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mmdet 2.25.0
Browse files- mmdet_configs/README.md +1 -1
- mmdet_configs/configs.tar +2 -2
- model_dict/instance_segmentation.yaml +12 -0
- model_dict/panoptic_segmentation.yaml +6 -0
- requirements.txt +1 -1
mmdet_configs/README.md
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`configs.tar` is a tarball of https://github.com/open-mmlab/mmdetection/tree/v2.
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The license file of the mmdetection is also included in this directory.
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`configs.tar` is a tarball of https://github.com/open-mmlab/mmdetection/tree/v2.25.0/configs.
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The license file of the mmdetection is also included in this directory.
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mmdet_configs/configs.tar
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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size 3604480
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model_dict/instance_segmentation.yaml
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DetectoRS (HTC + ResNet-101):
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config: https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_htc_r101_20e_coco.py
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model: https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r101_20e_coco/detectors_htc_r101_20e_coco_20210419_203638-348d533b.pth
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SCNet (X-101-64x4d-FPN):
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config: https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py
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model: https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_x101_64x4d_fpn_20e_coco/scnet_x101_64x4d_fpn_20e_coco-fb09dec9.pth
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QueryInst (R-101-FPN):
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config: https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
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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
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DetectoRS (HTC + ResNet-101):
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config: https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_htc_r101_20e_coco.py
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model: https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r101_20e_coco/detectors_htc_r101_20e_coco_20210419_203638-348d533b.pth
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#SOLOv2 (R-50):
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# config: https://github.com/open-mmlab/mmdetection/tree/master/configs/solov2/solov2_r50_fpn_3x_coco.py
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# model: https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_r50_fpn_3x_coco/solov2_r50_fpn_3x_coco_20220512_125856-fed092d4.pth
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#SOLOv2 (X-101 (DCN)):
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# config: https://github.com/open-mmlab/mmdetection/tree/master/configs/solov2/solov2_x101_dcn_fpn_3x_coco.py
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# 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
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SCNet (X-101-64x4d-FPN):
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config: https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py
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model: https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_x101_64x4d_fpn_20e_coco/scnet_x101_64x4d_fpn_20e_coco-fb09dec9.pth
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QueryInst (R-101-FPN):
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config: https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
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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
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Mask2Former (R-50):
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config: https://github.com/open-mmlab/mmdetection/tree/master/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco.py
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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
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Mask2Former (Swin-S):
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config: https://github.com/open-mmlab/mmdetection/tree/master/configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco.py
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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
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model_dict/panoptic_segmentation.yaml
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MaskFormer (Swin-L):
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config: https://github.com/open-mmlab/mmdetection/tree/master/configs/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco.py
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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
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MaskFormer (Swin-L):
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config: https://github.com/open-mmlab/mmdetection/tree/master/configs/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco.py
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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
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Mask2Former (R-50):
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config: https://github.com/open-mmlab/mmdetection/tree/master/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic.py
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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
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Mask2Former (Swin-L):
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config: https://github.com/open-mmlab/mmdetection/tree/master/configs/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic.py
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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
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requirements.txt
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mmcv-full==1.5.2
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mmdet==2.
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numpy==1.22.4
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opencv-python-headless==4.5.5.64
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openmim==0.1.5
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mmcv-full==1.5.2
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mmdet==2.25.0
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numpy==1.22.4
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opencv-python-headless==4.5.5.64
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openmim==0.1.5
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