2-lsj-50e_coco.py │ ├── mask2former_swin-b-p4-w12-384-in21k_8xb2-lsj-50e_coco-panoptic.py │ ├── mask2former_swin-b-p4-w12-384_8xb2-lsj-50e_coco-panoptic.py │ ├── mask2former_swin-l-p4-w12-384-in21k_16xb1-lsj-100e_coco-panoptic.py │ ├── mask2former_swin-s-p4-w7-224_8xb2-lsj-50e_coco-panoptic.py │ ├── mask2former_swin-s-p4-w7-224_8xb2-lsj-50e_coco.py │ ├── mask2former_swin-t-p4-w7-224_8xb2-lsj-50e_coco-panoptic.py │ ├── mask2former_swin-t-p4-w7-224_8xb2-lsj-50e_coco.py │ └── metafile.yml ├── mask_rcnn │ ├── README.md │ ├── mask-rcnn_r101-caffe_fpn_1x_coco.py │ ├── mask-rcnn_r101-caffe_fpn_ms-poly-3x_coco.py │ ├── mask-rcnn_r101_fpn_1x_coco.py │ ├── mask-rcnn_r101_fpn_2x_coco.py │ ├── mask-rcnn_r101_fpn_8xb8-amp-lsj-200e_coco.py │ ├── mask-rcnn_r101_fpn_ms-poly-3x_coco.py │ ├── mask-rcnn_r18_fpn_8xb8-amp-lsj-200e_coco.py │ ├── mask-rcnn_r50-caffe-c4_1x_coco.py │ ├── mask-rcnn_r50-caffe_fpn_1x_coco.py │ ├── mask-rcnn_r50-caffe_fpn_ms-1x_coco.py │ ├── mask-rcnn_r50-caffe_fpn_ms-poly-1x_coco.py │ ├── mask-rcnn_r50-caffe_fpn_ms-poly-2x_coco.py │ ├── mask-rcnn_r50-caffe_fpn_ms-poly-3x_coco.py │ ├── mask-rcnn_r50-caffe_fpn_poly-1x_coco_v1.py │ ├── mask-rcnn_r50_fpn_1x-wandb_coco.py │ ├── mask-rcnn_r50_fpn_1x_coco.py │ ├── mask-rcnn_r50_fpn_2x_coco.py │ ├── mask-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py │ ├── mask-rcnn_r50_fpn_amp-1x_coco.py │ ├── mask-rcnn_r50_fpn_ms-poly-3x_coco.py │ ├── mask-rcnn_r50_fpn_poly-1x_coco.py │ ├── mask-rcnn_x101-32x4d_fpn_1x_coco.py │ ├── mask-rcnn_x101-32x4d_fpn_2x_coco.py │ ├── mask-rcnn_x101-32x4d_fpn_ms-poly-3x_coco.py │ ├── mask-rcnn_x101-32x8d_fpn_1x_coco.py │ ├── mask-rcnn_x101-32x8d_fpn_ms-poly-1x_coco.py │ ├── mask-rcnn_x101-32x8d_fpn_ms-poly-3x_coco.py │ ├── mask-rcnn_x101-64x4d_fpn_1x_coco.py │ ├── mask-rcnn_x101-64x4d_fpn_2x_coco.py │ ├── mask-rcnn_x101-64x4d_fpn_ms-poly_3x_coco.py │ └── metafile.yml ├── maskformer │ ├── README.md │ ├── maskformer_r50_ms-16xb1-75e_coco.py │ ├── maskformer_swin-l-p4-w12_64xb1-ms-300e_coco.py │ └── metafile.yml ├── misc │ ├── d2_faster-rcnn_r50-caffe_fpn_ms-90k_coco.py │ ├── d2_mask-rcnn_r50-caffe_fpn_ms-90k_coco.py │ └── d2_retinanet_r50-caffe_fpn_ms-90k_coco.py ├── ms_rcnn │ ├── README.md │ ├── metafile.yml │ ├── ms-rcnn_r101-caffe_fpn_1x_coco.py │ ├── ms-rcnn_r101-caffe_fpn_2x_coco.py │ ├── ms-rcnn_r50-caffe_fpn_1x_coco.py │ ├── ms-rcnn_r50-caffe_fpn_2x_coco.py │ ├── ms-rcnn_r50_fpn_1x_coco.py │ ├── ms-rcnn_x101-32x4d_fpn_1x_coco.py │ ├── ms-rcnn_x101-64x4d_fpn_1x_coco.py │ └── ms-rcnn_x101-64x4d_fpn_2x_coco.py ├── nas_fcos │ ├── README.md │ ├── metafile.yml │ ├── nas-fcos_r50-caffe_fpn_fcoshead-gn-head_4xb4-1x_coco.py │ └── nas-fcos_r50-caffe_fpn_nashead-gn-head_4xb4-1x_coco.py ├── nas_fpn │ ├── README.md │ ├── metafile.yml │ ├── retinanet_r50_fpn_crop640-50e_coco.py │ └── retinanet_r50_nasfpn_crop640-50e_coco.py ├── objects365 │ ├── README.md │ ├── faster-rcnn_r50-syncbn_fpn_1350k_objects365v1.py │ ├── faster-rcnn_r50_fpn_16xb4-1x_objects365v1.py │ ├── faster-rcnn_r50_fpn_16xb4-1x_objects365v2.py │ ├── metafile.yml │ ├── retinanet_r50-syncbn_fpn_1350k_objects365v1.py │ ├── retinanet_r50_fpn_1x_objects365v1.py │ └── retinanet_r50_fpn_1x_objects365v2.py ├── openimages │ ├── README.md │ ├── faster-rcnn_r50_fpn_32xb2-1x_openimages-challenge.py │ ├── faster-rcnn_r50_fpn_32xb2-1x_openimages.py │ ├── faster-rcnn_r50_fpn_32xb2-cas-1x_openimages-challenge.py │ ├── faster-rcnn_r50_fpn_32xb2-cas-1x_openimages.py │ ├── metafile.yml │ ├── retinanet_r50_fpn_32xb2-1x_openimages.py │ └── ssd300_32xb8-36e_openimages.py ├── paa │ ├── README.md │ ├── metafile.yml │ ├── paa_r101_fpn_1x_coco.py │ ├── paa_r101_fpn_2x_coco.py │ ├── paa_r101_fpn_ms-3x_coco.py │ ├── paa_r50_fpn_1.5x_coco.py │ ├── paa_r50_fpn_1x_coco.py │ ├── paa_r50_fpn_2x_coco.py │ └── paa_r50_fpn_ms-3x_coco.py ├── pafpn │ ├── README.md │ ├── faster-rcnn_r50_pafpn_1x_coco.py │ └── metafile.yml ├── panoptic_fpn │ ├── README.md │ ├── metafile.yml │ ├── panoptic-fpn_r101_fpn_1x_coco.py │ ├── panoptic-fpn_r101_fpn_ms-3x_coco.py │ ├── panoptic-fpn_r50_fpn_1x_coco.py │ └── panoptic-fpn_r50_fpn_ms-3x_coco.py ├── pascal_voc │ ├── README.md │ ├── faster-rcnn_r50-caffe-c4_ms-18k_voc0712.py │ ├── faster-rcnn_r50_fpn_1x_voc0712-cocofmt.py │ ├── faster-rcnn_r50_fpn_1x_voc0712.py │ ├── retinanet_r50_fpn_1x_voc0712.py │ ├── ssd300_voc0712.py │ └── ssd512_voc0712.py ├── pisa │ ├── README.md │ ├── faster-rcnn_r50_fpn_pisa_1x_coco.py │ ├── faster-rcnn_x101-32x4d_fpn_pisa_1x_coco.py │ ├── mask-rcnn_r50_fpn_pisa_1x_coco.py │ ├── mask-rcnn_x101-32x4d_fpn_pisa_1x_coco.py │ ├── metafile.yml │ ├── retinanet-r50_fpn_pisa_1x_coco.py │ ├── retinanet_x101-32x4d_fpn_pisa_1x_coco.py │ ├── ssd300_pisa_coco.py │ └── ssd512_pisa_coco.py ├── point_rend │ ├── README.md │ ├── metafile.yml │ ├── point-rend_r50-caffe_fpn_ms-1x_coco.py │ └── point-rend_r50-caffe_fpn_ms-3x_coco.py ├── pvt │ ├── README.md │ ├── metafile.yml │ ├── retinanet_pvt-l_fpn_1x_coco.py │ ├── retinanet_pvt-m_fpn_1x_coco.py │ ├── retinanet_pvt-s_fpn_1x_coco.py │ ├── retinanet_pvt-t_fpn_1x_coco.py │ ├── retinanet_pvtv2-b0_fpn_1x_coco.py │ ├── retinanet_pvtv2-b1_fpn_1x_coco.py │ ├── retinanet_pvtv2-b2_fpn_1x_coco.py │ ├── retinanet_pvtv2-b3_fpn_1x_coco.py │ ├── retinanet_pvtv2-b4_fpn_1x_coco.py │ └── retinanet_pvtv2-b5_fpn_1x_coco.py ├── queryinst │ ├── README.md │ ├── metafile.yml │ ├── queryinst_r101_fpn_300-proposals_crop-ms-480-800-3x_coco.py │ ├── queryinst_r101_fpn_ms-480-800-3x_coco.py │ ├── queryinst_r50_fpn_1x_coco.py │ ├── queryinst_r50_fpn_300-proposals_crop-ms-480-800-3x_coco.py │ └── queryinst_r50_fpn_ms-480-800-3x_coco.py ├── regnet │ ├── README.md │ ├── cascade-mask-rcnn_regnetx-1.6GF_fpn_ms-3x_coco.py │ ├── cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py │ ├── cascade-mask-rcnn_regnetx-400MF_fpn_ms-3x_coco.py │ ├── cascade-mask-rcnn_regnetx-4GF_fpn_ms-3x_coco.py │ ├── cascade-mask-rcnn_regnetx-800MF_fpn_ms-3x_coco.py │ ├── faster-rcnn_regnetx-1.6GF_fpn_ms-3x_coco.py │ ├── faster-rcnn_regnetx-3.2GF_fpn_1x_coco.py │ ├── faster-rcnn_regnetx-3.2GF_fpn_2x_coco.py │ ├── faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py │ ├── faster-rcnn_regnetx-400MF_fpn_ms-3x_coco.py │ ├── faster-rcnn_regnetx-4GF_fpn_ms-3x_coco.py │ ├── faster-rcnn_regnetx-800MF_fpn_ms-3x_coco.py │ ├── mask-rcnn_regnetx-1.6GF_fpn_ms-poly-3x_coco.py │ ├── mask-rcnn_regnetx-12GF_fpn_1x_coco.py │ ├── mask-rcnn_regnetx-3.2GF-mdconv-c3-c5_fpn_1x_coco.py │ ├── mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py │ ├── mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py │ ├── mask-rcnn_regnetx-400MF_fpn_ms-poly-3x_coco.py │ ├── mask-rcnn_regnetx-4GF_fpn_1x_coco.py │ ├── mask-rcnn_regnetx-4GF_fpn_ms-poly-3x_coco.py │ ├── mask-rcnn_regnetx-6.4GF_fpn_1x_coco.py │ ├── mask-rcnn_regnetx-800MF_fpn_ms-poly-3x_coco.py │ ├── mask-rcnn_regnetx-8GF_fpn_1x_coco.py │ ├── metafile.yml │ ├── retinanet_regnetx-1.6GF_fpn_1x_coco.py │ ├── retinanet_regnetx-3.2GF_fpn_1x_coco.py │ └── retinanet_regnetx-800MF_fpn_1x_coco.py ├── reppoints │ ├── README.md │ ├── metafile.yml │ ├── reppoints-bbox_r50-center_fpn-gn_head-gn-grid_1x_coco.py │ ├── reppoints-bbox_r50_fpn-gn_head-gn-grid_1x_coco.py │ ├── reppoints-minmax_r50_fpn-gn_head-gn_1x_coco.py │ ├── reppoints-moment_r101-dconv-c3-c5_fpn-gn_head-gn_2x_coco.py │ ├── reppoints-moment_r101_fpn-gn_head-gn_2x_coco.py │ ├── reppoints-moment_r50_fpn-gn_head-gn_1x_coco.py │ ├── reppoints-moment_r50_fpn-gn_head-gn_2x_coco.py │ ├── reppoints-moment_r50_fpn_1x_coco.py │ ├── reppoints-moment_x101-dconv-c3-c5_fpn-gn_head-gn_2x_coco.py │ ├── reppoints-partial-minmax_r50_fpn-gn_head-gn_1x_coco.py │ └── reppoints.png ├── res2net │ ├── README.md │ ├── cascade-mask-rcnn_res2net-101_fpn_20e_coco.py │ ├── cascade-rcnn_res2net-101_fpn_20e_coco.py │ ├── faster-rcnn_res2net-101_fpn_2x_coco.py │ ├── htc_res2net-101_fpn_20e_coco.py │ ├── mask-rcnn_res2net-101_fpn_2x_coco.py │ └── metafile.yml ├── resnest │ ├── README.md │ ├── cascade-mask-rcnn_s101_fpn_syncbn-backbone+head_ms-1x_coco.py │ ├── cascade-mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py │ ├── cascade-rcnn_s101_fpn_syncbn-backbone+head_ms-range-1x_coco.py │ ├── cascade-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py │ ├── faster-rcnn_s101_fpn_syncbn-backbone+head_ms-range-1x_coco.py │ ├── faster-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py │ ├── mask-rcnn_s101_fpn_syncbn-backbone+head_ms-1x_coco.py │ ├── mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py │ └── metafile.yml ├── resnet_strikes_back │ ├── README.md │ ├── cascade-mask-rcnn_r50-rsb-pre_fpn_1x_coco.py │ ├── faster-rcnn_r50-rsb-pre_fpn_1x_coco.py │ ├── mask-rcnn_r50-rsb-pre_fpn_1x_coco.py │ ├── metafile.yml │ └── retinanet_r50-rsb-pre_fpn_1x_coco.py ├── retinanet │ ├── README.md │ ├── metafile.yml │ ├── retinanet_r101-caffe_fpn_1x_coco.py │ ├── retinanet_r101-caffe_fpn_ms-3x_coco.py │ ├── retinanet_r101_fpn_1x_coco.py │ ├── retinanet_r101_fpn_2x_coco.py │ ├── retinanet_r101_fpn_8xb8-amp-lsj-200e_coco.py │ ├── retinanet_r101_fpn_ms-640-800-3x_coco.py │ ├── retinanet_r18_fpn_1x_coco.py │ ├── retinanet_r18_fpn_1xb8-1x_coco.py │ ├── retinanet_r18_fpn_8xb8-amp-lsj-200e_coco.py │ ├── retinanet_r50-caffe_fpn_1x_coco.py │ ├── retinanet_r50-caffe_fpn_ms-1x_coco.py │ ├── retinanet_r50-caffe_fpn_ms-2x_coco.py │ ├── retinanet_r50-caffe_fpn_ms-3x_coco.py │ ├── retinanet_r50_fpn_1x_coco.py │ ├── retinanet_r50_fpn_2x_coco.py │ ├── retinanet_r50_fpn_8xb8-amp-lsj-200e_coco.py │ ├── retinanet_r50_fpn_90k_coco.py │ ├── retinanet_r50_fpn_amp-1x_coco.py │ ├── retinanet_r50_fpn_ms-640-800-3x_coco.py │ ├── retinanet_tta.py │ ├── retinanet_x101-32x4d_fpn_1x_coco.py │ ├── retinanet_x101-32x4d_fpn_2x_coco.py │ ├── retinanet_x101-64x4d_fpn_1x_coco.py │ ├── retinanet_x101-64x4d_fpn_2x_coco.py │ └── retinanet_x101-64x4d_fpn_ms-640-800-3x_coco.py ├── rpn │ ├── README.md │ ├── metafile.yml │ ├── rpn_r101-caffe_fpn_1x_coco.py │ ├── rpn_r101_fpn_1x_coco.py │ ├── rpn_r101_fpn_2x_coco.py │ ├── rpn_r50-caffe-c4_1x_coco.py │ ├── rpn_r50-caffe_fpn_1x_coco.py │ ├── rpn_r50_fpn_1x_coco.py │ ├── rpn_r50_fpn_2x_coco.py │ ├── rpn_x101-32x4d_fpn_1x_coco.py │ ├── rpn_x101-32x4d_fpn_2x_coco.py │ ├── rpn_x101-64x4d_fpn_1x_coco.py │ └── rpn_x101-64x4d_fpn_2x_coco.py ├── rtmdet │ ├── README.md │ ├── classification │ │ ├── README.md │ │ ├── cspnext-l_8xb256-rsb-a1-600e_in1k.py │ │ ├── cspnext-m_8xb256-rsb-a1-600e_in1k.py │ │ ├── cspnext-s_8xb256-rsb-a1-600e_in1k.py │ │ ├── cspnext-tiny_8xb256-rsb-a1-600e_in1k.py │ │ └── cspnext-x_8xb256-rsb-a1-600e_in1k.py │ ├── metafile.yml │ ├── rtmdet-ins_l_8xb32-300e_coco.py │ ├── rtmdet-ins_m_8xb32-300e_coco.py │ ├── rtmdet-ins_s_8xb32-300e_coco.py │ ├── rtmdet-ins_tiny_8xb32-300e_coco.py │ ├── rtmdet-ins_x_8xb16-300e_coco.py │ ├── rtmdet_l_8xb32-300e_coco.py │ ├── rtmdet_m_8xb32-300e_coco.py │ ├── rtmdet_s_8xb32-300e_coco.py │ ├── rtmdet_tiny_8xb32-300e_coco.py │ ├── rtmdet_tta.py │ └── rtmdet_x_8xb32-300e_coco.py ├── sabl │ ├── README.md │ ├── metafile.yml │ ├── sabl-cascade-rcnn_r101_fpn_1x_coco.py │ ├── sabl-cascade-rcnn_r50_fpn_1x_coco.py │ ├── sabl-faster-rcnn_r101_fpn_1x_coco.py │ ├── sabl-faster-rcnn_r50_fpn_1x_coco.py │ ├── sabl-retinanet_r101-gn_fpn_1x_coco.py │ ├── sabl-retinanet_r101-gn_fpn_ms-480-960-2x_coco.py │ ├── sabl-retinanet_r101-gn_fpn_ms-640-800-2x_coco.py │ ├── sabl-retinanet_r101_fpn_1x_coco.py │ ├── sabl-retinanet_r50-gn_fpn_1x_coco.py │ └── sabl-retinanet_r50_fpn_1x_coco.py ├── scnet │ ├── README.md │ ├── metafile.yml │ ├── scnet_r101_fpn_20e_coco.py │ ├── scnet_r50_fpn_1x_coco.py │ ├── scnet_r50_fpn_20e_coco.py │ ├── scnet_x101-64x4d_fpn_20e_coco.py │ └── scnet_x101-64x4d_fpn_8xb1-20e_coco.py ├── scratch │ ├── README.md │ ├── faster-rcnn_r50-scratch_fpn_gn-all_6x_coco.py │ ├── mask-rcnn_r50-scratch_fpn_gn-all_6x_coco.py │ └── metafile.yml ├── seesaw_loss │ ├── README.md │ ├── cascade-mask-rcnn_r101_fpn_seesaw-loss-normed-mask_random-ms-2x_lvis-v1.py │ ├── cascade-mask-rcnn_r101_fpn_seesaw-loss-normed-mask_sample1e-3-ms-2x_lvis-v1.py │ ├── cascade-mask-rcnn_r101_fpn_seesaw-loss_random-ms-2x_lvis-v1.py │ ├── cascade-mask-rcnn_r101_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py │ ├── mask-rcnn_r101_fpn_seesaw-loss-normed-mask_random-ms-2x_lvis-v1.py │ ├── mask-rcnn_r101_fpn_seesaw-loss-normed-mask_sample1e-3-ms-2x_lvis-v1.py │ ├── mask-rcnn_r101_fpn_seesaw-loss_random-ms-2x_lvis-v1.py │ ├── mask-rcnn_r101_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py │ ├── mask-rcnn_r50_fpn_seesaw-loss-normed-mask_random-ms-2x_lvis-v1.py │ ├── mask-rcnn_r50_fpn_seesaw-loss-normed-mask_sample1e-3-ms-2x_lvis-v1.py │ ├── mask-rcnn_r50_fpn_seesaw-loss_random-ms-2x_lvis-v1.py │ ├── mask-rcnn_r50_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py │ └── metafile.yml ├── selfsup_pretrain │ ├── README.md │ ├── mask-rcnn_r50-mocov2-pre_fpn_1x_coco.py │ ├── mask-rcnn_r50-mocov2-pre_fpn_ms-2x_coco.py │ ├── mask-rcnn_r50-swav-pre_fpn_1x_coco.py │ └── mask-rcnn_r50-swav-pre_fpn_ms-2x_coco.py ├── simple_copy_paste │ ├── README.md │ ├── mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-270k_coco.py │ ├── mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-90k_coco.py │ ├── mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-scp-270k_coco.py │ ├── mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-scp-90k_coco.py │ └── metafile.yml ├── soft_teacher │ ├── README.md │ ├── metafile.yml │ ├── soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.01-coco.py │ ├── soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.02-coco.py │ ├── soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.05-coco.py │ └── soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.1-coco.py ├── solo │ ├── README.md │ ├── decoupled-solo-light_r50_fpn_3x_coco.py │ ├── decoupled-solo_r50_fpn_1x_coco.py │ ├── decoupled-solo_r50_fpn_3x_coco.py │ ├── metafile.yml │ ├── solo_r101_fpn_8xb8-lsj-200e_coco.py │ ├── solo_r18_fpn_8xb8-lsj-200e_coco.py │ ├── solo_r50_fpn_1x_coco.py │ ├── solo_r50_fpn_3x_coco.py │ └── solo_r50_fpn_8xb8-lsj-200e_coco.py ├── solov2 │ ├── README.md │ ├── metafile.yml │ ├── solov2-light_r18_fpn_ms-3x_coco.py │ ├── solov2-light_r34_fpn_ms-3x_coco.py │ ├── solov2-light_r50-dcn_fpn_ms-3x_coco.py │ ├── solov2-light_r50_fpn_ms-3x_coco.py │ ├── solov2_r101-dcn_fpn_ms-3x_coco.py │ ├── solov2_r101_fpn_ms-3x_coco.py │ ├── solov2_r50_fpn_1x_coco.py │ ├── solov2_r50_fpn_ms-3x_coco.py │ └── solov2_x101-dcn_fpn_ms-3x_coco.py ├── sparse_rcnn │ ├── README.md │ ├── metafile.yml │ ├── sparse-rcnn_r101_fpn_300-proposals_crop-ms-480-800-3x_coco.py │ ├── sparse-rcnn_r101_fpn_ms-480-800-3x_coco.py │ ├── sparse-rcnn_r50_fpn_1x_coco.py │ ├── sparse-rcnn_r50_fpn_300-proposals_crop-ms-480-800-3x_coco.py │ └── sparse-rcnn_r50_fpn_ms-480-800-3x_coco.py ├── ssd │ ├── README.md │ ├── metafile.yml │ ├── ssd300_coco.py │ ├── ssd512_coco.py │ └── ssdlite_mobilenetv2-scratch_8xb24-600e_coco.py ├── strong_baselines │ ├── README.md │ ├── mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_amp-lsj-100e_coco.py │ ├── mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py │ ├── mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-400e_coco.py │ ├── mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_amp-lsj-100e_coco.py │ ├── mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py │ ├── mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-50e_coco.py │ └── metafile.yml ├── swin │ ├── README.md │ ├── mask-rcnn_swin-s-p4-w7_fpn_amp-ms-crop-3x_coco.py │ ├── mask-rcnn_swin-t-p4-w7_fpn_1x_coco.py │ ├── mask-rcnn_swin-t-p4-w7_fpn_amp-ms-crop-3x_coco.py │ ├── mask-rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py │ ├── metafile.yml │ └── retinanet_swin-t-p4-w7_fpn_1x_coco.py ├── timm_example │ ├── README.md │ ├── retinanet_timm-efficientnet-b1_fpn_1x_coco.py │ └── retinanet_timm-tv-resnet50_fpn_1x_coco.py ├── tood │ ├── README.md │ ├── metafile.yml │ ├── tood_r101-dconv-c3-c5_fpn_ms-2x_coco.py │ ├── tood_r101_fpn_ms-2x_coco.py │ ├── tood_r50_fpn_1x_coco.py │ ├── tood_r50_fpn_anchor-based_1x_coco.py │ ├── tood_r50_fpn_ms-2x_coco.py │ ├── tood_x101-64x4d-dconv-c4-c5_fpn_ms-2x_coco.py │ └── tood_x101-64x4d_fpn_ms-2x_coco.py ├── tridentnet │ ├── README.md │ ├── metafile.yml │ ├── tridentnet_r50-caffe_1x_coco.py │ ├── tridentnet_r50-caffe_ms-1x_coco.py │ └── tridentnet_r50-caffe_ms-3x_coco.py ├── vfnet │ ├── README.md │ ├── metafile.yml │ ├── vfnet_r101-mdconv-c3-c5_fpn_ms-2x_coco.py │ ├── vfnet_r101_fpn_1x_coco.py │ ├── vfnet_r101_fpn_2x_coco.py │ ├── vfnet_r101_fpn_ms-2x_coco.py │ ├── vfnet_r50-mdconv-c3-c5_fpn_ms-2x_coco.py │ ├── vfnet_r50_fpn_1x_coco.py │ ├── vfnet_r50_fpn_ms-2x_coco.py │ ├── vfnet_res2net-101_fpn_ms-2x_coco.py │ ├── vfnet_res2net101-mdconv-c3-c5_fpn_ms-2x_coco.py │ ├── vfnet_x101-32x4d-mdconv-c3-c5_fpn_ms-2x_coco.py │ ├── vfnet_x101-32x4d_fpn_ms-2x_coco.py │ ├── vfnet_x101-64x4d-mdconv-c3-c5_fpn_ms-2x_coco.py │ └── vfnet_x101-64x4d_fpn_ms-2x_coco.py ├── wider_face │ ├── README.md │ ├── retinanet_r50_fpn_1x_widerface.py │ └── ssd300_8xb32-24e_widerface.py ├── yolact │ ├── README.md │ ├── metafile.yml │ ├── yolact_r101_1xb8-55e_coco.py │ ├── yolact_r50_1xb8-55e_coco.py │ └── yolact_r50_8xb8-55e_coco.py ├── yolo │ ├── README.md │ ├── metafile.yml │ ├── yolov3_d53_8xb8-320-273e_coco.py │ ├── yolov3_d53_8xb8-amp-ms-608-273e_coco.py │ ├── yolov3_d53_8xb8-ms-416-273e_coco.py │ ├── yolov3_d53_8xb8-ms-608-273e_coco.py │ ├── yolov3_mobilenetv2_8xb24-320-300e_coco.py │ └── yolov3_mobilenetv2_8xb24-ms-416-300e_coco.py ├── yolof │ ├── README.md │ ├── metafile.yml │ ├── yolof_r50-c5_8xb8-1x_coco.py │ └── yolof_r50-c5_8xb8-iter-1x_coco.py └── yolox │ ├── README.md │ ├── metafile.yml │ ├── yolox_l_8xb8-300e_coco.py │ ├── yolox_m_8xb8-300e_coco.py │ ├── yolox_nano_8xb8-300e_coco.py │ ├── yolox_s_8xb8-300e_coco.py │ ├── yolox_tiny_8xb8-300e_coco.py │ ├── yolox_tta.py │ └── yolox_x_8xb8-300e_coco.py ├── icons └── mmlab.png ├── requirements1.txt ├── requirements2.txt ├── requirements3.txt ├── train_mmlab_detection.py ├── train_mmlab_detection_process.py ├── train_mmlab_detection_test.py ├── train_mmlab_detection_widget.py └── utils.py /__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ikomia-hub/train_mmlab_detection/63795200e4c00fc95fd1cfc3acd4a58baa1c3140/__init__.py -------------------------------------------------------------------------------- /configs/albu_example/metafile.yml: -------------------------------------------------------------------------------- 1 | Models: 2 | - Name: mask-rcnn_r50_fpn_albu-1x_coco 3 | In Collection: Mask R-CNN 4 | Config: mask-rcnn_r50_fpn_albu-1x_coco.py 5 | Metadata: 6 | Training Memory (GB): 4.4 7 | Epochs: 12 8 | Results: 9 | - Task: Object Detection 10 | Dataset: COCO 11 | Metrics: 12 | box AP: 38.0 13 | - Task: Instance Segmentation 14 | Dataset: COCO 15 | Metrics: 16 | mask AP: 34.5 17 | Weights: https://download.openmmlab.com/mmdetection/v2.0/albu_example/mask_rcnn_r50_fpn_albu_1x_coco/mask_rcnn_r50_fpn_albu_1x_coco_20200208-ab203bcd.pth 18 | -------------------------------------------------------------------------------- /configs/atss/atss_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './atss_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/atss/atss_r101_fpn_8xb8-amp-lsj-200e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './atss_r50_fpn_8xb8-amp-lsj-200e_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/atss/atss_r18_fpn_8xb8-amp-lsj-200e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './atss_r50_fpn_8xb8-amp-lsj-200e_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=18, 6 | init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')), 7 | neck=dict(in_channels=[64, 128, 256, 512])) 8 | -------------------------------------------------------------------------------- /configs/boxinst/boxinst_r101_fpn_ms-90k_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './boxinst_r50_fpn_ms-90k_coco.py' 2 | 3 | # model settings 4 | model = dict( 5 | backbone=dict( 6 | depth=101, 7 | init_cfg=dict(type='Pretrained', 8 | checkpoint='torchvision://resnet101'))) 9 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-mask-rcnn_r101-caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-mask-rcnn_r50-caffe_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 8 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-mask-rcnn_r101-caffe_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-mask-rcnn_r50-caffe_fpn_ms-3x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 8 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-mask-rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-mask-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-mask-rcnn_r101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-mask-rcnn_r50_fpn_20e_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-mask-rcnn_r101_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-mask-rcnn_r50_fpn_ms-3x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-mask-rcnn_r50-caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./cascade-mask-rcnn_r50_fpn_1x_coco.py'] 2 | 3 | model = dict( 4 | data_preprocessor=dict( 5 | mean=[103.530, 116.280, 123.675], 6 | std=[1.0, 1.0, 1.0], 7 | bgr_to_rgb=False), 8 | backbone=dict( 9 | norm_cfg=dict(requires_grad=False), 10 | norm_eval=True, 11 | style='caffe', 12 | init_cfg=dict( 13 | type='Pretrained', 14 | checkpoint='open-mmlab://detectron2/resnet50_caffe'))) 15 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-mask-rcnn_r50-caffe_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../common/ms_3x_coco-instance.py', 3 | '../_base_/models/cascade-mask-rcnn_r50_fpn.py' 4 | ] 5 | 6 | model = dict( 7 | # use caffe img_norm 8 | data_preprocessor=dict( 9 | mean=[103.530, 116.280, 123.675], 10 | std=[1.0, 1.0, 1.0], 11 | bgr_to_rgb=False), 12 | backbone=dict( 13 | norm_cfg=dict(requires_grad=False), 14 | norm_eval=True, 15 | style='caffe', 16 | init_cfg=dict( 17 | type='Pretrained', 18 | checkpoint='open-mmlab://detectron2/resnet50_caffe'))) 19 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/cascade-mask-rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_instance.