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Fix dcff related issue [464] & [469]，add url for model & log #473

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Fix dcff related issue [464] & [469]，add url for model & log #473

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6c3ff54
fix issue 464&469
Lxtccc Mar 7, 2023
107334c
add data url
Lxtccc Mar 7, 2023
814d24d
update
Lxtccc Mar 7, 2023
48038d2
fix ci
Lxtccc Mar 7, 2023
0fe347f
fix conflict
Lxtccc Mar 16, 2023
ec87694
update readme
Lxtccc Mar 16, 2023
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add data url
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@Lxtccc
Lxtccc committed Mar 7, 2023
commit 107334cddbde8be923111a5f9e9182dd21416695
8 changes: 4 additions & 4 deletions configs/pruning/mmcls/dcff/README.md
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Expand Up @@ -12,25 +12,25 @@ The mainstream approach for filter pruning is usually either to force a hard-cod

| Dataset | Backbone | Params(M) | FLOPs(M) | lr_type | Top-1 (%) | Top-5 (%) | CPrate | Config | Download |
| :------: | :----------: | :-------: | :------: | :-----: | :-------: | :-------: | :---------------------------------------------: | :--------------------------------------------------: | :--------------------------: |
| ImageNet | DCFFResNet50 | 15.16 | 2260 | step | 73.96 | 91.66 | \[0.0\]+\[0.35,0.4,0.1\]\*10+\[0.3,0.3,0.1\]\*6 | [config](../../mmcls/dcff/dcff_resnet_8xb32_in1k.py) | [model](<>) \| \[log\] (\<>) |
| ImageNet | DCFFResNet50 | 15.16 | 2260 | step | 73.96 | 91.66 | \[0.0\]+\[0.35,0.4,0.1\]\*10+\[0.3,0.3,0.1\]\*6 | [config](../../mmcls/dcff/dcff_resnet_8xb32_in1k.py) | [model](<https://download.openmmlab.com/mmrazor/v1/pruning/dcff/mmcls/dcff_mmcls.pth>) \ [log](<https://download.openmmlab.com/mmrazor/v1/pruning/dcff/mmcls/dcff_mmcls_sup_20220906_131949.log>) |

### 2. Detection

| Dataset | Method | Backbone | Style | Lr schd | Params(M) | FLOPs(M) | bbox AP | CPrate | Config | Download |
| :-----: | :---------: | :----------: | :-----: | :-----: | :-------: | :------: | :-----: | :---------------------------------------------: | :---------------------------------------------------------------: | :--------------------------: |
| COCO | Faster_RCNN | DCFFResNet50 | pytorch | step | 33.31 | 168320 | 35.8 | \[0.0\]+\[0.35,0.4,0.1\]\*10+\[0.3,0.3,0.1\]\*6 | [config](../../mmdet/dcff/dcff_faster_rcnn_resnet50_8xb4_coco.py) | [model](<>) \| \[log\] (\<>) |
| COCO | Faster_RCNN | DCFFResNet50 | pytorch | step | 33.31 | 168320 | 35.8 | \[0.0\]+\[0.35,0.4,0.1\]\*10+\[0.3,0.3,0.1\]\*6 | [config](../../mmdet/dcff/dcff_faster_rcnn_resnet50_8xb4_coco.py) | [model](<https://download.openmmlab.com/mmrazor/v1/pruning/dcff/mmdet/dcff_mmdet.pth>) \ [log](<https://download.openmmlab.com/mmrazor/v1/pruning/dcff/mmdet/dcff_mmdet_sup_20220909_103653.log>) |

