trying to only keep detection and limiting models in yaml

model_dict/detection.yaml

@@ -3,40 +3,4 @@ Faster R-CNN (R-50-FPN):
     model: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth
 Faster R-CNN (X-101-64x4d-FPN):
     config: https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth
-SSD (VGG16):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd/ssd512_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd512_coco/ssd512_coco_20210803_022849-0a47a1ca.pth
-RetinaNet (X-101-64x4d-FPN):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_mstrain_3x_coco/retinanet_x101_64x4d_fpn_mstrain_3x_coco_20210719_051838-022c2187.pth
-YOLOv3 (DarkNet-53 608):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-608_273e_coco/yolov3_d53_mstrain-608_273e_coco_20210518_115020-a2c3acb8.pth
-CornerNet (HourglassNet-104):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720-5fefbf1c.pth
-FCOS (X-101):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco-ede514a8.pth
-DETR:
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/detr/detr_r50_8x2_150e_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth
-YOLOX-tiny:
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox/yolox_tiny_8x8_300e_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_tiny_8x8_300e_coco/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth
-YOLOX-s:
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox/yolox_s_8x8_300e_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_s_8x8_300e_coco/yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth
-YOLOX-l:
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox/yolox_l_8x8_300e_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth
-YOLOX-x:
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox/yolox_x_8x8_300e_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_x_8x8_300e_coco/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth
-Deformable DETR (R-50 two-stage Deformable DETR):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco/deformable_detr_twostage_refine_r50_16x2_50e_coco_20210419_220613-9d28ab72.pth
-TOOD (R-101-dcnv2):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/tood/tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/tood/tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco/tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20211210_213728-4a824142.pth
+    model: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth

model_dict/instance_segmentation.yaml

@@ -1,51 +1,3 @@
 Mask R-CNN (R-50-FPN):
     config: https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_fpn_mstrain-poly_3x_coco_20210524_201154-21b550bb.pth
-Mask R-CNN (X-101-64x4d-FPN):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco_20210526_120447-c376f129.pth
-Cascade Mask R-CNN (X-101-64x4d-FPN):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210719_210311-d3e64ba0.pth
-Mask Scoring R-CNN (R-X101-64x4d):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco/ms_rcnn_x101_64x4d_fpn_1x_coco_20200206-86ba88d2.pth
-HTC (X-101-64x4d-FPN):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_16x1_20e_coco/htc_x101_64x4d_fpn_16x1_20e_coco_20200318-b181fd7a.pth
-YOLACT:
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/yolact/yolact_r50_1x8_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/yolact/yolact_r50_1x8_coco/yolact_r50_1x8_coco_20200908-f38d58df.pth
-Instaboost (Mask R-CNN (X-101-64x4d-FPN)):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947-8ed58c1b.pth
-SOLO:
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/solo/solo_r50_fpn_3x_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/solo/solo_r50_fpn_3x_coco/solo_r50_fpn_3x_coco_20210901_012353-11d224d7.pth
-PointRend (R-50-FPN):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco/point_rend_r50_caffe_fpn_mstrain_3x_coco-e0ebb6b7.pth
-DetectoRS (HTC + ResNet-101):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_htc_r101_20e_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r101_20e_coco/detectors_htc_r101_20e_coco_20210419_203638-348d533b.pth
-SOLOv2 (R-50):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/solov2/solov2_r50_fpn_3x_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_r50_fpn_3x_coco/solov2_r50_fpn_3x_coco_20220512_125856-fed092d4.pth
-SOLOv2 (X-101 (DCN)):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/solov2/solov2_x101_dcn_fpn_3x_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_x101_dcn_fpn_3x_coco/solov2_x101_dcn_fpn_3x_coco_20220513_214337-aef41095.pth
-SCNet (X-101-64x4d-FPN):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_x101_64x4d_fpn_20e_coco/scnet_x101_64x4d_fpn_20e_coco-fb09dec9.pth
-QueryInst (R-50-FPN):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_101802-85cffbd8.pth
-QueryInst (R-101-FPN):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_153621-76cce59f.pth
-Mask2Former (R-50):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r50_lsj_8x2_50e_coco/mask2former_r50_lsj_8x2_50e_coco_20220506_191028-8e96e88b.pth
-Mask2Former (Swin-S):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco_20220504_001756-743b7d99.pth
+    model: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_fpn_mstrain-poly_3x_coco_20210524_201154-21b550bb.pth

model_dict/panoptic_segmentation.yaml

@@ -1,15 +1,3 @@
 Panoptic FPN (R-50-FPN):
     config: https://github.com/open-mmlab/mmdetection/tree/master/configs/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco/panoptic_fpn_r50_fpn_mstrain_3x_coco_20210824_171155-5650f98b.pth
-MaskFormer (R-50):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/maskformer/maskformer_r50_mstrain_16x1_75e_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/maskformer/maskformer_r50_mstrain_16x1_75e_coco/maskformer_r50_mstrain_16x1_75e_coco_20220221_141956-bc2699cb.pth
-MaskFormer (Swin-L):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco_20220326_221612-061b4eb8.pth
-Mask2Former (R-50):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic/mask2former_r50_lsj_8x2_50e_coco-panoptic_20220326_224516-11a44721.pth
-Mask2Former (Swin-L):
-    config: https://github.com/open-mmlab/mmdetection/tree/master/configs/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic.py
-    model: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic_20220407_104949-d4919c44.pth
+    model: https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco/panoptic_fpn_r50_fpn_mstrain_3x_coco_20210824_171155-5650f98b.pth