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-mask-rcnn_r50_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/cascade-mask-rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_instance.py', 4 | '../_base_/schedules/schedule_20e.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-mask-rcnn_r50_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../common/ms_3x_coco-instance.py', 3 | '../_base_/models/cascade-mask-rcnn_r50_fpn.py' 4 | ] 5 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-mask-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-mask-rcnn_r50_fpn_20e_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-mask-rcnn_r50_fpn_ms-3x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-mask-rcnn_x101-64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-mask-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-mask-rcnn_x101-64x4d_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-mask-rcnn_r50_fpn_20e_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-mask-rcnn_x101-64x4d_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-mask-rcnn_r50_fpn_ms-3x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-rcnn_r101-caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-rcnn_r50-caffe_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 8 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-rcnn_r101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-rcnn_r50_fpn_20e_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-rcnn_r101_fpn_8xb8-amp-lsj-200e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-rcnn_r18_fpn_8xb8-amp-lsj-200e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=18, 6 | init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')), 7 | neck=dict(in_channels=[64, 128, 256, 512])) 8 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-rcnn_r50-caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-rcnn_r50_fpn_1x_coco.py' 2 | 3 | model = dict( 4 | # use caffe img_norm 5 | data_preprocessor=dict( 6 | type='DetDataPreprocessor', 7 | mean=[103.530, 116.280, 123.675], 8 | std=[1.0, 1.0, 1.0], 9 | bgr_to_rgb=False, 10 | pad_size_divisor=32), 11 | backbone=dict( 12 | norm_cfg=dict(requires_grad=False), 13 | style='caffe', 14 | init_cfg=dict( 15 | type='Pretrained', 16 | checkpoint='open-mmlab://detectron2/resnet50_caffe'))) 17 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/cascade-rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_detection.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-rcnn_r50_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/cascade-rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_detection.py', 4 | '../_base_/schedules/schedule_20e.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-rcnn_x101-32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-rcnn_x101-32x4d_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-rcnn_r50_fpn_20e_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-rcnn_x101-64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | type='CascadeRCNN', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch', 14 | init_cfg=dict( 15 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 16 | -------------------------------------------------------------------------------- /configs/cascade_rcnn/cascade-rcnn_x101_64x4d_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-rcnn_r50_fpn_20e_coco.py' 2 | model = dict( 3 | type='CascadeRCNN', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | style='pytorch', 14 | init_cfg=dict( 15 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 16 | -------------------------------------------------------------------------------- /configs/centernet/centernet-update_r101_fpn_8xb8-amp-lsj-200e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './centernet-update_r50_fpn_8xb8-amp-lsj-200e_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/centernet/centernet-update_r18_fpn_8xb8-amp-lsj-200e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './centernet-update_r50_fpn_8xb8-amp-lsj-200e_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=18, 6 | init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')), 7 | neck=dict(in_channels=[64, 128, 256, 512])) 8 | -------------------------------------------------------------------------------- /configs/centernet/centernet_r18_8xb16-crop512-140e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './centernet_r18-dcnv2_8xb16-crop512-140e_coco.py' 2 | 3 | model = dict(neck=dict(use_dcn=False)) 4 | -------------------------------------------------------------------------------- /configs/common/lsj-200e_coco-detection.py: -------------------------------------------------------------------------------- 1 | _base_ = './lsj-100e_coco-detection.py' 2 | 3 | # 8x25=200e 4 | train_dataloader = dict(dataset=dict(times=8)) 5 | 6 | # learning rate 7 | param_scheduler = [ 8 | dict( 9 | type='LinearLR', start_factor=0.067, by_epoch=False, begin=0, 10 | end=1000), 11 | dict( 12 | type='MultiStepLR', 13 | begin=0, 14 | end=25, 15 | by_epoch=True, 16 | milestones=[22, 24], 17 | gamma=0.1) 18 | ] 19 | -------------------------------------------------------------------------------- /configs/common/lsj-200e_coco-instance.py: -------------------------------------------------------------------------------- 1 | _base_ = './lsj-100e_coco-instance.py' 2 | 3 | # 8x25=200e 4 | train_dataloader = dict(dataset=dict(times=8)) 5 | 6 | # learning rate 7 | param_scheduler = [ 8 | dict( 9 | type='LinearLR', start_factor=0.067, by_epoch=False, begin=0, 10 | end=1000), 11 | dict( 12 | type='MultiStepLR', 13 | begin=0, 14 | end=25, 15 | by_epoch=True, 16 | milestones=[22, 24], 17 | gamma=0.1) 18 | ] 19 | -------------------------------------------------------------------------------- /configs/cornernet/cornernet_hourglass104_10xb5-crop511-210e-mstest_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cornernet_hourglass104_8xb6-210e-mstest_coco.py' 2 | 3 | train_dataloader = dict(batch_size=5) 4 | 5 | # NOTE: `auto_scale_lr` is for automatically scaling LR, 6 | # USER SHOULD NOT CHANGE ITS VALUES. 7 | # base_batch_size = (10 GPUs) x (5 samples per GPU) 8 | auto_scale_lr = dict(base_batch_size=50) 9 | -------------------------------------------------------------------------------- /configs/cornernet/cornernet_hourglass104_32xb3-210e-mstest_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cornernet_hourglass104_8xb6-210e-mstest_coco.py' 2 | 3 | train_dataloader = dict(batch_size=3) 4 | 5 | # NOTE: `auto_scale_lr` is for automatically scaling LR, 6 | # USER SHOULD NOT CHANGE ITS VALUES. 7 | # base_batch_size = (32 GPUs) x (3 samples per GPU) 8 | auto_scale_lr = dict(base_batch_size=96) 9 | -------------------------------------------------------------------------------- /configs/crowddet/crowddet-rcnn_refine_r50_fpn_8xb2-30e_crowdhuman.py: -------------------------------------------------------------------------------- 1 | _base_ = './crowddet-rcnn_r50_fpn_8xb2-30e_crowdhuman.py' 2 | 3 | model = dict(roi_head=dict(bbox_head=dict(with_refine=True))) 4 | -------------------------------------------------------------------------------- /configs/dcn/cascade-mask-rcnn_r101-dconv-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade-mask-rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/dcn/cascade-mask-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/dcn/cascade-mask-rcnn_x101-32x4d-dconv-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/dcn/cascade-rcnn_r101-dconv-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade-rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/dcn/cascade-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/dcn/faster-rcnn_r101-dconv-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster-rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/dcn/faster-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/dcn/faster-rcnn_r50_fpn_dpool_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | roi_head=dict( 4 | bbox_roi_extractor=dict( 5 | type='SingleRoIExtractor', 6 | roi_layer=dict( 7 | _delete_=True, 8 | type='DeformRoIPoolPack', 9 | output_size=7, 10 | output_channels=256), 11 | out_channels=256, 12 | featmap_strides=[4, 8, 16, 32]))) 13 | -------------------------------------------------------------------------------- /configs/dcn/faster-rcnn_x101-32x4d-dconv-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 14 | stage_with_dcn=(False, True, True, True), 15 | init_cfg=dict( 16 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 17 | -------------------------------------------------------------------------------- /configs/dcn/mask-rcnn_r101-dconv-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/dcn/mask-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/dcn/mask-rcnn_r50-dconv-c3-c5_fpn_amp-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | 7 | # MMEngine support the following two ways, users can choose 8 | # according to convenience 9 | # optim_wrapper = dict(type='AmpOptimWrapper') 10 | _base_.optim_wrapper.type = 'AmpOptimWrapper' 11 | -------------------------------------------------------------------------------- /configs/dcnv2/faster-rcnn_r50-mdconv-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/dcnv2/faster-rcnn_r50-mdconv-group4-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCNv2', deform_groups=4, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/dcnv2/faster-rcnn_r50_fpn_mdpool_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | roi_head=dict( 4 | bbox_roi_extractor=dict( 5 | type='SingleRoIExtractor', 6 | roi_layer=dict( 7 | _delete_=True, 8 | type='ModulatedDeformRoIPoolPack', 9 | output_size=7, 10 | output_channels=256), 11 | out_channels=256, 12 | featmap_strides=[4, 8, 16, 32]))) 13 | -------------------------------------------------------------------------------- /configs/dcnv2/mask-rcnn_r50-mdconv-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | -------------------------------------------------------------------------------- /configs/dcnv2/mask-rcnn_r50-mdconv-c3-c5_fpn_amp-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True))) 6 | 7 | # MMEngine support the following two ways, users can choose 8 | # according to convenience 9 | # optim_wrapper = dict(type='AmpOptimWrapper') 10 | _base_.optim_wrapper.type = 'AmpOptimWrapper' 11 | -------------------------------------------------------------------------------- /configs/deformable_detr/deformable-detr-refine-twostage_r50_16xb2-50e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'deformable-detr-refine_r50_16xb2-50e_coco.py' 2 | model = dict(as_two_stage=True) 3 | -------------------------------------------------------------------------------- /configs/deformable_detr/deformable-detr-refine_r50_16xb2-50e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'deformable-detr_r50_16xb2-50e_coco.py' 2 | model = dict(with_box_refine=True) 3 | -------------------------------------------------------------------------------- /configs/detectors/cascade-rcnn_r50-sac_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/cascade-rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_detection.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | 7 | model = dict( 8 | backbone=dict( 9 | type='DetectoRS_ResNet', 10 | conv_cfg=dict(type='ConvAWS'), 11 | sac=dict(type='SAC', use_deform=True), 12 | stage_with_sac=(False, True, True, True))) 13 | -------------------------------------------------------------------------------- /configs/detectors/htc_r50-sac_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../htc/htc_r50_fpn_1x_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | type='DetectoRS_ResNet', 6 | conv_cfg=dict(type='ConvAWS'), 7 | sac=dict(type='SAC', use_deform=True), 8 | stage_with_sac=(False, True, True, True))) 9 | -------------------------------------------------------------------------------- /configs/detr/detr_r101_8xb2-500e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './detr_r50_8xb2-500e_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/detr/detr_r18_8xb2-500e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './detr_r50_8xb2-500e_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=18, 6 | init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')), 7 | neck=dict(in_channels=[512])) 8 | -------------------------------------------------------------------------------- /configs/dino/dino-4scale_r50_8xb2-24e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './dino-4scale_r50_8xb2-12e_coco.py' 2 | max_epochs = 24 3 | train_cfg = dict( 4 | type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1) 5 | param_scheduler = [ 6 | dict( 7 | type='MultiStepLR', 8 | begin=0, 9 | end=max_epochs, 10 | by_epoch=True, 11 | milestones=[20], 12 | gamma=0.1) 13 | ] 14 | -------------------------------------------------------------------------------- /configs/dino/dino-4scale_r50_8xb2-36e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './dino-4scale_r50_8xb2-12e_coco.py' 2 | max_epochs = 36 3 | train_cfg = dict( 4 | type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1) 5 | param_scheduler = [ 6 | dict( 7 | type='MultiStepLR', 8 | begin=0, 9 | end=max_epochs, 10 | by_epoch=True, 11 | milestones=[30], 12 | gamma=0.1) 13 | ] 14 | -------------------------------------------------------------------------------- /configs/dino/dino-5scale_swin-l_8xb2-36e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './dino-5scale_swin-l_8xb2-12e_coco.py' 2 | max_epochs = 36 3 | train_cfg = dict( 4 | type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1) 5 | param_scheduler = [ 6 | dict( 7 | type='MultiStepLR', 8 | begin=0, 9 | end=max_epochs, 10 | by_epoch=True, 11 | milestones=[27, 33], 12 | gamma=0.1) 13 | ] 14 | -------------------------------------------------------------------------------- /configs/empirical_attention/faster-rcnn_r50-attn0010_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict(plugins=[ 4 | dict( 5 | cfg=dict( 6 | type='GeneralizedAttention', 7 | spatial_range=-1, 8 | num_heads=8, 9 | attention_type='0010', 10 | kv_stride=2), 11 | stages=(False, False, True, True), 12 | position='after_conv2') 13 | ])) 14 | -------------------------------------------------------------------------------- /configs/empirical_attention/faster-rcnn_r50-attn1111_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict(plugins=[ 4 | dict( 5 | cfg=dict( 6 | type='GeneralizedAttention', 7 | spatial_range=-1, 8 | num_heads=8, 9 | attention_type='1111', 10 | kv_stride=2), 11 | stages=(False, False, True, True), 12 | position='after_conv2') 13 | ])) 14 | -------------------------------------------------------------------------------- /configs/fast_rcnn/fast-rcnn_r101-caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fast-rcnn_r50-caffe_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 8 | -------------------------------------------------------------------------------- /configs/fast_rcnn/fast-rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fast-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/fast_rcnn/fast-rcnn_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fast-rcnn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/fast_rcnn/fast-rcnn_r50-caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fast-rcnn_r50_fpn_1x_coco.py' 2 | 3 | model = dict( 4 | data_preprocessor=dict( 5 | type='DetDataPreprocessor', 6 | mean=[103.530, 116.280, 123.675], 7 | std=[1.0, 1.0, 1.0], 8 | bgr_to_rgb=False, 9 | pad_size_divisor=32), 10 | backbone=dict( 11 | norm_cfg=dict(type='BN', requires_grad=False), 12 | style='caffe', 13 | norm_eval=True, 14 | init_cfg=dict( 15 | type='Pretrained', 16 | checkpoint='open-mmlab://detectron2/resnet50_caffe'))) 17 | -------------------------------------------------------------------------------- /configs/fast_rcnn/fast-rcnn_r50_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fast-rcnn_r50_fpn_1x_coco.py' 2 | 3 | train_cfg = dict(max_epochs=24) 4 | param_scheduler = [ 5 | dict( 6 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 7 | dict( 8 | type='MultiStepLR', 9 | begin=0, 10 | end=24, 11 | by_epoch=True, 12 | milestones=[16, 22], 13 | gamma=0.1) 14 | ] 15 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r101-caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50-caffe_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 8 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r101-caffe_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'faster-rcnn_r50_fpn_ms-3x_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | norm_cfg=dict(requires_grad=False), 7 | norm_eval=True, 8 | style='caffe', 9 | init_cfg=dict( 10 | type='Pretrained', 11 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 12 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r101_fpn_8xb8-amp-lsj-200e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r101_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'faster-rcnn_r50_fpn_ms-3x_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r18_fpn_8xb8-amp-lsj-200e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=18, 6 | init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')), 7 | neck=dict(in_channels=[64, 128, 256, 512])) 8 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r50-caffe-c4_ms-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50-caffe_c4-1x_coco.py' 2 | 3 | train_pipeline = [ 4 | dict(type='LoadImageFromFile', backend_args=_base_.backend_args), 5 | dict(type='LoadAnnotations', with_bbox=True), 6 | dict( 7 | type='RandomChoiceResize', 8 | scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768), 9 | (1333, 800)], 10 | keep_ratio=True), 11 | dict(type='RandomFlip', prob=0.5), 12 | dict(type='PackDetInputs') 13 | ] 14 | _base_.train_dataloader.dataset.pipeline = train_pipeline 15 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r50-caffe-dc5_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/faster-rcnn_r50-caffe-dc5.py', 3 | '../_base_/datasets/coco_detection.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r50-caffe-dc5_ms-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'faster-rcnn_r50-caffe-dc5_1x_coco.py' 2 | 3 | train_pipeline = [ 4 | dict(type='LoadImageFromFile', backend_args=_base_.backend_args), 5 | dict(type='LoadAnnotations', with_bbox=True), 6 | dict( 7 | type='RandomChoiceResize', 8 | scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768), 9 | (1333, 800)], 10 | keep_ratio=True), 11 | dict(type='RandomFlip', prob=0.5), 12 | dict(type='PackDetInputs') 13 | ] 14 | _base_.train_dataloader.dataset.pipeline = train_pipeline 15 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r50-caffe-dc5_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50-caffe-dc5_ms-1x_coco.py' 2 | 3 | # MMEngine support the following two ways, users can choose 4 | # according to convenience 5 | # param_scheduler = [ 6 | # dict( 7 | # type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), # noqa 8 | # dict( 9 | # type='MultiStepLR', 10 | # begin=0, 11 | # end=12, 12 | # by_epoch=True, 13 | # milestones=[28, 34], 14 | # gamma=0.1) 15 | # ] 16 | _base_.param_scheduler[1].milestones = [28, 34] 17 | 18 | train_cfg = dict(max_epochs=36) 19 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r50-caffe_c4-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/faster-rcnn_r50-caffe-c4.py', 3 | '../_base_/datasets/coco_detection.