### 3. Segmentation

| Dataset | Method | Backbone | crop size | Lr schd | Params(M) | FLOPs(M) | mIoU | CPrate | Config | Download |
| :--------: | :-------: | :-------------: | :-------: | :-----: | :-------: | :------: | :---: | :-----------------------------------------------------------------: | :-------------------------------------------------------------------: | :--------------------------: |
| Cityscapes | PointRend | DCFFResNetV1c50 | 512x1024 | 160k | 18.43 | 74410 | 76.75 | \[0.0, 0.0, 0.0\] + \[0.35, 0.4, 0.1\] * 10 + \[0.3, 0.3, 0.1\] * 6 | [config](../../mmseg/dcff/dcff_pointrend_resnet50_8xb2_cityscapes.py) | [model](<>) \| \[log\] (\<>) |
| Cityscapes | PointRend | DCFFResNetV1c50 | 512x1024 | 160k | 18.43 | 74410 | 76.75 | \[0.0, 0.0, 0.0\] + \[0.35, 0.4, 0.1\] * 10 + \[0.3, 0.3, 0.1\] * 6 | [config](../../mmseg/dcff/dcff_pointrend_resnet50_8xb2_cityscapes.py) | [model](<https://download.openmmlab.com/mmrazor/v1/pruning/dcff/mmseg/dcff_mmseg.pth>) \ [log](<https://download.openmmlab.com/mmrazor/v1/pruning/dcff/mmseg/dcff_mmpose_sup_20220908_172111.log>) |

### 4. Pose

| Dataset | Method | Backbone | crop size | total epochs | Params(M) | FLOPs(M) | AP | CPrate | Config | Download |
| :-----: | :-------------: | :----------: | :-------: | :----------: | :-------: | :------: | :--: | :--------------------------------------------------------: | :---------------------------------------------------------------: | :--------------------------: |
| COCO | TopDown HeatMap | DCFFResNet50 | 256x192 | 300 | 26.95 | 4290 | 68.3 | \[0.0\] + \[0.2, 0.2, 0.1\] * 10 + \[0.15, 0.15, 0.1\] * 6 | [config](../../mmpose/dcff/dcff_topdown_heatmap_resnet50_coco.py) | [model](<>) \| \[log\] (\<>) |
| COCO | TopDown HeatMap | DCFFResNet50 | 256x192 | 300 | 26.95 | 4290 | 68.3 | \[0.0\] + \[0.2, 0.2, 0.1\] * 10 + \[0.15, 0.15, 0.1\] * 6 | [config](../../mmpose/dcff/dcff_topdown_heatmap_resnet50_coco.py) | [model](<https://download.openmmlab.com/mmrazor/v1/pruning/dcff/mmpose/dcff_pose.pth>) \ [log](<https://download.openmmlab.com/mmrazor/v1/pruning/dcff/mmpose/dcff_mmpose_20220908_140331.log>) |

## Citation

Expand Down
8 changes: 4 additions & 4 deletions configs/pruning/mmpose/dcff/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -12,25 +12,25 @@ The mainstream approach for filter pruning is usually either to force a hard-cod

| Dataset | Backbone | Params(M) | FLOPs(M) | lr_type | Top-1 (%) | Top-5 (%) | CPrate | Config | Download |
| :------: | :----------: | :-------: | :------: | :-----: | :-------: | :-------: | :---------------------------------------------: | :--------------------------------------------------: | :--------------------------: |
| ImageNet | DCFFResNet50 | 15.16 | 2260 | step | 73.96 | 91.66 | \[0.0\]+\[0.35,0.4,0.1\]\*10+\[0.3,0.3,0.1\]\*6 | [config](../../mmcls/dcff/dcff_resnet_8xb32_in1k.py) | [model](<>) \| \[log\] (\<>) |
| ImageNet | DCFFResNet50 | 15.16 | 2260 | step | 73.96 | 91.66 | \[0.0\]+\[0.35,0.4,0.1\]\*10+\[0.3,0.3,0.1\]\*6 | [config](../../mmcls/dcff/dcff_resnet_8xb32_in1k.py) | [model](<https://download.openmmlab.com/mmrazor/v1/pruning/dcff/mmcls/dcff_mmcls.pth>) \ [log](<https://download.openmmlab.com/mmrazor/v1/pruning/dcff/mmcls/dcff_mmcls_sup_20220906_131949.log>) |