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | data_preprocessor=dict( 4 | type='DetDataPreprocessor', 5 | mean=[103.530, 116.280, 123.675], 6 | std=[1.0, 1.0, 1.0], 7 | bgr_to_rgb=False, 8 | pad_size_divisor=32), 9 | backbone=dict( 10 | norm_cfg=dict(requires_grad=False), 11 | norm_eval=True, 12 | style='caffe', 13 | init_cfg=dict( 14 | type='Pretrained', 15 | checkpoint='open-mmlab://detectron2/resnet50_caffe'))) 16 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco-person.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50-caffe_fpn_ms-1x_coco.py' 2 | model = dict(roi_head=dict(bbox_head=dict(num_classes=1))) 3 | metainfo = { 4 | 'classes': ('person', ), 5 | 'palette': [ 6 | (220, 20, 60), 7 | ] 8 | } 9 | 10 | train_dataloader = dict(dataset=dict(metainfo=metainfo)) 11 | val_dataloader = dict(dataset=dict(metainfo=metainfo)) 12 | test_dataloader = dict(dataset=dict(metainfo=metainfo)) 13 | 14 | load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth' # noqa 15 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50-caffe_fpn_ms-1x_coco.py' 2 | 3 | # MMEngine support the following two ways, users can choose 4 | # according to convenience 5 | # param_scheduler = [ 6 | # dict( 7 | # type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), # noqa 8 | # dict( 9 | # type='MultiStepLR', 10 | # begin=0, 11 | # end=12, 12 | # by_epoch=True, 13 | # milestones=[16, 23], 14 | # gamma=0.1) 15 | # ] 16 | _base_.param_scheduler[1].milestones = [16, 23] 17 | 18 | train_cfg = dict(max_epochs=24) 19 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'faster-rcnn_r50_fpn_ms-3x_coco.py' 2 | model = dict( 3 | data_preprocessor=dict( 4 | type='DetDataPreprocessor', 5 | mean=[103.530, 116.280, 123.675], 6 | std=[1.0, 1.0, 1.0], 7 | bgr_to_rgb=False, 8 | pad_size_divisor=32), 9 | backbone=dict( 10 | norm_cfg=dict(requires_grad=False), 11 | norm_eval=True, 12 | style='caffe', 13 | init_cfg=dict( 14 | type='Pretrained', 15 | checkpoint='open-mmlab://detectron2/resnet50_caffe'))) 16 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r50-tnr-pre_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/faster-rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_detection.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | 7 | checkpoint = 'https://download.pytorch.org/models/resnet50-11ad3fa6.pth' 8 | model = dict( 9 | backbone=dict(init_cfg=dict(type='Pretrained', checkpoint=checkpoint))) 10 | 11 | # `lr` and `weight_decay` have been searched to be optimal. 12 | optim_wrapper = dict( 13 | optimizer=dict(_delete_=True, type='AdamW', lr=0.0001, weight_decay=0.1), 14 | paramwise_cfg=dict(norm_decay_mult=0., bypass_duplicate=True)) 15 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/faster-rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_detection.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r50_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/faster-rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_detection.py', 4 | '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r50_fpn_amp-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50_fpn_1x_coco.py' 2 | 3 | # MMEngine support the following two ways, users can choose 4 | # according to convenience 5 | # optim_wrapper = dict(type='AmpOptimWrapper') 6 | _base_.optim_wrapper.type = 'AmpOptimWrapper' 7 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r50_fpn_bounded-iou_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | roi_head=dict( 4 | bbox_head=dict( 5 | reg_decoded_bbox=True, 6 | loss_bbox=dict(type='BoundedIoULoss', loss_weight=10.0)))) 7 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r50_fpn_ciou_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | roi_head=dict( 4 | bbox_head=dict( 5 | reg_decoded_bbox=True, 6 | loss_bbox=dict(type='CIoULoss', loss_weight=12.0)))) 7 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r50_fpn_giou_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | roi_head=dict( 4 | bbox_head=dict( 5 | reg_decoded_bbox=True, 6 | loss_bbox=dict(type='GIoULoss', loss_weight=10.0)))) 7 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r50_fpn_iou_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | roi_head=dict( 4 | bbox_head=dict( 5 | reg_decoded_bbox=True, 6 | loss_bbox=dict(type='IoULoss', loss_weight=10.0)))) 7 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r50_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = ['../common/ms_3x_coco.py', '../_base_/models/faster-rcnn_r50_fpn.py'] 2 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r50_fpn_ohem_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50_fpn_1x_coco.py' 2 | model = dict(train_cfg=dict(rcnn=dict(sampler=dict(type='OHEMSampler')))) 3 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_r50_fpn_soft-nms_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/faster-rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_detection.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | 7 | model = dict( 8 | test_cfg=dict( 9 | rcnn=dict( 10 | score_thr=0.05, 11 | nms=dict(type='soft_nms', iou_threshold=0.5), 12 | max_per_img=100))) 13 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_x101-32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_x101-32x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_x101-32x4d_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = ['../common/ms_3x_coco.py', '../_base_/models/faster-rcnn_r50_fpn.py'] 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_x101-64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_x101-64x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/faster_rcnn/faster-rcnn_x101-64x4d_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = ['../common/ms_3x_coco.py', '../_base_/models/faster-rcnn_r50_fpn.py'] 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/fcos/fcos_r101-caffe_fpn_gn-head-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py' 2 | 3 | # model settings 4 | model = dict( 5 | backbone=dict( 6 | depth=101, 7 | init_cfg=dict( 8 | type='Pretrained', 9 | checkpoint='open-mmlab://detectron/resnet101_caffe'))) 10 | -------------------------------------------------------------------------------- /configs/fcos/fcos_r101_fpn_gn-head-center-normbbox-centeronreg-giou_8xb8-amp-lsj-200e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcos_r50_fpn_gn-head-center-normbbox-centeronreg-giou_8xb8-amp-lsj-200e_coco.py' # noqa 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/fcos/fcos_r18_fpn_gn-head-center-normbbox-centeronreg-giou_8xb8-amp-lsj-200e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcos_r50_fpn_gn-head-center-normbbox-centeronreg-giou_8xb8-amp-lsj-200e_coco.py' # noqa 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=18, 6 | init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')), 7 | neck=dict(in_channels=[64, 128, 256, 512])) 8 | -------------------------------------------------------------------------------- /configs/fcos/fcos_r50-caffe_fpn_gn-head-center_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py' 2 | 3 | # model settings 4 | model = dict(bbox_head=dict(center_sampling=True, center_sample_radius=1.5)) 5 | -------------------------------------------------------------------------------- /configs/fcos/fcos_r50-caffe_fpn_gn-head_4xb4-1x_coco.py: -------------------------------------------------------------------------------- 1 | # TODO: Remove this config after benchmarking all related configs 2 | _base_ = 'fcos_r50-caffe_fpn_gn-head_1x_coco.py' 3 | 4 | # dataset settings 5 | train_dataloader = dict(batch_size=4, num_workers=4) 6 | -------------------------------------------------------------------------------- /configs/foveabox/fovea_r101_fpn_4xb4-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fovea_r50_fpn_4xb4-1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/foveabox/fovea_r101_fpn_4xb4-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fovea_r50_fpn_4xb4-2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/foveabox/fovea_r50_fpn_4xb4-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fovea_r50_fpn_4xb4-1x_coco.py' 2 | # learning policy 3 | max_epochs = 24 4 | param_scheduler = [ 5 | dict( 6 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 7 | dict( 8 | type='MultiStepLR', 9 | begin=0, 10 | end=max_epochs, 11 | by_epoch=True, 12 | milestones=[16, 22], 13 | gamma=0.1) 14 | ] 15 | train_cfg = dict(max_epochs=max_epochs) 16 | -------------------------------------------------------------------------------- /configs/fpg/faster-rcnn_r50_fpg-chn128_crop640-50e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'faster-rcnn_r50_fpg_crop640-50e_coco.py' 2 | 3 | norm_cfg = dict(type='BN', requires_grad=True) 4 | model = dict( 5 | neck=dict(out_channels=128, inter_channels=128), 6 | rpn_head=dict(in_channels=128), 7 | roi_head=dict( 8 | bbox_roi_extractor=dict(out_channels=128), 9 | bbox_head=dict(in_channels=128))) 10 | -------------------------------------------------------------------------------- /configs/fpg/mask-rcnn_r50_fpg-chn128_crop640-50e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'mask-rcnn_r50_fpg_crop640-50e_coco.py' 2 | 3 | model = dict( 4 | neck=dict(out_channels=128, inter_channels=128), 5 | rpn_head=dict(in_channels=128), 6 | roi_head=dict( 7 | bbox_roi_extractor=dict(out_channels=128), 8 | bbox_head=dict(in_channels=128), 9 | mask_roi_extractor=dict(out_channels=128), 10 | mask_head=dict(in_channels=128))) 11 | -------------------------------------------------------------------------------- /configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'retinanet_r50_fpg_crop640_50e_coco.py' 2 | 3 | model = dict( 4 | neck=dict(out_channels=128, inter_channels=128), 5 | bbox_head=dict(in_channels=128)) 6 | -------------------------------------------------------------------------------- /configs/free_anchor/freeanchor_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './freeanchor_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/free_anchor/freeanchor_x101-32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './freeanchor_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | style='pytorch', 12 | init_cfg=dict( 13 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 14 | -------------------------------------------------------------------------------- /configs/fsaf/fsaf_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fsaf_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/fsaf/fsaf_x101-64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fsaf_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn-dconv-c3-c5-r16-gcb-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../dcn/cascade-mask-rcnn_x101-32x4d-dconv-c3-c5_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), 5 | norm_eval=False, 6 | plugins=[ 7 | dict( 8 | cfg=dict(type='ContextBlock', ratio=1. / 16), 9 | stages=(False, True, True, True), 10 | position='after_conv3') 11 | ])) 12 | -------------------------------------------------------------------------------- /configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn-dconv-c3-c5-r4-gcb-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../dcn/cascade-mask-rcnn_x101-32x4d-dconv-c3-c5_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), 5 | norm_eval=False, 6 | plugins=[ 7 | dict( 8 | cfg=dict(type='ContextBlock', ratio=1. / 4), 9 | stages=(False, True, True, True), 10 | position='after_conv3') 11 | ])) 12 | -------------------------------------------------------------------------------- /configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn-dconv-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../dcn/cascade-mask-rcnn_x101-32x4d-dconv-c3-c5_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) 5 | -------------------------------------------------------------------------------- /configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn-r16-gcb-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), 5 | norm_eval=False, 6 | plugins=[ 7 | dict( 8 | cfg=dict(type='ContextBlock', ratio=1. / 16), 9 | stages=(False, True, True, True), 10 | position='after_conv3') 11 | ])) 12 | -------------------------------------------------------------------------------- /configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn-r4-gcb-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), 5 | norm_eval=False, 6 | plugins=[ 7 | dict( 8 | cfg=dict(type='ContextBlock', ratio=1. / 4), 9 | stages=(False, True, True, True), 10 | position='after_conv3') 11 | ])) 12 | -------------------------------------------------------------------------------- /configs/gcnet/cascade-mask-rcnn_x101-32x4d-syncbn_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) 5 | -------------------------------------------------------------------------------- /configs/gcnet/mask-rcnn_r101-gcb-r16-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict(plugins=[ 4 | dict( 5 | cfg=dict(type='ContextBlock', ratio=1. / 16), 6 | stages=(False, True, True, True), 7 | position='after_conv3') 8 | ])) 9 | -------------------------------------------------------------------------------- /configs/gcnet/mask-rcnn_r101-gcb-r4-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict(plugins=[ 4 | dict( 5 | cfg=dict(type='ContextBlock', ratio=1. / 4), 6 | stages=(False, True, True, True), 7 | position='after_conv3') 8 | ])) 9 | -------------------------------------------------------------------------------- /configs/gcnet/mask-rcnn_r101-syncbn-gcb-r16-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), 5 | norm_eval=False, 6 | plugins=[ 7 | dict( 8 | cfg=dict(type='ContextBlock', ratio=1. / 16), 9 | stages=(False, True, True, True), 10 | position='after_conv3') 11 | ])) 12 | -------------------------------------------------------------------------------- /configs/gcnet/mask-rcnn_r101-syncbn-gcb-r4-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), 5 | norm_eval=False, 6 | plugins=[ 7 | dict( 8 | cfg=dict(type='ContextBlock', ratio=1. / 4), 9 | stages=(False, True, True, True), 10 | position='after_conv3') 11 | ])) 12 | -------------------------------------------------------------------------------- /configs/gcnet/mask-rcnn_r101-syncbn_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) 5 | -------------------------------------------------------------------------------- /configs/gcnet/mask-rcnn_r50-gcb-r16-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict(plugins=[ 4 | dict( 5 | cfg=dict(type='ContextBlock', ratio=1. / 16), 6 | stages=(False, True, True, True), 7 | position='after_conv3') 8 | ])) 9 | -------------------------------------------------------------------------------- /configs/gcnet/mask-rcnn_r50-gcb-r4-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict(plugins=[ 4 | dict( 5 | cfg=dict(type='ContextBlock', ratio=1. / 4), 6 | stages=(False, True, True, True), 7 | position='after_conv3') 8 | ])) 9 | -------------------------------------------------------------------------------- /configs/gcnet/mask-rcnn_r50-syncbn-gcb-r16-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), 5 | norm_eval=False, 6 | plugins=[ 7 | dict( 8 | cfg=dict(type='ContextBlock', ratio=1. / 16), 9 | stages=(False, True, True, True), 10 | position='after_conv3') 11 | ])) 12 | -------------------------------------------------------------------------------- /configs/gcnet/mask-rcnn_r50-syncbn-gcb-r4-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), 5 | norm_eval=False, 6 | plugins=[ 7 | dict( 8 | cfg=dict(type='ContextBlock', ratio=1. / 4), 9 | stages=(False, True, True, True), 10 | position='after_conv3') 11 | ])) 12 | -------------------------------------------------------------------------------- /configs/gcnet/mask-rcnn_r50-syncbn_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) 5 | -------------------------------------------------------------------------------- /configs/gcnet/mask-rcnn_x101-32x4d-syncbn-gcb-r16-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask-rcnn_x101-32x4d_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), 5 | norm_eval=False, 6 | plugins=[ 7 | dict( 8 | cfg=dict(type='ContextBlock', ratio=1. / 16), 9 | stages=(False, True, True, True), 10 | position='after_conv3') 11 | ])) 12 | -------------------------------------------------------------------------------- /configs/gcnet/mask-rcnn_x101-32x4d-syncbn-gcb-r4-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask-rcnn_x101-32x4d_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), 5 | norm_eval=False, 6 | plugins=[ 7 | dict( 8 | cfg=dict(type='ContextBlock', ratio=1. / 4), 9 | stages=(False, True, True, True), 10 | position='after_conv3') 11 | ])) 12 | -------------------------------------------------------------------------------- /configs/gcnet/mask-rcnn_x101-32x4d-syncbn_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask-rcnn_x101-32x4d_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) 5 | -------------------------------------------------------------------------------- /configs/gfl/gfl_r101-dconv-c3-c5_fpn_ms-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './gfl_r50_fpn_ms-2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNet', 5 | depth=101, 6 | num_stages=4, 7 | out_indices=(0, 1, 2, 3), 8 | frozen_stages=1, 9 | norm_cfg=dict(type='BN', requires_grad=True), 10 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 11 | stage_with_dcn=(False, True, True, True), 12 | norm_eval=True, 13 | style='pytorch', 14 | init_cfg=dict(type='Pretrained', 15 | checkpoint='torchvision://resnet101'))) 16 | -------------------------------------------------------------------------------- /configs/gfl/gfl_r101_fpn_ms-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './gfl_r50_fpn_ms-2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNet', 5 | depth=101, 6 | num_stages=4, 7 | out_indices=(0, 1, 2, 3), 8 | frozen_stages=1, 9 | norm_cfg=dict(type='BN', requires_grad=True), 10 | norm_eval=True, 11 | style='pytorch', 12 | init_cfg=dict(type='Pretrained', 13 | checkpoint='torchvision://resnet101'))) 14 | -------------------------------------------------------------------------------- /configs/gfl/gfl_x101-32x4d_fpn_ms-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './gfl_r50_fpn_ms-2x_coco.py' 2 | model = dict( 3 | type='GFL', 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=32, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | norm_eval=True, 14 | style='pytorch', 15 | init_cfg=dict( 16 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 17 | -------------------------------------------------------------------------------- /configs/ghm/retinanet_r101_fpn_ghm-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_ghm-1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/ghm/retinanet_r50_fpn_ghm-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' 2 | model = dict( 3 | bbox_head=dict( 4 | loss_cls=dict( 5 | _delete_=True, 6 | type='GHMC', 7 | bins=30, 8 | momentum=0.75, 9 | use_sigmoid=True, 10 | loss_weight=1.0), 11 | loss_bbox=dict( 12 | _delete_=True, 13 | type='GHMR', 14 | mu=0.02, 15 | bins=10, 16 | momentum=0.7, 17 | loss_weight=10.0))) 18 | optim_wrapper = dict(clip_grad=dict(max_norm=35, norm_type=2)) 19 | -------------------------------------------------------------------------------- /configs/ghm/retinanet_x101-32x4d_fpn_ghm-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_ghm-1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/ghm/retinanet_x101-64x4d_fpn_ghm-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_ghm-1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/gn+ws/faster-rcnn_r101_fpn_gn-ws-all_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', checkpoint='open-mmlab://jhu/resnet101_gn_ws'))) 7 | -------------------------------------------------------------------------------- /configs/gn+ws/faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py' 2 | conv_cfg = dict(type='ConvWS') 3 | norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) 4 | model = dict( 5 | backbone=dict( 6 | conv_cfg=conv_cfg, 7 | norm_cfg=norm_cfg, 8 | init_cfg=dict( 9 | type='Pretrained', checkpoint='open-mmlab://jhu/resnet50_gn_ws')), 10 | neck=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), 11 | roi_head=dict( 12 | bbox_head=dict( 13 | type='Shared4Conv1FCBBoxHead', 14 | conv_out_channels=256, 15 | conv_cfg=conv_cfg, 16 | norm_cfg=norm_cfg))) 17 | -------------------------------------------------------------------------------- /configs/gn+ws/faster-rcnn_x101-32x4d_fpn_gn-ws-all_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py' 2 | conv_cfg = dict(type='ConvWS') 3 | norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) 4 | model = dict( 5 | backbone=dict( 6 | type='ResNeXt', 7 | depth=101, 8 | groups=32, 9 | base_width=4, 10 | num_stages=4, 11 | out_indices=(0, 1, 2, 3), 12 | frozen_stages=1, 13 | style='pytorch', 14 | conv_cfg=conv_cfg, 15 | norm_cfg=norm_cfg, 16 | init_cfg=dict( 17 | type='Pretrained', 18 | checkpoint='open-mmlab://jhu/resnext101_32x4d_gn_ws'))) 19 | -------------------------------------------------------------------------------- /configs/gn+ws/faster-rcnn_x50-32x4d_fpn_gn-ws-all_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py' 2 | conv_cfg = dict(type='ConvWS') 3 | norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) 4 | model = dict( 5 | backbone=dict( 6 | type='ResNeXt', 7 | depth=50, 8 | groups=32, 9 | base_width=4, 10 | num_stages=4, 11 | out_indices=(0, 1, 2, 3), 12 | frozen_stages=1, 13 | style='pytorch', 14 | conv_cfg=conv_cfg, 15 | norm_cfg=norm_cfg, 16 | init_cfg=dict( 17 | type='Pretrained', 18 | checkpoint='open-mmlab://jhu/resnext50_32x4d_gn_ws'))) 19 | -------------------------------------------------------------------------------- /configs/gn+ws/mask-rcnn_r101_fpn_gn-ws-all_20-23-24e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r101_fpn_gn-ws-all_2x_coco.py' 2 | # learning policy 3 | max_epochs = 24 4 | train_cfg = dict(max_epochs=max_epochs) 5 | 6 | # learning rate 7 | param_scheduler = [ 8 | dict( 9 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 10 | dict( 11 | type='MultiStepLR', 12 | begin=0, 13 | end=max_epochs, 14 | by_epoch=True, 15 | milestones=[20, 23], 16 | gamma=0.1) 17 | ] 18 | -------------------------------------------------------------------------------- /configs/gn+ws/mask-rcnn_r101_fpn_gn-ws-all_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_gn-ws-all_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', checkpoint='open-mmlab://jhu/resnet101_gn_ws'))) 7 | -------------------------------------------------------------------------------- /configs/gn+ws/mask-rcnn_r50_fpn_gn-ws-all_20-23-24e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_gn-ws-all_2x_coco.py' 2 | # learning policy 3 | max_epochs = 24 4 | train_cfg = dict(max_epochs=max_epochs) 5 | 6 | # learning rate 7 | param_scheduler = [ 8 | dict( 9 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 10 | dict( 11 | type='MultiStepLR', 12 | begin=0, 13 | end=max_epochs, 14 | by_epoch=True, 15 | milestones=[20, 23], 16 | gamma=0.1) 17 | ] 18 | -------------------------------------------------------------------------------- /configs/gn+ws/mask-rcnn_x101-32x4d_fpn_gn-ws-all_20-23-24e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_x101-32x4d_fpn_gn-ws-all_2x_coco.py' 2 | # learning policy 3 | max_epochs = 24 4 | train_cfg = dict(max_epochs=max_epochs) 5 | 6 | # learning rate 7 | param_scheduler = [ 8 | dict( 9 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 10 | dict( 11 | type='MultiStepLR', 12 | begin=0, 13 | end=max_epochs, 14 | by_epoch=True, 15 | milestones=[20, 23], 16 | gamma=0.1) 17 | ] 18 | -------------------------------------------------------------------------------- /configs/gn+ws/mask-rcnn_x101-32x4d_fpn_gn-ws-all_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_gn-ws-all_2x_coco.py' 2 | # model settings 3 | conv_cfg = dict(type='ConvWS') 4 | norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) 5 | model = dict( 6 | backbone=dict( 7 | type='ResNeXt', 8 | depth=101, 9 | groups=32, 10 | base_width=4, 11 | num_stages=4, 12 | out_indices=(0, 1, 2, 3), 13 | frozen_stages=1, 14 | style='pytorch', 15 | conv_cfg=conv_cfg, 16 | norm_cfg=norm_cfg, 17 | init_cfg=dict( 18 | type='Pretrained', 19 | checkpoint='open-mmlab://jhu/resnext101_32x4d_gn_ws'))) 20 | -------------------------------------------------------------------------------- /configs/gn+ws/mask-rcnn_x50-32x4d_fpn_gn-ws-all_20-23-24e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_x50-32x4d_fpn_gn-ws-all_2x_coco.py' 2 | # learning policy 3 | max_epochs = 24 4 | train_cfg = dict(max_epochs=max_epochs) 5 | 6 | # learning rate 7 | param_scheduler = [ 8 | dict( 9 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 10 | dict( 11 | type='MultiStepLR', 12 | begin=0, 13 | end=max_epochs, 14 | by_epoch=True, 15 | milestones=[20, 23], 16 | gamma=0.1) 17 | ] 18 | -------------------------------------------------------------------------------- /configs/gn+ws/mask-rcnn_x50-32x4d_fpn_gn-ws-all_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_gn-ws-all_2x_coco.py' 2 | # model settings 3 | conv_cfg = dict(type='ConvWS') 4 | norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) 5 | model = dict( 6 | backbone=dict( 7 | type='ResNeXt', 8 | depth=50, 9 | groups=32, 10 | base_width=4, 11 | num_stages=4, 12 | out_indices=(0, 1, 2, 3), 13 | frozen_stages=1, 14 | style='pytorch', 15 | conv_cfg=conv_cfg, 16 | norm_cfg=norm_cfg, 17 | init_cfg=dict( 18 | type='Pretrained', 19 | checkpoint='open-mmlab://jhu/resnext50_32x4d_gn_ws'))) 20 | -------------------------------------------------------------------------------- /configs/gn/mask-rcnn_r101_fpn_gn-all_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_gn-all_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron/resnet101_gn'))) 8 | -------------------------------------------------------------------------------- /configs/gn/mask-rcnn_r101_fpn_gn-all_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r101_fpn_gn-all_2x_coco.py' 2 | 3 | # learning policy 4 | max_epochs = 36 5 | train_cfg = dict(max_epochs=max_epochs) 6 | 7 | # learning rate 8 | param_scheduler = [ 9 | dict( 10 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 11 | dict( 12 | type='MultiStepLR', 13 | begin=0, 14 | end=max_epochs, 15 | by_epoch=True, 16 | milestones=[28, 34], 17 | gamma=0.1) 18 | ] 19 | -------------------------------------------------------------------------------- /configs/gn/mask-rcnn_r50-contrib_fpn_gn-all_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50-contrib_fpn_gn-all_2x_coco.py' 2 | 3 | # learning policy 4 | max_epochs = 36 5 | train_cfg = dict(max_epochs=max_epochs) 6 | 7 | # learning rate 8 | param_scheduler = [ 9 | dict( 10 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 11 | dict( 12 | type='MultiStepLR', 13 | begin=0, 14 | end=max_epochs, 15 | by_epoch=True, 16 | milestones=[28, 34], 17 | gamma=0.1) 18 | ] 19 | -------------------------------------------------------------------------------- /configs/gn/mask-rcnn_r50_fpn_gn-all_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_gn-all_2x_coco.py' 2 | 3 | # learning policy 4 | max_epochs = 36 5 | train_cfg = dict(max_epochs=max_epochs) 6 | 7 | # learning rate 8 | param_scheduler = [ 9 | dict( 10 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 11 | dict( 12 | type='MultiStepLR', 13 | begin=0, 14 | end=max_epochs, 15 | by_epoch=True, 16 | milestones=[28, 34], 17 | gamma=0.1) 18 | ] 19 | -------------------------------------------------------------------------------- /configs/grid_rcnn/grid-rcnn_r101_fpn_gn-head_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './grid-rcnn_r50_fpn_gn-head_2x_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/grid_rcnn/grid-rcnn_r50_fpn_gn-head_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './grid-rcnn_r50_fpn_gn-head_2x_coco.py' 2 | 3 | # training schedule 4 | max_epochs = 12 5 | train_cfg = dict(max_epochs=max_epochs) 6 | 7 | # learning rate 8 | param_scheduler = [ 9 | dict( 10 | type='LinearLR', start_factor=0.0001, by_epoch=False, begin=0, 11 | end=500), 12 | dict( 13 | type='MultiStepLR', 14 | begin=0, 15 | end=max_epochs, 16 | by_epoch=True, 17 | milestones=[8, 11], 18 | gamma=0.1) 19 | ] 20 | -------------------------------------------------------------------------------- /configs/grid_rcnn/grid-rcnn_x101-32x4d_fpn_gn-head_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './grid-rcnn_r50_fpn_gn-head_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | style='pytorch', 12 | init_cfg=dict( 13 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 14 | -------------------------------------------------------------------------------- /configs/grid_rcnn/grid-rcnn_x101-64x4d_fpn_gn-head_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './grid-rcnn_x101-32x4d_fpn_gn-head_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | style='pytorch', 12 | init_cfg=dict( 13 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 14 | -------------------------------------------------------------------------------- /configs/guided_anchoring/ga-faster-rcnn_r101-caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga-faster-rcnn_r50-caffe_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 8 | -------------------------------------------------------------------------------- /configs/guided_anchoring/ga-faster-rcnn_x101-32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga-faster-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/guided_anchoring/ga-faster-rcnn_x101-64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga-faster-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/guided_anchoring/ga-retinanet_r101-caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga-retinanet_r50-caffe_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 8 | -------------------------------------------------------------------------------- /configs/guided_anchoring/ga-retinanet_x101-32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga-retinanet_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/guided_anchoring/ga-retinanet_x101-64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga-retinanet_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/guided_anchoring/ga-rpn_r101-caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga-rpn_r50-caffe_fpn_1x_coco.py' 2 | # model settings 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict( 7 | type='Pretrained', 8 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 9 | -------------------------------------------------------------------------------- /configs/guided_anchoring/ga-rpn_x101-32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga-rpn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/guided_anchoring/ga-rpn_x101-64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ga-rpn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/hrnet/cascade-mask-rcnn_hrnetv2p-w18_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-mask-rcnn_hrnetv2p-w32_20e_coco.py' 2 | # model settings 3 | model = dict( 4 | backbone=dict( 5 | extra=dict( 6 | stage2=dict(num_channels=(18, 36)), 7 | stage3=dict(num_channels=(18, 36, 72)), 8 | stage4=dict(num_channels=(18, 36, 72, 144))), 9 | init_cfg=dict( 10 | type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), 11 | neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) 12 | -------------------------------------------------------------------------------- /configs/hrnet/cascade-mask-rcnn_hrnetv2p-w40-20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-mask-rcnn_hrnetv2p-w32_20e_coco.py' 2 | # model settings 3 | model = dict( 4 | backbone=dict( 5 | type='HRNet', 6 | extra=dict( 7 | stage2=dict(num_channels=(40, 80)), 8 | stage3=dict(num_channels=(40, 80, 160)), 9 | stage4=dict(num_channels=(40, 80, 160, 320))), 10 | init_cfg=dict( 11 | type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')), 12 | neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) 13 | -------------------------------------------------------------------------------- /configs/hrnet/cascade-rcnn_hrnetv2p-w18-20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-rcnn_hrnetv2p-w32-20e_coco.py' 2 | # model settings 3 | model = dict( 4 | backbone=dict( 5 | extra=dict( 6 | stage2=dict(num_channels=(18, 36)), 7 | stage3=dict(num_channels=(18, 36, 72)), 8 | stage4=dict(num_channels=(18, 36, 72, 144))), 9 | init_cfg=dict( 10 | type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), 11 | neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) 12 | -------------------------------------------------------------------------------- /configs/hrnet/cascade-rcnn_hrnetv2p-w40-20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-rcnn_hrnetv2p-w32-20e_coco.py' 2 | # model settings 3 | model = dict( 4 | backbone=dict( 5 | type='HRNet', 6 | extra=dict( 7 | stage2=dict(num_channels=(40, 80)), 8 | stage3=dict(num_channels=(40, 80, 160)), 9 | stage4=dict(num_channels=(40, 80, 160, 320))), 10 | init_cfg=dict( 11 | type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')), 12 | neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) 13 | -------------------------------------------------------------------------------- /configs/hrnet/faster-rcnn_hrnetv2p-w18-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_hrnetv2p-w32-1x_coco.py' 2 | # model settings 3 | model = dict( 4 | backbone=dict( 5 | extra=dict( 6 | stage2=dict(num_channels=(18, 36)), 7 | stage3=dict(num_channels=(18, 36, 72)), 8 | stage4=dict(num_channels=(18, 36, 72, 144))), 9 | init_cfg=dict( 10 | type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), 11 | neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) 12 | -------------------------------------------------------------------------------- /configs/hrnet/faster-rcnn_hrnetv2p-w18-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_hrnetv2p-w18-1x_coco.py' 2 | 3 | # learning policy 4 | max_epochs = 24 5 | train_cfg = dict(max_epochs=max_epochs) 6 | param_scheduler = [ 7 | dict( 8 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 9 | dict( 10 | type='MultiStepLR', 11 | begin=0, 12 | end=max_epochs, 13 | by_epoch=True, 14 | milestones=[16, 22], 15 | gamma=0.1) 16 | ] 17 | -------------------------------------------------------------------------------- /configs/hrnet/faster-rcnn_hrnetv2p-w32_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_hrnetv2p-w32-1x_coco.py' 2 | 3 | # learning policy 4 | max_epochs = 24 5 | train_cfg = dict(max_epochs=max_epochs) 6 | param_scheduler = [ 7 | dict( 8 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 9 | dict( 10 | type='MultiStepLR', 11 | begin=0, 12 | end=max_epochs, 13 | by_epoch=True, 14 | milestones=[16, 22], 15 | gamma=0.1) 16 | ] 17 | -------------------------------------------------------------------------------- /configs/hrnet/faster-rcnn_hrnetv2p-w40-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_hrnetv2p-w32-1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='HRNet', 5 | extra=dict( 6 | stage2=dict(num_channels=(40, 80)), 7 | stage3=dict(num_channels=(40, 80, 160)), 8 | stage4=dict(num_channels=(40, 80, 160, 320))), 9 | init_cfg=dict( 10 | type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')), 11 | neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) 12 | -------------------------------------------------------------------------------- /configs/hrnet/faster-rcnn_hrnetv2p-w40_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_hrnetv2p-w40-1x_coco.py' 2 | 3 | # learning policy 4 | max_epochs = 24 5 | train_cfg = dict(max_epochs=max_epochs) 6 | param_scheduler = [ 7 | dict( 8 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 9 | dict( 10 | type='MultiStepLR', 11 | begin=0, 12 | end=max_epochs, 13 | by_epoch=True, 14 | milestones=[16, 22], 15 | gamma=0.1) 16 | ] 17 | -------------------------------------------------------------------------------- /configs/hrnet/fcos_hrnetv2p-w18-gn-head_4xb4-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcos_hrnetv2p-w32-gn-head_4xb4-1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | extra=dict( 5 | stage2=dict(num_channels=(18, 36)), 6 | stage3=dict(num_channels=(18, 36, 72)), 7 | stage4=dict(num_channels=(18, 36, 72, 144))), 8 | init_cfg=dict( 9 | type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), 10 | neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) 11 | -------------------------------------------------------------------------------- /configs/hrnet/fcos_hrnetv2p-w18-gn-head_4xb4-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcos_hrnetv2p-w18-gn-head_4xb4-1x_coco.py' 2 | 3 | # learning policy 4 | max_epochs = 24 5 | train_cfg = dict(max_epochs=max_epochs) 6 | param_scheduler = [ 7 | dict( 8 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 9 | dict( 10 | type='MultiStepLR', 11 | begin=0, 12 | end=max_epochs, 13 | by_epoch=True, 14 | milestones=[16, 22], 15 | gamma=0.1) 16 | ] 17 | -------------------------------------------------------------------------------- /configs/hrnet/fcos_hrnetv2p-w18-gn-head_ms-640-800-4xb4-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcos_hrnetv2p-w32-gn-head_ms-640-800-4xb4-2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | extra=dict( 5 | stage2=dict(num_channels=(18, 36)), 6 | stage3=dict(num_channels=(18, 36, 72)), 7 | stage4=dict(num_channels=(18, 36, 72, 144))), 8 | init_cfg=dict( 9 | type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), 10 | neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) 11 | -------------------------------------------------------------------------------- /configs/hrnet/fcos_hrnetv2p-w32-gn-head_4xb4-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcos_hrnetv2p-w32-gn-head_4xb4-1x_coco.py' 2 | 3 | # learning policy 4 | max_epochs = 24 5 | train_cfg = dict(max_epochs=max_epochs) 6 | param_scheduler = [ 7 | dict( 8 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 9 | dict( 10 | type='MultiStepLR', 11 | begin=0, 12 | end=max_epochs, 13 | by_epoch=True, 14 | milestones=[16, 22], 15 | gamma=0.1) 16 | ] 17 | -------------------------------------------------------------------------------- /configs/hrnet/fcos_hrnetv2p-w40-gn-head_ms-640-800-4xb4-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './fcos_hrnetv2p-w32-gn-head_ms-640-800-4xb4-2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='HRNet', 5 | extra=dict( 6 | stage2=dict(num_channels=(40, 80)), 7 | stage3=dict(num_channels=(40, 80, 160)), 8 | stage4=dict(num_channels=(40, 80, 160, 320))), 9 | init_cfg=dict( 10 | type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')), 11 | neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) 12 | -------------------------------------------------------------------------------- /configs/hrnet/htc_hrnetv2p-w18_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './htc_hrnetv2p-w32_20e_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | extra=dict( 5 | stage2=dict(num_channels=(18, 36)), 6 | stage3=dict(num_channels=(18, 36, 72)), 7 | stage4=dict(num_channels=(18, 36, 72, 144))), 8 | init_cfg=dict( 9 | type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), 10 | neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) 11 | -------------------------------------------------------------------------------- /configs/hrnet/htc_hrnetv2p-w40_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './htc_hrnetv2p-w32_20e_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='HRNet', 5 | extra=dict( 6 | stage2=dict(num_channels=(40, 80)), 7 | stage3=dict(num_channels=(40, 80, 160)), 8 | stage4=dict(num_channels=(40, 80, 160, 320))), 9 | init_cfg=dict( 10 | type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')), 11 | neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) 12 | -------------------------------------------------------------------------------- /configs/hrnet/htc_hrnetv2p-w40_28e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './htc_hrnetv2p-w40_20e_coco.py' 2 | 3 | # learning policy 4 | max_epochs = 28 5 | train_cfg = dict(max_epochs=max_epochs) 6 | param_scheduler = [ 7 | dict( 8 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 9 | dict( 10 | type='MultiStepLR', 11 | begin=0, 12 | end=max_epochs, 13 | by_epoch=True, 14 | milestones=[24, 27], 15 | gamma=0.1) 16 | ] 17 | -------------------------------------------------------------------------------- /configs/hrnet/htc_x101-64x4d_fpn_16xb1-28e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../htc/htc_x101-64x4d_fpn_16xb1-20e_coco.py' 2 | 3 | # learning policy 4 | max_epochs = 28 5 | train_cfg = dict(max_epochs=max_epochs) 6 | param_scheduler = [ 7 | dict( 8 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 9 | dict( 10 | type='MultiStepLR', 11 | begin=0, 12 | end=max_epochs, 13 | by_epoch=True, 14 | milestones=[24, 27], 15 | gamma=0.1) 16 | ] 17 | -------------------------------------------------------------------------------- /configs/hrnet/mask-rcnn_hrnetv2p-w18-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_hrnetv2p-w32-1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | extra=dict( 5 | stage2=dict(num_channels=(18, 36)), 6 | stage3=dict(num_channels=(18, 36, 72)), 7 | stage4=dict(num_channels=(18, 36, 72, 144))), 8 | init_cfg=dict( 9 | type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), 10 | neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) 11 | -------------------------------------------------------------------------------- /configs/hrnet/mask-rcnn_hrnetv2p-w18-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_hrnetv2p-w18-1x_coco.py' 2 | 3 | # learning policy 4 | max_epochs = 24 5 | train_cfg = dict(max_epochs=max_epochs) 6 | param_scheduler = [ 7 | dict( 8 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 9 | dict( 10 | type='MultiStepLR', 11 | begin=0, 12 | end=max_epochs, 13 | by_epoch=True, 14 | milestones=[16, 22], 15 | gamma=0.1) 16 | ] 17 | -------------------------------------------------------------------------------- /configs/hrnet/mask-rcnn_hrnetv2p-w32-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_hrnetv2p-w32-1x_coco.py' 2 | 3 | # learning policy 4 | max_epochs = 24 5 | train_cfg = dict(max_epochs=max_epochs) 6 | param_scheduler = [ 7 | dict( 8 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 9 | dict( 10 | type='MultiStepLR', 11 | begin=0, 12 | end=max_epochs, 13 | by_epoch=True, 14 | milestones=[16, 22], 15 | gamma=0.1) 16 | ] 17 | -------------------------------------------------------------------------------- /configs/hrnet/mask-rcnn_hrnetv2p-w40-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_hrnetv2p-w40_1x_coco.py' 2 | 3 | # learning policy 4 | max_epochs = 24 5 | train_cfg = dict(max_epochs=max_epochs) 6 | param_scheduler = [ 7 | dict( 8 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 9 | dict( 10 | type='MultiStepLR', 11 | begin=0, 12 | end=max_epochs, 13 | by_epoch=True, 14 | milestones=[16, 22], 15 | gamma=0.1) 16 | ] 17 | -------------------------------------------------------------------------------- /configs/hrnet/mask-rcnn_hrnetv2p-w40_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_hrnetv2p-w18-1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='HRNet', 5 | extra=dict( 6 | stage2=dict(num_channels=(40, 80)), 7 | stage3=dict(num_channels=(40, 80, 160)), 8 | stage4=dict(num_channels=(40, 80, 160, 320))), 9 | init_cfg=dict( 10 | type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')), 11 | neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) 12 | -------------------------------------------------------------------------------- /configs/htc/htc_r101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './htc_r50_fpn_20e_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/htc/htc_r50_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './htc_r50_fpn_1x_coco.py' 2 | 3 | # learning policy 4 | max_epochs = 20 5 | param_scheduler = [ 6 | dict( 7 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 8 | dict( 9 | type='MultiStepLR', 10 | begin=0, 11 | end=max_epochs, 12 | by_epoch=True, 13 | milestones=[16, 19], 14 | gamma=0.1) 15 | ] 16 | train_cfg = dict(max_epochs=max_epochs) 17 | -------------------------------------------------------------------------------- /configs/htc/htc_x101-64x4d_fpn_16xb1-20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './htc_x101-32x4d_fpn_16xb1-20e_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | groups=64, 6 | init_cfg=dict( 7 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 8 | -------------------------------------------------------------------------------- /configs/instaboost/cascade-mask-rcnn_r101_fpn_instaboost-4x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-mask-rcnn_r50_fpn_instaboost-4x_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/instaboost/cascade-mask-rcnn_x101-64x4d_fpn_instaboost-4x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-mask-rcnn_r50_fpn_instaboost-4x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/instaboost/mask-rcnn_r101_fpn_instaboost-4x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_instaboost-4x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/instaboost/mask-rcnn_x101-64x4d_fpn_instaboost-4x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_instaboost-4x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/ld/ld_r34-gflv1-r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./ld_r18-gflv1-r101_fpn_1x_coco.py'] 2 | model = dict( 3 | backbone=dict( 4 | type='ResNet', 5 | depth=34, 6 | num_stages=4, 7 | out_indices=(0, 1, 2, 3), 8 | frozen_stages=1, 9 | norm_cfg=dict(type='BN', requires_grad=True), 10 | norm_eval=True, 11 | style='pytorch', 12 | init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet34')), 13 | neck=dict( 14 | type='FPN', 15 | in_channels=[64, 128, 256, 512], 16 | out_channels=256, 17 | start_level=1, 18 | add_extra_convs='on_output', 19 | num_outs=5)) 20 | -------------------------------------------------------------------------------- /configs/ld/ld_r50-gflv1-r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./ld_r18-gflv1-r101_fpn_1x_coco.py'] 2 | model = dict( 3 | backbone=dict( 4 | type='ResNet', 5 | depth=50, 6 | num_stages=4, 7 | out_indices=(0, 1, 2, 3), 8 | frozen_stages=1, 9 | norm_cfg=dict(type='BN', requires_grad=True), 10 | norm_eval=True, 11 | style='pytorch', 12 | init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), 13 | neck=dict( 14 | type='FPN', 15 | in_channels=[256, 512, 1024, 2048], 16 | out_channels=256, 17 | start_level=1, 18 | add_extra_convs='on_output', 19 | num_outs=5)) 20 | -------------------------------------------------------------------------------- /configs/legacy_1.x/retinanet_r50-caffe_fpn_1x_coco_v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_1x_coco_v1.py' 2 | model = dict( 3 | data_preprocessor=dict( 4 | type='DetDataPreprocessor', 5 | # use caffe img_norm 6 | mean=[102.9801, 115.9465, 122.7717], 7 | std=[1.0, 1.0, 1.0], 8 | bgr_to_rgb=False, 9 | pad_size_divisor=32), 10 | backbone=dict( 11 | norm_cfg=dict(requires_grad=False), 12 | norm_eval=True, 13 | style='caffe', 14 | init_cfg=dict( 15 | type='Pretrained', 16 | checkpoint='open-mmlab://detectron/resnet50_caffe'))) 17 | -------------------------------------------------------------------------------- /configs/libra_rcnn/libra-faster-rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './libra-faster-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/libra_rcnn/libra-faster-rcnn_x101-64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './libra-faster-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/lvis/mask-rcnn_r101_fpn_sample1e-3_ms-1x_lvis-v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_sample1e-3_ms-1x_lvis-v1.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/lvis/mask-rcnn_r101_fpn_sample1e-3_ms-2x_lvis-v0.5.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_sample1e-3_ms-2x_lvis-v0.5.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/lvis/mask-rcnn_r50_fpn_sample1e-3_ms-1x_lvis-v1.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/mask-rcnn_r50_fpn.py', 3 | '../_base_/datasets/lvis_v1_instance.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | model = dict( 7 | roi_head=dict( 8 | bbox_head=dict(num_classes=1203), mask_head=dict(num_classes=1203)), 9 | test_cfg=dict( 10 | rcnn=dict( 11 | score_thr=0.0001, 12 | # LVIS allows up to 300 13 | max_per_img=300))) 14 | -------------------------------------------------------------------------------- /configs/lvis/mask-rcnn_r50_fpn_sample1e-3_ms-2x_lvis-v0.5.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/mask-rcnn_r50_fpn.py', 3 | '../_base_/datasets/lvis_v0.5_instance.py', 4 | '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' 5 | ] 6 | model = dict( 7 | roi_head=dict( 8 | bbox_head=dict(num_classes=1230), mask_head=dict(num_classes=1230)), 9 | test_cfg=dict( 10 | rcnn=dict( 11 | score_thr=0.0001, 12 | # LVIS allows up to 300 13 | max_per_img=300))) 14 | -------------------------------------------------------------------------------- /configs/lvis/mask-rcnn_x101-32x4d_fpn_sample1e-3_ms-1x_lvis-v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_sample1e-3_ms-1x_lvis-v1.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/lvis/mask-rcnn_x101-32x4d_fpn_sample1e-3_ms-2x_lvis-v0.5.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_sample1e-3_ms-2x_lvis-v0.5.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/lvis/mask-rcnn_x101-64x4d_fpn_sample1e-3_ms-1x_lvis-v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_sample1e-3_ms-1x_lvis-v1.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/lvis/mask-rcnn_x101-64x4d_fpn_sample1e-3_ms-2x_lvis-v0.5.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_sample1e-3_ms-2x_lvis-v0.5.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/mask2former/mask2former_r101_8xb2-lsj-50e_coco-panoptic.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask2former_r50_8xb2-lsj-50e_coco-panoptic.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-lsj-50e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./mask2former_r50_8xb2-lsj-50e_coco.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-p4-w12-384-in21k_8xb2-lsj-50e_coco-panoptic.py: -------------------------------------------------------------------------------- 1 | _base_ = ['./mask2former_swin-b-p4-w12-384_8xb2-lsj-50e_coco-panoptic.py'] 2 | pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth' # noqa 3 | 4 | model = dict( 5 | backbone=dict(init_cfg=dict(type='Pretrained', checkpoint=pretrained))) 6 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_r101-caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50-caffe_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 8 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_r101-caffe_fpn_ms-poly-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../common/ms-poly_3x_coco-instance.py', 3 | '../_base_/models/mask-rcnn_r50_fpn.py' 4 | ] 5 | 6 | model = dict( 7 | # use caffe img_norm 8 | data_preprocessor=dict( 9 | mean=[103.530, 116.280, 123.675], 10 | std=[1.0, 1.0, 1.0], 11 | bgr_to_rgb=False), 12 | backbone=dict( 13 | depth=101, 14 | norm_cfg=dict(requires_grad=False), 15 | norm_eval=True, 16 | style='caffe', 17 | init_cfg=dict( 18 | type='Pretrained', 19 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 20 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_r101_fpn_8xb8-amp-lsj-200e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_r101_fpn_ms-poly-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../common/ms-poly_3x_coco-instance.py', 3 | '../_base_/models/mask-rcnn_r50_fpn.py' 4 | ] 5 | 6 | model = dict( 7 | backbone=dict( 8 | depth=101, 9 | init_cfg=dict(type='Pretrained', 10 | checkpoint='torchvision://resnet101'))) 11 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_r18_fpn_8xb8-amp-lsj-200e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=18, 6 | init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')), 7 | neck=dict(in_channels=[64, 128, 256, 512])) 8 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_r50-caffe-c4_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/mask-rcnn_r50-caffe-c4.py', 3 | '../_base_/datasets/coco_instance.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | # use caffe img_norm 4 | data_preprocessor=dict( 5 | mean=[103.530, 116.280, 123.675], 6 | std=[1.0, 1.0, 1.0], 7 | bgr_to_rgb=False), 8 | backbone=dict( 9 | norm_cfg=dict(requires_grad=False), 10 | style='caffe', 11 | init_cfg=dict( 12 | type='Pretrained', 13 | checkpoint='open-mmlab://detectron2/resnet50_caffe'))) 14 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_ms-poly-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50-caffe_fpn_ms-poly-1x_coco.py' 2 | 3 | train_cfg = dict(max_epochs=24) 4 | # learning rate 5 | param_scheduler = [ 6 | dict( 7 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 8 | dict( 9 | type='MultiStepLR', 10 | begin=0, 11 | end=24, 12 | by_epoch=True, 13 | milestones=[16, 22], 14 | gamma=0.1) 15 | ] 16 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_r50-caffe_fpn_ms-poly-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50-caffe_fpn_ms-poly-1x_coco.py' 2 | 3 | train_cfg = dict(max_epochs=36) 4 | # learning rate 5 | param_scheduler = [ 6 | dict( 7 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 8 | dict( 9 | type='MultiStepLR', 10 | begin=0, 11 | end=24, 12 | by_epoch=True, 13 | milestones=[28, 34], 14 | gamma=0.1) 15 | ] 16 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_r50_fpn_1x-wandb_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/mask-rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_instance.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | 7 | vis_backends = [dict(type='LocalVisBackend'), dict(type='WandBVisBackend')] 8 | visualizer = dict(vis_backends=vis_backends) 9 | 10 | # MMEngine support the following two ways, users can choose 11 | # according to convenience 12 | # default_hooks = dict(checkpoint=dict(interval=4)) 13 | _base_.default_hooks.checkpoint.interval = 4 14 | 15 | # train_cfg = dict(val_interval=2) 16 | _base_.train_cfg.val_interval = 2 17 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/mask-rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_instance.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_r50_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/mask-rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_instance.py', 4 | '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' 5 | ] 6 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_r50_fpn_amp-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_1x_coco.py' 2 | 3 | # Enable automatic-mixed-precision training with AmpOptimWrapper. 4 | optim_wrapper = dict(type='AmpOptimWrapper') 5 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_r50_fpn_ms-poly-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../common/ms-poly_3x_coco-instance.py', 3 | '../_base_/models/mask-rcnn_r50_fpn.py' 4 | ] 5 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_r50_fpn_poly-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/mask-rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_instance.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | 7 | train_pipeline = [ 8 | dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}), 9 | dict( 10 | type='LoadAnnotations', 11 | with_bbox=True, 12 | with_mask=True, 13 | poly2mask=False), 14 | dict(type='Resize', scale=(1333, 800), keep_ratio=True), 15 | dict(type='RandomFlip', prob=0.5), 16 | dict(type='PackDetInputs'), 17 | ] 18 | train_dataloader = dict(dataset=dict(pipeline=train_pipeline)) 19 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_x101-32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r101_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_x101-32x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r101_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_x101-32x4d_fpn_ms-poly-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../common/ms-poly_3x_coco-instance.py', 3 | '../_base_/models/mask-rcnn_r50_fpn.py' 4 | ] 5 | 6 | model = dict( 7 | backbone=dict( 8 | type='ResNeXt', 9 | depth=101, 10 | groups=32, 11 | base_width=4, 12 | num_stages=4, 13 | out_indices=(0, 1, 2, 3), 14 | frozen_stages=1, 15 | norm_cfg=dict(type='BN', requires_grad=True), 16 | style='pytorch', 17 | init_cfg=dict( 18 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 19 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_x101-64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_x101-32x4d_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_x101-64x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_x101-32x4d_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/mask_rcnn/mask-rcnn_x101-64x4d_fpn_ms-poly_3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../common/ms-poly_3x_coco-instance.py', 3 | '../_base_/models/mask-rcnn_r50_fpn.py' 4 | ] 5 | 6 | model = dict( 7 | backbone=dict( 8 | type='ResNeXt', 9 | depth=101, 10 | groups=64, 11 | base_width=4, 12 | num_stages=4, 13 | out_indices=(0, 1, 2, 3), 14 | frozen_stages=1, 15 | norm_cfg=dict(type='BN', requires_grad=True), 16 | style='pytorch', 17 | init_cfg=dict( 18 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 19 | -------------------------------------------------------------------------------- /configs/ms_rcnn/ms-rcnn_r101-caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ms-rcnn_r50-caffe_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 8 | -------------------------------------------------------------------------------- /configs/ms_rcnn/ms-rcnn_r101-caffe_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ms-rcnn_r101-caffe_fpn_1x_coco.py' 2 | # learning policy 3 | max_epochs = 24 4 | train_cfg = dict( 5 | type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1) 6 | 7 | param_scheduler = [ 8 | dict( 9 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 10 | dict( 11 | type='MultiStepLR', 12 | begin=0, 13 | end=max_epochs, 14 | by_epoch=True, 15 | milestones=[16, 22], 16 | gamma=0.1) 17 | ] 18 | -------------------------------------------------------------------------------- /configs/ms_rcnn/ms-rcnn_r50-caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask-rcnn_r50-caffe_fpn_1x_coco.py' 2 | model = dict( 3 | type='MaskScoringRCNN', 4 | roi_head=dict( 5 | type='MaskScoringRoIHead', 6 | mask_iou_head=dict( 7 | type='MaskIoUHead', 8 | num_convs=4, 9 | num_fcs=2, 10 | roi_feat_size=14, 11 | in_channels=256, 12 | conv_out_channels=256, 13 | fc_out_channels=1024, 14 | num_classes=80)), 15 | # model training and testing settings 16 | train_cfg=dict(rcnn=dict(mask_thr_binary=0.5))) 17 | -------------------------------------------------------------------------------- /configs/ms_rcnn/ms-rcnn_r50-caffe_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ms-rcnn_r50-caffe_fpn_1x_coco.py' 2 | # learning policy 3 | max_epochs = 24 4 | train_cfg = dict( 5 | type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1) 6 | 7 | param_scheduler = [ 8 | dict( 9 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 10 | dict( 11 | type='MultiStepLR', 12 | begin=0, 13 | end=max_epochs, 14 | by_epoch=True, 15 | milestones=[16, 22], 16 | gamma=0.1) 17 | ] 18 | -------------------------------------------------------------------------------- /configs/ms_rcnn/ms-rcnn_r50_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | type='MaskScoringRCNN', 4 | roi_head=dict( 5 | type='MaskScoringRoIHead', 6 | mask_iou_head=dict( 7 | type='MaskIoUHead', 8 | num_convs=4, 9 | num_fcs=2, 10 | roi_feat_size=14, 11 | in_channels=256, 12 | conv_out_channels=256, 13 | fc_out_channels=1024, 14 | num_classes=80)), 15 | # model training and testing settings 16 | train_cfg=dict(rcnn=dict(mask_thr_binary=0.5))) 17 | -------------------------------------------------------------------------------- /configs/ms_rcnn/ms-rcnn_x101-32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ms-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/ms_rcnn/ms-rcnn_x101-64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ms-rcnn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/ms_rcnn/ms-rcnn_x101-64x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './ms-rcnn_x101-64x4d_fpn_1x_coco.py' 2 | # learning policy 3 | max_epochs = 24 4 | train_cfg = dict( 5 | type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1) 6 | 7 | param_scheduler = [ 8 | dict( 9 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 10 | dict( 11 | type='MultiStepLR', 12 | begin=0, 13 | end=max_epochs, 14 | by_epoch=True, 15 | milestones=[16, 22], 16 | gamma=0.1) 17 | ] 18 | -------------------------------------------------------------------------------- /configs/nas_fpn/retinanet_r50_nasfpn_crop640-50e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_crop640-50e_coco.py' 2 | 3 | # model settings 4 | model = dict( 5 | # `pad_size_divisor=128` ensures the feature maps sizes 6 | # in `NAS_FPN` won't mismatch. 