### 2. Detection

| Dataset | Method | Backbone | Style | Lr schd | Params(M) | FLOPs(M) | bbox AP | CPrate | Config | Download |
| :-----: | :---------: | :----------: | :-----: | :-----: | :-------: | :------: | :-----: | :---------------------------------------------: | :---------------------------------------------------------------: | :--------------------------: |
| COCO | Faster_RCNN | DCFFResNet50 | pytorch | step | 33.31 | 168320 | 35.8 | \[0.0\]+\[0.35,0.4,0.1\]\*10+\[0.3,0.3,0.1\]\*6 | [config](../../mmdet/dcff/dcff_faster_rcnn_resnet50_8xb4_coco.py) | [model](<>) \| \[log\] (\<>) |
| COCO | Faster_RCNN | DCFFResNet50 | pytorch | step | 33.31 | 168320 | 35.8 | \[0.0\]+\[0.35,0.4,0.1\]\*10+\[0.3,0.3,0.1\]\*6 | [config](../../mmdet/dcff/dcff_faster_rcnn_resnet50_8xb4_coco.py) | [model](<https://download.openmmlab.com/mmrazor/v1/pruning/dcff/mmdet/dcff_mmdet.pth>) \ [log](<https://download.openmmlab.com/mmrazor/v1/pruning/dcff/mmdet/dcff_mmdet_sup_20220909_103653.log>) |

### 3. Segmentation

| Dataset | Method | Backbone | crop size | Lr schd | Params(M) | FLOPs(M) | mIoU | CPrate | Config | Download |
| :--------: | :-------: | :-------------: | :-------: | :-----: | :-------: | :------: | :---: | :-----------------------------------------------------------------: | :-------------------------------------------------------------------: | :--------------------------: |
| Cityscapes | PointRend | DCFFResNetV1c50 | 512x1024 | 160k | 18.43 | 74410 | 76.75 | \[0.0, 0.0, 0.0\] + \[0.35, 0.4, 0.1\] * 10 + \[0.3, 0.3, 0.1\] * 6 | [config](../../mmseg/dcff/dcff_pointrend_resnet50_8xb2_cityscapes.py) | [model](<>) \| \[log\] (\<>) |
| Cityscapes | PointRend | DCFFResNetV1c50 | 512x1024 | 160k | 18.43 | 74410 | 76.75 | \[0.0, 0.0, 0.0\] + \[0.35, 0.4, 0.1\] * 10 + \[0.3, 0.3, 0.1\] * 6 | [config](../../mmseg/dcff/dcff_pointrend_resnet50_8xb2_cityscapes.py) | [model](<https://download.openmmlab.com/mmrazor/v1/pruning/dcff/mmseg/dcff_mmseg.pth>) \ [log](<https://download.openmmlab.com/mmrazor/v1/pruning/dcff/mmseg/dcff_mmpose_sup_20220908_172111.log>) |

### 4. Pose

| Dataset | Method | Backbone | crop size | total epochs | Params(M) | FLOPs(M) | AP | CPrate | Config | Download |
| :-----: | :-------------: | :----------: | :-------: | :----------: | :-------: | :------: | :--: | :--------------------------------------------------------: | :---------------------------------------------------------------: | :--------------------------: |
| COCO | TopDown HeatMap | DCFFResNet50 | 256x192 | 300 | 26.95 | 4290 | 68.3 | \[0.0\] + \[0.2, 0.2, 0.1\] * 10 + \[0.15, 0.15, 0.1\] * 6 | [config](../../mmpose/dcff/dcff_topdown_heatmap_resnet50_coco.py) | [model](<>) \| \[log\] (\<>) |
| COCO | TopDown HeatMap | DCFFResNet50 | 256x192 | 300 | 26.95 | 4290 | 68.3 | \[0.0\] + \[0.2, 0.2, 0.1\] * 10 + \[0.15, 0.15, 0.1\] * 6 | [config](../../mmpose/dcff/dcff_topdown_heatmap_resnet50_coco.py) | [model](<https://download.openmmlab.com/mmrazor/v1/pruning/dcff/mmpose/dcff_pose.pth>) \ [log](<https://download.openmmlab.com/mmrazor/v1/pruning/dcff/mmpose/dcff_mmpose_20220908_140331.log>) |

## Citation

Expand Down