7 | data_preprocessor=dict(pad_size_divisor=128), 8 | neck=dict( 9 | _delete_=True, 10 | type='NASFPN', 11 | in_channels=[256, 512, 1024, 2048], 12 | out_channels=256, 13 | num_outs=5, 14 | stack_times=7, 15 | start_level=1, 16 | norm_cfg=dict(type='BN', requires_grad=True))) 17 | -------------------------------------------------------------------------------- /configs/openimages/faster-rcnn_r50_fpn_32xb2-cas-1x_openimages-challenge.py: -------------------------------------------------------------------------------- 1 | _base_ = ['faster-rcnn_r50_fpn_32xb2-1x_openimages-challenge.py'] 2 | 3 | # Use ClassAwareSampler 4 | train_dataloader = dict( 5 | sampler=dict(_delete_=True, type='ClassAwareSampler', num_sample_class=1)) 6 | -------------------------------------------------------------------------------- /configs/openimages/faster-rcnn_r50_fpn_32xb2-cas-1x_openimages.py: -------------------------------------------------------------------------------- 1 | _base_ = ['faster-rcnn_r50_fpn_32xb2-1x_openimages.py'] 2 | 3 | # Use ClassAwareSampler 4 | train_dataloader = dict( 5 | sampler=dict(_delete_=True, type='ClassAwareSampler', num_sample_class=1)) 6 | -------------------------------------------------------------------------------- /configs/paa/paa_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './paa_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/paa/paa_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './paa_r101_fpn_1x_coco.py' 2 | max_epochs = 24 3 | 4 | # learning rate 5 | param_scheduler = [ 6 | dict( 7 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 8 | dict( 9 | type='MultiStepLR', 10 | begin=0, 11 | end=max_epochs, 12 | by_epoch=True, 13 | milestones=[16, 22], 14 | gamma=0.1) 15 | ] 16 | 17 | # training schedule for 2x 18 | train_cfg = dict(max_epochs=max_epochs) 19 | -------------------------------------------------------------------------------- /configs/paa/paa_r101_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './paa_r50_fpn_ms-3x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/paa/paa_r50_fpn_1.5x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './paa_r50_fpn_1x_coco.py' 2 | max_epochs = 18 3 | 4 | # learning rate 5 | param_scheduler = [ 6 | dict( 7 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 8 | dict( 9 | type='MultiStepLR', 10 | begin=0, 11 | end=max_epochs, 12 | by_epoch=True, 13 | milestones=[12, 16], 14 | gamma=0.1) 15 | ] 16 | 17 | # training schedule for 1.5x 18 | train_cfg = dict(max_epochs=max_epochs) 19 | -------------------------------------------------------------------------------- /configs/paa/paa_r50_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './paa_r50_fpn_1x_coco.py' 2 | max_epochs = 24 3 | 4 | # learning rate 5 | param_scheduler = [ 6 | dict( 7 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 8 | dict( 9 | type='MultiStepLR', 10 | begin=0, 11 | end=max_epochs, 12 | by_epoch=True, 13 | milestones=[16, 22], 14 | gamma=0.1) 15 | ] 16 | 17 | # training schedule for 2x 18 | train_cfg = dict(max_epochs=max_epochs) 19 | -------------------------------------------------------------------------------- /configs/pafpn/faster-rcnn_r50_pafpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py' 2 | 3 | model = dict( 4 | neck=dict( 5 | type='PAFPN', 6 | in_channels=[256, 512, 1024, 2048], 7 | out_channels=256, 8 | num_outs=5)) 9 | -------------------------------------------------------------------------------- /configs/panoptic_fpn/panoptic-fpn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './panoptic-fpn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/panoptic_fpn/panoptic-fpn_r101_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './panoptic-fpn_r50_fpn_ms-3x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/pisa/retinanet-r50_fpn_pisa_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' 2 | 3 | model = dict( 4 | bbox_head=dict( 5 | type='PISARetinaHead', 6 | loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)), 7 | train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2))) 8 | -------------------------------------------------------------------------------- /configs/pisa/retinanet_x101-32x4d_fpn_pisa_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../retinanet/retinanet_x101-32x4d_fpn_1x_coco.py' 2 | 3 | model = dict( 4 | bbox_head=dict( 5 | type='PISARetinaHead', 6 | loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)), 7 | train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2))) 8 | -------------------------------------------------------------------------------- /configs/pisa/ssd300_pisa_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../ssd/ssd300_coco.py' 2 | 3 | model = dict( 4 | bbox_head=dict(type='PISASSDHead'), 5 | train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2))) 6 | 7 | optim_wrapper = dict(clip_grad=dict(max_norm=35, norm_type=2)) 8 | -------------------------------------------------------------------------------- /configs/pisa/ssd512_pisa_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../ssd/ssd512_coco.py' 2 | 3 | model = dict( 4 | bbox_head=dict(type='PISASSDHead'), 5 | train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2))) 6 | 7 | optim_wrapper = dict(clip_grad=dict(max_norm=35, norm_type=2)) 8 | -------------------------------------------------------------------------------- /configs/point_rend/point-rend_r50-caffe_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './point-rend_r50-caffe_fpn_ms-1x_coco.py' 2 | 3 | max_epochs = 36 4 | 5 | # learning policy 6 | param_scheduler = [ 7 | dict( 8 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 9 | dict( 10 | type='MultiStepLR', 11 | begin=0, 12 | end=max_epochs, 13 | by_epoch=True, 14 | milestones=[28, 34], 15 | gamma=0.1) 16 | ] 17 | 18 | train_cfg = dict(max_epochs=max_epochs) 19 | -------------------------------------------------------------------------------- /configs/pvt/retinanet_pvt-l_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'retinanet_pvt-t_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | num_layers=[3, 8, 27, 3], 5 | init_cfg=dict(checkpoint='https://github.com/whai362/PVT/' 6 | 'releases/download/v2/pvt_large.pth'))) 7 | # Enable automatic-mixed-precision training with AmpOptimWrapper. 8 | optim_wrapper = dict(type='AmpOptimWrapper') 9 | -------------------------------------------------------------------------------- /configs/pvt/retinanet_pvt-m_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'retinanet_pvt-t_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | num_layers=[3, 4, 18, 3], 5 | init_cfg=dict(checkpoint='https://github.com/whai362/PVT/' 6 | 'releases/download/v2/pvt_medium.pth'))) 7 | -------------------------------------------------------------------------------- /configs/pvt/retinanet_pvt-s_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'retinanet_pvt-t_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | num_layers=[3, 4, 6, 3], 5 | init_cfg=dict(checkpoint='https://github.com/whai362/PVT/' 6 | 'releases/download/v2/pvt_small.pth'))) 7 | -------------------------------------------------------------------------------- /configs/pvt/retinanet_pvtv2-b1_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | embed_dims=64, 5 | init_cfg=dict(checkpoint='https://github.com/whai362/PVT/' 6 | 'releases/download/v2/pvt_v2_b1.pth')), 7 | neck=dict(in_channels=[64, 128, 320, 512])) 8 | -------------------------------------------------------------------------------- /configs/pvt/retinanet_pvtv2-b2_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | embed_dims=64, 5 | num_layers=[3, 4, 6, 3], 6 | init_cfg=dict(checkpoint='https://github.com/whai362/PVT/' 7 | 'releases/download/v2/pvt_v2_b2.pth')), 8 | neck=dict(in_channels=[64, 128, 320, 512])) 9 | -------------------------------------------------------------------------------- /configs/pvt/retinanet_pvtv2-b3_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | embed_dims=64, 5 | num_layers=[3, 4, 18, 3], 6 | init_cfg=dict(checkpoint='https://github.com/whai362/PVT/' 7 | 'releases/download/v2/pvt_v2_b3.pth')), 8 | neck=dict(in_channels=[64, 128, 320, 512])) 9 | -------------------------------------------------------------------------------- /configs/queryinst/queryinst_r101_fpn_300-proposals_crop-ms-480-800-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './queryinst_r50_fpn_300-proposals_crop-ms-480-800-3x_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/queryinst/queryinst_r101_fpn_ms-480-800-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './queryinst_r50_fpn_ms-480-800-3x_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/regnet/cascade-mask-rcnn_regnetx-1.6GF_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='RegNet', 5 | arch='regnetx_1.6gf', 6 | out_indices=(0, 1, 2, 3), 7 | frozen_stages=1, 8 | norm_cfg=dict(type='BN', requires_grad=True), 9 | norm_eval=True, 10 | style='pytorch', 11 | init_cfg=dict( 12 | type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')), 13 | neck=dict( 14 | type='FPN', 15 | in_channels=[72, 168, 408, 912], 16 | out_channels=256, 17 | num_outs=5)) 18 | -------------------------------------------------------------------------------- /configs/regnet/cascade-mask-rcnn_regnetx-400MF_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='RegNet', 5 | arch='regnetx_400mf', 6 | out_indices=(0, 1, 2, 3), 7 | frozen_stages=1, 8 | norm_cfg=dict(type='BN', requires_grad=True), 9 | norm_eval=True, 10 | style='pytorch', 11 | init_cfg=dict( 12 | type='Pretrained', checkpoint='open-mmlab://regnetx_400mf')), 13 | neck=dict( 14 | type='FPN', 15 | in_channels=[32, 64, 160, 384], 16 | out_channels=256, 17 | num_outs=5)) 18 | -------------------------------------------------------------------------------- /configs/regnet/cascade-mask-rcnn_regnetx-4GF_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='RegNet', 5 | arch='regnetx_4.0gf', 6 | out_indices=(0, 1, 2, 3), 7 | frozen_stages=1, 8 | norm_cfg=dict(type='BN', requires_grad=True), 9 | norm_eval=True, 10 | style='pytorch', 11 | init_cfg=dict( 12 | type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')), 13 | neck=dict( 14 | type='FPN', 15 | in_channels=[80, 240, 560, 1360], 16 | out_channels=256, 17 | num_outs=5)) 18 | -------------------------------------------------------------------------------- /configs/regnet/cascade-mask-rcnn_regnetx-800MF_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='RegNet', 5 | arch='regnetx_800mf', 6 | out_indices=(0, 1, 2, 3), 7 | frozen_stages=1, 8 | norm_cfg=dict(type='BN', requires_grad=True), 9 | norm_eval=True, 10 | style='pytorch', 11 | init_cfg=dict( 12 | type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')), 13 | neck=dict( 14 | type='FPN', 15 | in_channels=[64, 128, 288, 672], 16 | out_channels=256, 17 | num_outs=5)) 18 | -------------------------------------------------------------------------------- /configs/regnet/faster-rcnn_regnetx-1.6GF_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='RegNet', 5 | arch='regnetx_1.6gf', 6 | out_indices=(0, 1, 2, 3), 7 | frozen_stages=1, 8 | norm_cfg=dict(type='BN', requires_grad=True), 9 | norm_eval=True, 10 | style='pytorch', 11 | init_cfg=dict( 12 | type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')), 13 | neck=dict( 14 | type='FPN', 15 | in_channels=[72, 168, 408, 912], 16 | out_channels=256, 17 | num_outs=5)) 18 | -------------------------------------------------------------------------------- /configs/regnet/faster-rcnn_regnetx-3.2GF_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_regnetx-3.2GF_fpn_1x_coco.py' 2 | 3 | # learning policy 4 | max_epochs = 24 5 | train_cfg = dict(max_epochs=max_epochs) 6 | param_scheduler = [ 7 | dict( 8 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 9 | dict( 10 | type='MultiStepLR', 11 | begin=0, 12 | end=max_epochs, 13 | by_epoch=True, 14 | milestones=[16, 22], 15 | gamma=0.1) 16 | ] 17 | -------------------------------------------------------------------------------- /configs/regnet/faster-rcnn_regnetx-400MF_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='RegNet', 5 | arch='regnetx_400mf', 6 | out_indices=(0, 1, 2, 3), 7 | frozen_stages=1, 8 | norm_cfg=dict(type='BN', requires_grad=True), 9 | norm_eval=True, 10 | style='pytorch', 11 | init_cfg=dict( 12 | type='Pretrained', checkpoint='open-mmlab://regnetx_400mf')), 13 | neck=dict( 14 | type='FPN', 15 | in_channels=[32, 64, 160, 384], 16 | out_channels=256, 17 | num_outs=5)) 18 | -------------------------------------------------------------------------------- /configs/regnet/faster-rcnn_regnetx-4GF_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='RegNet', 5 | arch='regnetx_4.0gf', 6 | out_indices=(0, 1, 2, 3), 7 | frozen_stages=1, 8 | norm_cfg=dict(type='BN', requires_grad=True), 9 | norm_eval=True, 10 | style='pytorch', 11 | init_cfg=dict( 12 | type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')), 13 | neck=dict( 14 | type='FPN', 15 | in_channels=[80, 240, 560, 1360], 16 | out_channels=256, 17 | num_outs=5)) 18 | -------------------------------------------------------------------------------- /configs/regnet/faster-rcnn_regnetx-800MF_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='RegNet', 5 | arch='regnetx_800mf', 6 | out_indices=(0, 1, 2, 3), 7 | frozen_stages=1, 8 | norm_cfg=dict(type='BN', requires_grad=True), 9 | norm_eval=True, 10 | style='pytorch', 11 | init_cfg=dict( 12 | type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')), 13 | neck=dict( 14 | type='FPN', 15 | in_channels=[64, 128, 288, 672], 16 | out_channels=256, 17 | num_outs=5)) 18 | -------------------------------------------------------------------------------- /configs/regnet/mask-rcnn_regnetx-12GF_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='RegNet', 5 | arch='regnetx_12gf', 6 | out_indices=(0, 1, 2, 3), 7 | frozen_stages=1, 8 | norm_cfg=dict(type='BN', requires_grad=True), 9 | norm_eval=True, 10 | style='pytorch', 11 | init_cfg=dict( 12 | type='Pretrained', checkpoint='open-mmlab://regnetx_12gf')), 13 | neck=dict( 14 | type='FPN', 15 | in_channels=[224, 448, 896, 2240], 16 | out_channels=256, 17 | num_outs=5)) 18 | -------------------------------------------------------------------------------- /configs/regnet/mask-rcnn_regnetx-3.2GF-mdconv-c3-c5_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True), 6 | init_cfg=dict( 7 | type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf'))) 8 | -------------------------------------------------------------------------------- /configs/regnet/mask-rcnn_regnetx-4GF_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='RegNet', 5 | arch='regnetx_4.0gf', 6 | out_indices=(0, 1, 2, 3), 7 | frozen_stages=1, 8 | norm_cfg=dict(type='BN', requires_grad=True), 9 | norm_eval=True, 10 | style='pytorch', 11 | init_cfg=dict( 12 | type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')), 13 | neck=dict( 14 | type='FPN', 15 | in_channels=[80, 240, 560, 1360], 16 | out_channels=256, 17 | num_outs=5)) 18 | -------------------------------------------------------------------------------- /configs/regnet/mask-rcnn_regnetx-6.4GF_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='RegNet', 5 | arch='regnetx_6.4gf', 6 | out_indices=(0, 1, 2, 3), 7 | frozen_stages=1, 8 | norm_cfg=dict(type='BN', requires_grad=True), 9 | norm_eval=True, 10 | style='pytorch', 11 | init_cfg=dict( 12 | type='Pretrained', checkpoint='open-mmlab://regnetx_6.4gf')), 13 | neck=dict( 14 | type='FPN', 15 | in_channels=[168, 392, 784, 1624], 16 | out_channels=256, 17 | num_outs=5)) 18 | -------------------------------------------------------------------------------- /configs/regnet/mask-rcnn_regnetx-8GF_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='RegNet', 5 | arch='regnetx_8.0gf', 6 | out_indices=(0, 1, 2, 3), 7 | frozen_stages=1, 8 | norm_cfg=dict(type='BN', requires_grad=True), 9 | norm_eval=True, 10 | style='pytorch', 11 | init_cfg=dict( 12 | type='Pretrained', checkpoint='open-mmlab://regnetx_8.0gf')), 13 | neck=dict( 14 | type='FPN', 15 | in_channels=[80, 240, 720, 1920], 16 | out_channels=256, 17 | num_outs=5)) 18 | -------------------------------------------------------------------------------- /configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='RegNet', 5 | arch='regnetx_1.6gf', 6 | out_indices=(0, 1, 2, 3), 7 | frozen_stages=1, 8 | norm_cfg=dict(type='BN', requires_grad=True), 9 | norm_eval=True, 10 | style='pytorch', 11 | init_cfg=dict( 12 | type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')), 13 | neck=dict( 14 | type='FPN', 15 | in_channels=[72, 168, 408, 912], 16 | out_channels=256, 17 | num_outs=5)) 18 | -------------------------------------------------------------------------------- /configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='RegNet', 5 | arch='regnetx_800mf', 6 | out_indices=(0, 1, 2, 3), 7 | frozen_stages=1, 8 | norm_cfg=dict(type='BN', requires_grad=True), 9 | norm_eval=True, 10 | style='pytorch', 11 | init_cfg=dict( 12 | type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')), 13 | neck=dict( 14 | type='FPN', 15 | in_channels=[64, 128, 288, 672], 16 | out_channels=256, 17 | num_outs=5)) 18 | -------------------------------------------------------------------------------- /configs/reppoints/reppoints-bbox_r50-center_fpn-gn_head-gn-grid_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_1x_coco.py' 2 | model = dict(bbox_head=dict(transform_method='minmax', use_grid_points=True)) 3 | -------------------------------------------------------------------------------- /configs/reppoints/reppoints-bbox_r50_fpn-gn_head-gn-grid_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_1x_coco.py' 2 | model = dict( 3 | bbox_head=dict(transform_method='minmax', use_grid_points=True), 4 | # training and testing settings 5 | train_cfg=dict( 6 | init=dict( 7 | assigner=dict( 8 | _delete_=True, 9 | type='MaxIoUAssigner', 10 | pos_iou_thr=0.5, 11 | neg_iou_thr=0.4, 12 | min_pos_iou=0, 13 | ignore_iof_thr=-1)))) 14 | -------------------------------------------------------------------------------- /configs/reppoints/reppoints-minmax_r50_fpn-gn_head-gn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_1x_coco.py' 2 | model = dict(bbox_head=dict(transform_method='minmax')) 3 | -------------------------------------------------------------------------------- /configs/reppoints/reppoints-moment_r101-dconv-c3-c5_fpn-gn_head-gn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 6 | stage_with_dcn=(False, True, True, True), 7 | init_cfg=dict(type='Pretrained', 8 | checkpoint='torchvision://resnet101'))) 9 | -------------------------------------------------------------------------------- /configs/reppoints/reppoints-moment_r101_fpn-gn_head-gn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/reppoints/reppoints-moment_r50_fpn-gn_head-gn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints-moment_r50_fpn_1x_coco.py' 2 | norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) 3 | model = dict(neck=dict(norm_cfg=norm_cfg), bbox_head=dict(norm_cfg=norm_cfg)) 4 | -------------------------------------------------------------------------------- /configs/reppoints/reppoints-moment_r50_fpn-gn_head-gn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_1x_coco.py' 2 | 3 | max_epochs = 24 4 | 5 | train_cfg = dict( 6 | type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1) 7 | param_scheduler = [ 8 | dict( 9 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 10 | dict( 11 | type='MultiStepLR', 12 | begin=0, 13 | end=max_epochs, 14 | by_epoch=True, 15 | milestones=[16, 22], 16 | gamma=0.1) 17 | ] 18 | -------------------------------------------------------------------------------- /configs/reppoints/reppoints-moment_x101-dconv-c3-c5_fpn-gn_head-gn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), 14 | stage_with_dcn=(False, True, True, True), 15 | init_cfg=dict( 16 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 17 | -------------------------------------------------------------------------------- /configs/reppoints/reppoints-partial-minmax_r50_fpn-gn_head-gn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './reppoints-moment_r50_fpn-gn_head-gn_1x_coco.py' 2 | model = dict(bbox_head=dict(transform_method='partial_minmax')) 3 | -------------------------------------------------------------------------------- /configs/reppoints/reppoints.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ikomia-hub/train_mmlab_detection/63795200e4c00fc95fd1cfc3acd4a58baa1c3140/configs/reppoints/reppoints.png -------------------------------------------------------------------------------- /configs/res2net/cascade-mask-rcnn_res2net-101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade-mask-rcnn_r50_fpn_20e_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='Res2Net', 5 | depth=101, 6 | scales=4, 7 | base_width=26, 8 | init_cfg=dict( 9 | type='Pretrained', 10 | checkpoint='open-mmlab://res2net101_v1d_26w_4s'))) 11 | -------------------------------------------------------------------------------- /configs/res2net/cascade-rcnn_res2net-101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../cascade_rcnn/cascade-rcnn_r50_fpn_20e_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='Res2Net', 5 | depth=101, 6 | scales=4, 7 | base_width=26, 8 | init_cfg=dict( 9 | type='Pretrained', 10 | checkpoint='open-mmlab://res2net101_v1d_26w_4s'))) 11 | -------------------------------------------------------------------------------- /configs/res2net/faster-rcnn_res2net-101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='Res2Net', 5 | depth=101, 6 | scales=4, 7 | base_width=26, 8 | init_cfg=dict( 9 | type='Pretrained', 10 | checkpoint='open-mmlab://res2net101_v1d_26w_4s'))) 11 | -------------------------------------------------------------------------------- /configs/res2net/htc_res2net-101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../htc/htc_r50_fpn_20e_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='Res2Net', 5 | depth=101, 6 | scales=4, 7 | base_width=26, 8 | init_cfg=dict( 9 | type='Pretrained', 10 | checkpoint='open-mmlab://res2net101_v1d_26w_4s'))) 11 | -------------------------------------------------------------------------------- /configs/res2net/mask-rcnn_res2net-101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='Res2Net', 5 | depth=101, 6 | scales=4, 7 | base_width=26, 8 | init_cfg=dict( 9 | type='Pretrained', 10 | checkpoint='open-mmlab://res2net101_v1d_26w_4s'))) 11 | -------------------------------------------------------------------------------- /configs/resnest/cascade-mask-rcnn_s101_fpn_syncbn-backbone+head_ms-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | stem_channels=128, 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='open-mmlab://resnest101'))) 8 | -------------------------------------------------------------------------------- /configs/resnest/cascade-rcnn_s101_fpn_syncbn-backbone+head_ms-range-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | stem_channels=128, 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='open-mmlab://resnest101'))) 8 | -------------------------------------------------------------------------------- /configs/resnest/faster-rcnn_s101_fpn_syncbn-backbone+head_ms-range-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './faster-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | stem_channels=128, 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='open-mmlab://resnest101'))) 8 | -------------------------------------------------------------------------------- /configs/resnest/mask-rcnn_s101_fpn_syncbn-backbone+head_ms-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | stem_channels=128, 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='open-mmlab://resnest101'))) 8 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_r101-caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50-caffe_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 8 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_r101-caffe_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50-caffe_fpn_ms-3x_coco.py' 2 | # learning policy 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict( 7 | type='Pretrained', 8 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 9 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_r101_fpn_8xb8-amp-lsj-200e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_8xb8-amp-lsj-200e_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_r101_fpn_ms-640-800-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = ['../_base_/models/retinanet_r50_fpn.py', '../common/ms_3x_coco.py'] 2 | # optimizer 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | optim_wrapper = dict( 9 | optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)) 10 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_r18_fpn_8xb8-amp-lsj-200e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_8xb8-amp-lsj-200e_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=18, 6 | init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')), 7 | neck=dict(in_channels=[64, 128, 256, 512])) 8 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_r50-caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_1x_coco.py' 2 | model = dict( 3 | data_preprocessor=dict( 4 | type='DetDataPreprocessor', 5 | # use caffe img_norm 6 | mean=[103.530, 116.280, 123.675], 7 | std=[1.0, 1.0, 1.0], 8 | bgr_to_rgb=False, 9 | pad_size_divisor=32), 10 | backbone=dict( 11 | norm_cfg=dict(requires_grad=False), 12 | norm_eval=True, 13 | style='caffe', 14 | init_cfg=dict( 15 | type='Pretrained', 16 | checkpoint='open-mmlab://detectron2/resnet50_caffe'))) 17 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_r50-caffe_fpn_ms-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50-caffe_fpn_1x_coco.py' 2 | 3 | train_pipeline = [ 4 | dict(type='LoadImageFromFile'), 5 | dict(type='LoadAnnotations', with_bbox=True), 6 | dict( 7 | type='RandomChoiceResize', 8 | scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768), 9 | (1333, 800)], 10 | keep_ratio=True), 11 | dict(type='RandomFlip', prob=0.5), 12 | dict(type='PackDetInputs') 13 | ] 14 | 15 | train_dataloader = dict(dataset=dict(pipeline=train_pipeline)) 16 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_r50-caffe_fpn_ms-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50-caffe_fpn_ms-1x_coco.py' 2 | # training schedule for 2x 3 | train_cfg = dict(max_epochs=24) 4 | 5 | # learning rate policy 6 | param_scheduler = [ 7 | dict( 8 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 9 | dict( 10 | type='MultiStepLR', 11 | begin=0, 12 | end=24, 13 | by_epoch=True, 14 | milestones=[16, 22], 15 | gamma=0.1) 16 | ] 17 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_r50-caffe_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50-caffe_fpn_ms-1x_coco.py' 2 | 3 | # training schedule for 2x 4 | train_cfg = dict(max_epochs=36) 5 | 6 | # learning rate policy 7 | param_scheduler = [ 8 | dict( 9 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 10 | dict( 11 | type='MultiStepLR', 12 | begin=0, 13 | end=36, 14 | by_epoch=True, 15 | milestones=[28, 34], 16 | gamma=0.1) 17 | ] 18 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_r50_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/retinanet_r50_fpn.py', 3 | '../_base_/datasets/coco_detection.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py', 5 | './retinanet_tta.py' 6 | ] 7 | 8 | # optimizer 9 | optim_wrapper = dict( 10 | optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)) 11 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_r50_fpn_amp-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_1x_coco.py' 2 | 3 | # MMEngine support the following two ways, users can choose 4 | # according to convenience 5 | # optim_wrapper = dict(type='AmpOptimWrapper') 6 | _base_.optim_wrapper.type = 'AmpOptimWrapper' 7 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_r50_fpn_ms-640-800-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = ['../_base_/models/retinanet_r50_fpn.py', '../common/ms_3x_coco.py'] 2 | # optimizer 3 | optim_wrapper = dict( 4 | optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)) 5 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_x101-32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_x101-32x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_x101-64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_x101-64x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './retinanet_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/retinanet/retinanet_x101-64x4d_fpn_ms-640-800-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = ['../_base_/models/retinanet_r50_fpn.py', '../common/ms_3x_coco.py'] 2 | # optimizer 3 | model = dict( 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | init_cfg=dict( 10 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 11 | optim_wrapper = dict(optimizer=dict(type='SGD', lr=0.01)) 12 | -------------------------------------------------------------------------------- /configs/rpn/rpn_r101-caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50-caffe_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict( 6 | type='Pretrained', 7 | checkpoint='open-mmlab://detectron2/resnet101_caffe'))) 8 | -------------------------------------------------------------------------------- /configs/rpn/rpn_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/rpn/rpn_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/rpn/rpn_r50-caffe-c4_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/rpn_r50-caffe-c4.py', 3 | '../_base_/datasets/coco_detection.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | 7 | val_evaluator = dict(metric='proposal_fast') 8 | test_evaluator = val_evaluator 9 | -------------------------------------------------------------------------------- /configs/rpn/rpn_r50-caffe_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_1x_coco.py' 2 | # use caffe img_norm 3 | model = dict( 4 | data_preprocessor=dict( 5 | type='DetDataPreprocessor', 6 | mean=[103.530, 116.280, 123.675], 7 | std=[1.0, 1.0, 1.0], 8 | bgr_to_rgb=False, 9 | pad_size_divisor=32), 10 | backbone=dict( 11 | norm_cfg=dict(requires_grad=False), 12 | norm_eval=True, 13 | style='caffe', 14 | init_cfg=dict( 15 | type='Pretrained', 16 | checkpoint='open-mmlab://detectron2/resnet50_caffe'))) 17 | -------------------------------------------------------------------------------- /configs/rpn/rpn_r50_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_1x_coco.py' 2 | 3 | # learning policy 4 | max_epochs = 24 5 | train_cfg = dict( 6 | type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1) 7 | param_scheduler = [ 8 | dict( 9 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 10 | dict( 11 | type='MultiStepLR', 12 | begin=0, 13 | end=max_epochs, 14 | by_epoch=True, 15 | milestones=[16, 22], 16 | gamma=0.1) 17 | ] 18 | -------------------------------------------------------------------------------- /configs/rpn/rpn_x101-32x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/rpn/rpn_x101-32x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 15 | -------------------------------------------------------------------------------- /configs/rpn/rpn_x101-64x4d_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/rpn/rpn_x101-64x4d_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rpn_r50_fpn_2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | style='pytorch', 13 | init_cfg=dict( 14 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 15 | -------------------------------------------------------------------------------- /configs/rtmdet/classification/cspnext-l_8xb256-rsb-a1-600e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = './cspnext-s_8xb256-rsb-a1-600e_in1k.py' 2 | 3 | model = dict( 4 | backbone=dict(deepen_factor=1, widen_factor=1), 5 | head=dict(in_channels=1024)) 6 | -------------------------------------------------------------------------------- /configs/rtmdet/classification/cspnext-m_8xb256-rsb-a1-600e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = './cspnext-s_8xb256-rsb-a1-600e_in1k.py' 2 | 3 | model = dict( 4 | backbone=dict(deepen_factor=0.67, widen_factor=0.75), 5 | head=dict(in_channels=768)) 6 | -------------------------------------------------------------------------------- /configs/rtmdet/classification/cspnext-tiny_8xb256-rsb-a1-600e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = './cspnext-s_8xb256-rsb-a1-600e_in1k.py' 2 | 3 | model = dict( 4 | backbone=dict(deepen_factor=0.167, widen_factor=0.375), 5 | head=dict(in_channels=384)) 6 | -------------------------------------------------------------------------------- /configs/rtmdet/classification/cspnext-x_8xb256-rsb-a1-600e_in1k.py: -------------------------------------------------------------------------------- 1 | _base_ = './cspnext-s_8xb256-rsb-a1-600e_in1k.py' 2 | 3 | model = dict( 4 | backbone=dict(deepen_factor=1.33, widen_factor=1.25), 5 | head=dict(in_channels=1280)) 6 | -------------------------------------------------------------------------------- /configs/rtmdet/rtmdet-ins_m_8xb32-300e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rtmdet-ins_l_8xb32-300e_coco.py' 2 | 3 | model = dict( 4 | backbone=dict(deepen_factor=0.67, widen_factor=0.75), 5 | neck=dict(in_channels=[192, 384, 768], out_channels=192, num_csp_blocks=2), 6 | bbox_head=dict(in_channels=192, feat_channels=192)) 7 | -------------------------------------------------------------------------------- /configs/rtmdet/rtmdet_m_8xb32-300e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rtmdet_l_8xb32-300e_coco.py' 2 | 3 | model = dict( 4 | backbone=dict(deepen_factor=0.67, widen_factor=0.75), 5 | neck=dict(in_channels=[192, 384, 768], out_channels=192, num_csp_blocks=2), 6 | bbox_head=dict(in_channels=192, feat_channels=192)) 7 | -------------------------------------------------------------------------------- /configs/rtmdet/rtmdet_x_8xb32-300e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './rtmdet_l_8xb32-300e_coco.py' 2 | 3 | model = dict( 4 | backbone=dict(deepen_factor=1.33, widen_factor=1.25), 5 | neck=dict( 6 | in_channels=[320, 640, 1280], out_channels=320, num_csp_blocks=4), 7 | bbox_head=dict(in_channels=320, feat_channels=320)) 8 | -------------------------------------------------------------------------------- /configs/scnet/scnet_r101_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './scnet_r50_fpn_20e_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/scnet/scnet_r50_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './scnet_r50_fpn_1x_coco.py' 2 | # learning policy 3 | max_epochs = 20 4 | param_scheduler = [ 5 | dict( 6 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 7 | dict( 8 | type='MultiStepLR', 9 | begin=0, 10 | end=max_epochs, 11 | by_epoch=True, 12 | milestones=[16, 19], 13 | gamma=0.1) 14 | ] 15 | train_cfg = dict(max_epochs=max_epochs) 16 | -------------------------------------------------------------------------------- /configs/scnet/scnet_x101-64x4d_fpn_20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './scnet_r50_fpn_20e_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | norm_eval=True, 13 | style='pytorch', 14 | init_cfg=dict( 15 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 16 | -------------------------------------------------------------------------------- /configs/scnet/scnet_x101-64x4d_fpn_8xb1-20e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './scnet_x101-64x4d_fpn_20e_coco.py' 2 | train_dataloader = dict(batch_size=1, num_workers=1) 3 | 4 | optim_wrapper = dict(optimizer=dict(lr=0.01)) 5 | # NOTE: `auto_scale_lr` is for automatically scaling LR, 6 | # USER SHOULD NOT CHANGE ITS VALUES. 7 | # base_batch_size = (8 GPUs) x (1 samples per GPU) 8 | auto_scale_lr = dict(base_batch_size=8) 9 | -------------------------------------------------------------------------------- /configs/seesaw_loss/cascade-mask-rcnn_r101_fpn_seesaw-loss-normed-mask_random-ms-2x_lvis-v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-mask-rcnn_r101_fpn_seesaw-loss_random-ms-2x_lvis-v1.py' # noqa: E501 2 | model = dict( 3 | roi_head=dict( 4 | mask_head=dict( 5 | predictor_cfg=dict(type='NormedConv2d', tempearture=20)))) 6 | -------------------------------------------------------------------------------- /configs/seesaw_loss/cascade-mask-rcnn_r101_fpn_seesaw-loss-normed-mask_sample1e-3-ms-2x_lvis-v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './cascade-mask-rcnn_r101_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py' # noqa: E501 2 | model = dict( 3 | roi_head=dict( 4 | mask_head=dict( 5 | predictor_cfg=dict(type='NormedConv2d', tempearture=20)))) 6 | -------------------------------------------------------------------------------- /configs/seesaw_loss/mask-rcnn_r101_fpn_seesaw-loss-normed-mask_random-ms-2x_lvis-v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_seesaw-loss-normed-mask_random-ms-2x_lvis-v1.py' # noqa: E501 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/seesaw_loss/mask-rcnn_r101_fpn_seesaw-loss-normed-mask_sample1e-3-ms-2x_lvis-v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_seesaw-loss-normed-mask_sample1e-3-ms-2x_lvis-v1.py' # noqa: E501 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/seesaw_loss/mask-rcnn_r101_fpn_seesaw-loss_random-ms-2x_lvis-v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_seesaw-loss_random-ms-2x_lvis-v1.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/seesaw_loss/mask-rcnn_r101_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/seesaw_loss/mask-rcnn_r50_fpn_seesaw-loss-normed-mask_random-ms-2x_lvis-v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_seesaw-loss_random-ms-2x_lvis-v1.py' 2 | model = dict( 3 | roi_head=dict( 4 | mask_head=dict( 5 | predictor_cfg=dict(type='NormedConv2d', tempearture=20)))) 6 | -------------------------------------------------------------------------------- /configs/seesaw_loss/mask-rcnn_r50_fpn_seesaw-loss-normed-mask_sample1e-3-ms-2x_lvis-v1.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_r50_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py' 2 | model = dict( 3 | roi_head=dict( 4 | mask_head=dict( 5 | predictor_cfg=dict(type='NormedConv2d', tempearture=20)))) 6 | -------------------------------------------------------------------------------- /configs/selfsup_pretrain/mask-rcnn_r50-mocov2-pre_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/mask-rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_instance.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | 7 | model = dict( 8 | backbone=dict( 9 | frozen_stages=0, 10 | norm_cfg=dict(type='SyncBN', requires_grad=True), 11 | norm_eval=False, 12 | init_cfg=dict( 13 | type='Pretrained', checkpoint='./mocov2_r50_800ep_pretrain.pth'))) 14 | -------------------------------------------------------------------------------- /configs/selfsup_pretrain/mask-rcnn_r50-swav-pre_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/mask-rcnn_r50_fpn.py', 3 | '../_base_/datasets/coco_instance.py', 4 | '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' 5 | ] 6 | 7 | model = dict( 8 | backbone=dict( 9 | frozen_stages=0, 10 | norm_cfg=dict(type='SyncBN', requires_grad=True), 11 | norm_eval=False, 12 | init_cfg=dict( 13 | type='Pretrained', checkpoint='./swav_800ep_pretrain.pth.tar'))) 14 | -------------------------------------------------------------------------------- /configs/simple_copy_paste/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-90k_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-270k_coco.py' # noqa 2 | 3 | # training schedule for 90k 4 | max_iters = 90000 5 | 6 | # learning rate policy 7 | # lr steps at [0.9, 0.95, 0.975] of the maximum iterations 8 | param_scheduler = [ 9 | dict( 10 | type='LinearLR', start_factor=0.067, by_epoch=False, begin=0, end=500), 11 | dict( 12 | type='MultiStepLR', 13 | begin=0, 14 | end=90000, 15 | by_epoch=False, 16 | milestones=[81000, 85500, 87750], 17 | gamma=0.1) 18 | ] 19 | -------------------------------------------------------------------------------- /configs/simple_copy_paste/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-scp-90k_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-scp-270k_coco.py' # noqa 2 | 3 | # training schedule for 90k 4 | max_iters = 90000 5 | 6 | # learning rate policy 7 | # lr steps at [0.9, 0.95, 0.975] of the maximum iterations 8 | param_scheduler = [ 9 | dict( 10 | type='LinearLR', start_factor=0.067, by_epoch=False, begin=0, end=500), 11 | dict( 12 | type='MultiStepLR', 13 | begin=0, 14 | end=90000, 15 | by_epoch=False, 16 | milestones=[81000, 85500, 87750], 17 | gamma=0.1) 18 | ] 19 | -------------------------------------------------------------------------------- /configs/soft_teacher/metafile.yml: -------------------------------------------------------------------------------- 1 | Collections: 2 | - Name: SoftTeacher 3 | Metadata: 4 | Training Data: COCO 5 | Training Techniques: 6 | - SGD with Momentum 7 | - Weight Decay 8 | Training Resources: 8x V100 GPUs 9 | Architecture: 10 | - FPN 11 | - ResNet 12 | Paper: 13 | URL: https://arxiv.org/abs/2106.09018 14 | Title: "End-to-End Semi-Supervised Object Detection with Soft Teacher" 15 | README: configs/soft_teacher/README.md 16 | Code: 17 | URL: https://github.com/open-mmlab/mmdetection/blob/v3.0.0rc1/mmdet/models/detectors/soft_teacher.py#L20 18 | Version: v3.0.0rc1 19 | -------------------------------------------------------------------------------- /configs/soft_teacher/soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.01-coco.py: -------------------------------------------------------------------------------- 1 | _base_ = ['soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.1-coco.py'] 2 | 3 | # 1% coco train2017 is set as labeled dataset 4 | labeled_dataset = _base_.labeled_dataset 5 | unlabeled_dataset = _base_.unlabeled_dataset 6 | labeled_dataset.ann_file = 'semi_anns/instances_train2017.1@1.json' 7 | unlabeled_dataset.ann_file = 'semi_anns/instances_train2017.1@1-unlabeled.json' 8 | train_dataloader = dict( 9 | dataset=dict(datasets=[labeled_dataset, unlabeled_dataset])) 10 | -------------------------------------------------------------------------------- /configs/soft_teacher/soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.02-coco.py: -------------------------------------------------------------------------------- 1 | _base_ = ['soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.1-coco.py'] 2 | 3 | # 2% coco train2017 is set as labeled dataset 4 | labeled_dataset = _base_.labeled_dataset 5 | unlabeled_dataset = _base_.unlabeled_dataset 6 | labeled_dataset.ann_file = 'semi_anns/instances_train2017.1@2.json' 7 | unlabeled_dataset.ann_file = 'semi_anns/instances_train2017.1@2-unlabeled.json' 8 | train_dataloader = dict( 9 | dataset=dict(datasets=[labeled_dataset, unlabeled_dataset])) 10 | -------------------------------------------------------------------------------- /configs/soft_teacher/soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.05-coco.py: -------------------------------------------------------------------------------- 1 | _base_ = ['soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.1-coco.py'] 2 | 3 | # 5% coco train2017 is set as labeled dataset 4 | labeled_dataset = _base_.labeled_dataset 5 | unlabeled_dataset = _base_.unlabeled_dataset 6 | labeled_dataset.ann_file = 'semi_anns/instances_train2017.1@5.json' 7 | unlabeled_dataset.ann_file = 'semi_anns/instances_train2017.1@5-unlabeled.json' 8 | train_dataloader = dict( 9 | dataset=dict(datasets=[labeled_dataset, unlabeled_dataset])) 10 | -------------------------------------------------------------------------------- /configs/solo/solo_r101_fpn_8xb8-lsj-200e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './solo_r50_fpn_8xb8-lsj-200e_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/solo/solo_r18_fpn_8xb8-lsj-200e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './solo_r50_fpn_8xb8-lsj-200e_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=18, 6 | init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')), 7 | neck=dict(in_channels=[64, 128, 256, 512])) 8 | -------------------------------------------------------------------------------- /configs/solov2/solov2-light_r18_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './solov2-light_r50_fpn_ms-3x_coco.py' 2 | 3 | # model settings 4 | model = dict( 5 | backbone=dict( 6 | depth=18, init_cfg=dict(checkpoint='torchvision://resnet18')), 7 | neck=dict(in_channels=[64, 128, 256, 512])) 8 | -------------------------------------------------------------------------------- /configs/solov2/solov2-light_r34_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './solov2-light_r50_fpn_ms-3x_coco.py' 2 | 3 | # model settings 4 | model = dict( 5 | backbone=dict( 6 | depth=34, init_cfg=dict(checkpoint='torchvision://resnet34')), 7 | neck=dict(in_channels=[64, 128, 256, 512])) 8 | -------------------------------------------------------------------------------- /configs/solov2/solov2-light_r50-dcn_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './solov2-light_r50_fpn_ms-3x_coco.py' 2 | 3 | # model settings 4 | model = dict( 5 | backbone=dict( 6 | dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False), 7 | stage_with_dcn=(False, True, True, True)), 8 | mask_head=dict( 9 | feat_channels=256, 10 | stacked_convs=3, 11 | scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)), 12 | mask_feature_head=dict(out_channels=128), 13 | dcn_cfg=dict(type='DCNv2'), 14 | dcn_apply_to_all_conv=False)) # light solov2 head 15 | -------------------------------------------------------------------------------- /configs/solov2/solov2_r101-dcn_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './solov2_r50_fpn_ms-3x_coco.py' 2 | 3 | # model settings 4 | model = dict( 5 | backbone=dict( 6 | depth=101, 7 | init_cfg=dict(checkpoint='torchvision://resnet101'), 8 | dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False), 9 | stage_with_dcn=(False, True, True, True)), 10 | mask_head=dict( 11 | mask_feature_head=dict(conv_cfg=dict(type='DCNv2')), 12 | dcn_cfg=dict(type='DCNv2'), 13 | dcn_apply_to_all_conv=True)) 14 | -------------------------------------------------------------------------------- /configs/solov2/solov2_r101_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './solov2_r50_fpn_ms-3x_coco.py' 2 | 3 | # model settings 4 | model = dict( 5 | backbone=dict( 6 | depth=101, init_cfg=dict(checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/solov2/solov2_x101-dcn_fpn_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './solov2_r50_fpn_ms-3x_coco.py' 2 | 3 | # model settings 4 | model = dict( 5 | backbone=dict( 6 | type='ResNeXt', 7 | depth=101, 8 | groups=64, 9 | base_width=4, 10 | dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False), 11 | stage_with_dcn=(False, True, True, True), 12 | init_cfg=dict( 13 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')), 14 | mask_head=dict( 15 | mask_feature_head=dict(conv_cfg=dict(type='DCNv2')), 16 | dcn_cfg=dict(type='DCNv2'), 17 | dcn_apply_to_all_conv=True)) 18 | -------------------------------------------------------------------------------- /configs/sparse_rcnn/sparse-rcnn_r101_fpn_300-proposals_crop-ms-480-800-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './sparse-rcnn_r50_fpn_300-proposals_crop-ms-480-800-3x_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/sparse_rcnn/sparse-rcnn_r101_fpn_ms-480-800-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './sparse-rcnn_r50_fpn_ms-480-800-3x_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/strong_baselines/mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_amp-lsj-100e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'mask-rcnn_r50-caffe_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py' # noqa 2 | 3 | # Enable automatic-mixed-precision training with AmpOptimWrapper. 4 | optim_wrapper = dict(type='AmpOptimWrapper') 5 | -------------------------------------------------------------------------------- /configs/strong_baselines/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_amp-lsj-100e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py' 2 | 3 | # Enable automatic-mixed-precision training with AmpOptimWrapper. 4 | optim_wrapper = dict(type='AmpOptimWrapper') 5 | -------------------------------------------------------------------------------- /configs/strong_baselines/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-50e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py' 2 | 3 | # Use RepeatDataset to speed up training 4 | # change repeat time from 4 (for 100 epochs) to 2 (for 50 epochs) 5 | train_dataloader = dict(dataset=dict(times=2)) 6 | -------------------------------------------------------------------------------- /configs/swin/mask-rcnn_swin-s-p4-w7_fpn_amp-ms-crop-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_swin-t-p4-w7_fpn_amp-ms-crop-3x_coco.py' 2 | pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth' # noqa 3 | model = dict( 4 | backbone=dict( 5 | depths=[2, 2, 18, 2], 6 | init_cfg=dict(type='Pretrained', checkpoint=pretrained))) 7 | -------------------------------------------------------------------------------- /configs/swin/mask-rcnn_swin-t-p4-w7_fpn_amp-ms-crop-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './mask-rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py' 2 | # Enable automatic-mixed-precision training with AmpOptimWrapper. 3 | optim_wrapper = dict(type='AmpOptimWrapper') 4 | -------------------------------------------------------------------------------- /configs/tood/tood_r101-dconv-c3-c5_fpn_ms-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './tood_r101_fpn_ms-2x_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False), 6 | stage_with_dcn=(False, True, True, True)), 7 | bbox_head=dict(num_dcn=2)) 8 | -------------------------------------------------------------------------------- /configs/tood/tood_r101_fpn_ms-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './tood_r50_fpn_ms-2x_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/tood/tood_r50_fpn_anchor-based_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './tood_r50_fpn_1x_coco.py' 2 | model = dict(bbox_head=dict(anchor_type='anchor_based')) 3 | -------------------------------------------------------------------------------- /configs/tood/tood_x101-64x4d-dconv-c4-c5_fpn_ms-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './tood_x101-64x4d_fpn_ms-2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, False, True, True), 6 | ), 7 | bbox_head=dict(num_dcn=2)) 8 | -------------------------------------------------------------------------------- /configs/tood/tood_x101-64x4d_fpn_ms-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './tood_r50_fpn_ms-2x_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | type='ResNeXt', 6 | depth=101, 7 | groups=64, 8 | base_width=4, 9 | num_stages=4, 10 | out_indices=(0, 1, 2, 3), 11 | frozen_stages=1, 12 | norm_cfg=dict(type='BN', requires_grad=True), 13 | norm_eval=True, 14 | style='pytorch', 15 | init_cfg=dict( 16 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 17 | -------------------------------------------------------------------------------- /configs/tridentnet/tridentnet_r50-caffe_ms-1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'tridentnet_r50-caffe_1x_coco.py' 2 | 3 | train_pipeline = [ 4 | dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}), 5 | dict(type='LoadAnnotations', with_bbox=True), 6 | dict( 7 | type='RandomChoiceResize', 8 | scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), 9 | (1333, 768), (1333, 800)], 10 | keep_ratio=True), 11 | dict(type='RandomFlip', prob=0.5), 12 | dict(type='PackDetInputs') 13 | ] 14 | 15 | train_dataloader = dict(dataset=dict(pipeline=train_pipeline)) 16 | -------------------------------------------------------------------------------- /configs/tridentnet/tridentnet_r50-caffe_ms-3x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = 'tridentnet_r50-caffe_ms-1x_coco.py' 2 | 3 | # learning rate 4 | max_epochs = 36 5 | train_cfg = dict( 6 | type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1) 7 | 8 | param_scheduler = [ 9 | dict( 10 | type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), 11 | dict( 12 | type='MultiStepLR', 13 | begin=0, 14 | end=max_epochs, 15 | by_epoch=True, 16 | milestones=[28, 34], 17 | gamma=0.1) 18 | ] 19 | -------------------------------------------------------------------------------- /configs/vfnet/vfnet_r101-mdconv-c3-c5_fpn_ms-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50-mdconv-c3-c5_fpn_ms-2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNet', 5 | depth=101, 6 | num_stages=4, 7 | out_indices=(0, 1, 2, 3), 8 | frozen_stages=1, 9 | norm_cfg=dict(type='BN', requires_grad=True), 10 | norm_eval=True, 11 | style='pytorch', 12 | dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), 13 | stage_with_dcn=(False, True, True, True), 14 | init_cfg=dict(type='Pretrained', 15 | checkpoint='torchvision://resnet101'))) 16 | -------------------------------------------------------------------------------- /configs/vfnet/vfnet_r101_fpn_1x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/vfnet/vfnet_r101_fpn_2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_1x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | # learning policy 8 | max_epochs = 24 9 | param_scheduler = [ 10 | dict(type='LinearLR', start_factor=0.1, by_epoch=False, begin=0, end=500), 11 | dict( 12 | type='MultiStepLR', 13 | begin=0, 14 | end=max_epochs, 15 | by_epoch=True, 16 | milestones=[16, 22], 17 | gamma=0.1) 18 | ] 19 | 20 | train_cfg = dict(max_epochs=max_epochs) 21 | -------------------------------------------------------------------------------- /configs/vfnet/vfnet_r101_fpn_ms-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_ms-2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | depth=101, 5 | init_cfg=dict(type='Pretrained', 6 | checkpoint='torchvision://resnet101'))) 7 | -------------------------------------------------------------------------------- /configs/vfnet/vfnet_r50-mdconv-c3-c5_fpn_ms-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_ms-2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), 5 | stage_with_dcn=(False, True, True, True)), 6 | bbox_head=dict(dcn_on_last_conv=True)) 7 | -------------------------------------------------------------------------------- /configs/vfnet/vfnet_res2net-101_fpn_ms-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_ms-2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='Res2Net', 5 | depth=101, 6 | scales=4, 7 | base_width=26, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | norm_eval=True, 13 | style='pytorch', 14 | init_cfg=dict( 15 | type='Pretrained', 16 | checkpoint='open-mmlab://res2net101_v1d_26w_4s'))) 17 | -------------------------------------------------------------------------------- /configs/vfnet/vfnet_x101-32x4d_fpn_ms-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_ms-2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=32, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | norm_eval=True, 13 | style='pytorch', 14 | init_cfg=dict( 15 | type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) 16 | -------------------------------------------------------------------------------- /configs/vfnet/vfnet_x101-64x4d_fpn_ms-2x_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './vfnet_r50_fpn_ms-2x_coco.py' 2 | model = dict( 3 | backbone=dict( 4 | type='ResNeXt', 5 | depth=101, 6 | groups=64, 7 | base_width=4, 8 | num_stages=4, 9 | out_indices=(0, 1, 2, 3), 10 | frozen_stages=1, 11 | norm_cfg=dict(type='BN', requires_grad=True), 12 | norm_eval=True, 13 | style='pytorch', 14 | init_cfg=dict( 15 | type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) 16 | -------------------------------------------------------------------------------- /configs/wider_face/retinanet_r50_fpn_1x_widerface.py: -------------------------------------------------------------------------------- 1 | _base_ = [ 2 | '../_base_/models/retinanet_r50_fpn.py', 3 | '../_base_/datasets/wider_face.py', '../_base_/schedules/schedule_1x.py', 4 | '../_base_/default_runtime.py' 5 | ] 6 | # model settings 7 | model = dict(bbox_head=dict(num_classes=1)) 8 | # optimizer 9 | optim_wrapper = dict( 10 | optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)) 11 | -------------------------------------------------------------------------------- /configs/yolact/yolact_r101_1xb8-55e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './yolact_r50_1xb8-55e_coco.py' 2 | 3 | model = dict( 4 | backbone=dict( 5 | depth=101, 6 | init_cfg=dict(type='Pretrained', 7 | checkpoint='torchvision://resnet101'))) 8 | -------------------------------------------------------------------------------- /configs/yolo/yolov3_d53_8xb8-amp-ms-608-273e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './yolov3_d53_8xb8-ms-608-273e_coco.py' 2 | # fp16 settings 3 | optim_wrapper = dict(type='AmpOptimWrapper', loss_scale='dynamic') 4 | -------------------------------------------------------------------------------- /configs/yolox/yolox_l_8xb8-300e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './yolox_s_8xb8-300e_coco.py' 2 | 3 | # model settings 4 | model = dict( 5 | backbone=dict(deepen_factor=1.0, widen_factor=1.0), 6 | neck=dict( 7 | in_channels=[256, 512, 1024], out_channels=256, num_csp_blocks=3), 8 | bbox_head=dict(in_channels=256, feat_channels=256)) 9 | -------------------------------------------------------------------------------- /configs/yolox/yolox_m_8xb8-300e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './yolox_s_8xb8-300e_coco.py' 2 | 3 | # model settings 4 | model = dict( 5 | backbone=dict(deepen_factor=0.67, widen_factor=0.75), 6 | neck=dict(in_channels=[192, 384, 768], out_channels=192, num_csp_blocks=2), 7 | bbox_head=dict(in_channels=192, feat_channels=192), 8 | ) 9 | -------------------------------------------------------------------------------- /configs/yolox/yolox_nano_8xb8-300e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './yolox_tiny_8xb8-300e_coco.py' 2 | 3 | # model settings 4 | model = dict( 5 | backbone=dict(deepen_factor=0.33, widen_factor=0.25, use_depthwise=True), 6 | neck=dict( 7 | in_channels=[64, 128, 256], 8 | out_channels=64, 9 | num_csp_blocks=1, 10 | use_depthwise=True), 11 | bbox_head=dict(in_channels=64, feat_channels=64, use_depthwise=True)) 12 | -------------------------------------------------------------------------------- /configs/yolox/yolox_x_8xb8-300e_coco.py: -------------------------------------------------------------------------------- 1 | _base_ = './yolox_s_8xb8-300e_coco.py' 2 | 3 | # model settings 4 | model = dict( 5 | backbone=dict(deepen_factor=1.33, widen_factor=1.25), 6 | neck=dict( 7 | in_channels=[320, 640, 1280], out_channels=320, num_csp_blocks=4), 8 | bbox_head=dict(in_channels=320, feat_channels=320)) 9 | -------------------------------------------------------------------------------- /icons/mmlab.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ikomia-hub/train_mmlab_detection/63795200e4c00fc95fd1cfc3acd4a58baa1c3140/icons/mmlab.png -------------------------------------------------------------------------------- /requirements1.txt: -------------------------------------------------------------------------------- 1 | --find-links https://download.pytorch.org/whl/torch_stable.html 2 | torch==2.1.2+cu121; python_version >= "3.11" 3 | torchvision==0.16.2+cu121; python_version >= "3.11" 4 | torch==1.13.1+cu116; python_version == "3.10" 5 | torchvision==0.14.1+cu116; python_version == "3.10" 6 | torch==1.9.0+cu111; python_version < "3.10" 7 | torchvision==0.10.0+cu111; python_version < "3.10" 8 | 9 | git+https://github.com/cocodataset/panopticapi.git 10 | 11 | --find-links https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html 12 | mmcv==2.0.0; python_version < "3.10" 13 | mmdet==3.0.0; python_version < "3.10" 14 | mmengine==0.7.1; python_version < "3.10" 15 | 16 | pillow<=9.5.0 17 | -------------------------------------------------------------------------------- /requirements2.txt: -------------------------------------------------------------------------------- 1 | --find-links https://download.openmmlab.com/mmcv/dist/cu116/torch1.13/index.html 2 | mmcv==2.0.0; python_version == "3.10" 3 | mmdet==3.0.0; python_version == "3.10" 4 | mmengine==0.7.1; python_version == "3.10" -------------------------------------------------------------------------------- /requirements3.txt: -------------------------------------------------------------------------------- 1 | --find-links https://download.openmmlab.com/mmcv/dist/cu121/torch2.1/index.html 2 | mmcv==2.1.0; python_version >= "3.11" 3 | mmdet==3.2.0; python_version >= "3.11" 4 | mmengine==0.7.1; python_version >= "3.11" -------------------------------------------------------------------------------- /train_mmlab_detection_test.py: -------------------------------------------------------------------------------- 1 | import logging 2 | from ikomia.utils.tests import run_for_test 3 | 4 | logger = logging.getLogger(__name__) 5 | 6 | 7 | def test(t, data_dict): 8 | logger.info("===== Test::train mmlab detection =====") 9 | input_dataset = t.get_input(0) 10 | params = t.get_parameters() 11 | params["epochs"] = "2" 12 | params["batch_size"] = "1" 13 | params["dataset_split_ratio"] = "50" 14 | t.set_parameters(params) 15 | input_dataset.load(data_dict["datasets"]["detection"]["dataset_coco"]) 16 | yield run_for_test(t) --------------------------------------------------------------------------------