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[Feature] Add UNet benchmark with multiple losses supervision (#1143)
* upload models and new configs * fix hrf readme error * fix hrf readme error * add mDice of old models * refactor configs
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MengzhangLI
GitHub
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@ -37,34 +37,53 @@ There is large consent that successful training of deep networks requires many t
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## Results and models
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### Cityscapes
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| Method | Backbone | Loss | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
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| ------ | --------- | --- |--------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| FCN | UNet-S5-D16 | Cross Entropy | 512x1024 | 160000 | 17.91 | 3.05 | 69.10 | 71.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204-6860854e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204.log.json) |
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### DRIVE
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| Method | Backbone | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Dice | config | download |
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| ----------- | --------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ----: | ------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| FCN | UNet-S5-D16 | 584x565 | 64x64 | 42x42 | 40000 | 0.680 | - | 78.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-5daf6d3b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_64x64_40k_drive/unet_s5-d16_64x64_40k_drive-20201223_191051.log.json) |
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| PSPNet | UNet-S5-D16 | 584x565 | 64x64 | 42x42 | 40000 | 0.599 | - | 78.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive-20201227_181818.log.json) |
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| DeepLabV3 | UNet-S5-D16 | 584x565 | 64x64 | 42x42 | 40000 | 0.596 | - | 78.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive-20201226_094047.log.json) |
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| Method | Backbone | Loss | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | mDice | Dice | config | download |
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| ----------- | --------- | -------------------- |---------- | --------- | -----: | ------- | -------- | -------------: | --: |----: | ------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| FCN | UNet-S5-D16 | Cross Entropy | 584x565 | 64x64 | 42x42 | 40000 | 0.680 | - | 88.38 | 78.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-5daf6d3b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_64x64_40k_drive/unet_s5-d16_64x64_40k_drive-20201223_191051.log.json) |
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| FCN | UNet-S5-D16 | Cross Entropy + Dice | 584x565 | 64x64 | 42x42 | 40000 | 0.582 | - | 88.71 | 79.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820-785de5c2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820.log.json) |
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| PSPNet | UNet-S5-D16 | Cross Entropy | 584x565 | 64x64 | 42x42 | 40000 | 0.599 | - | 88.35 | 78.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive-20201227_181818.log.json) |
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| PSPNet | UNet-S5-D16 | Cross Entropy + Dice | 584x565 | 64x64 | 42x42 | 40000 | 0.585 | - | 88.76 | 79.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821-22b3e3ba.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821.log.json) |
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| DeepLabV3 | UNet-S5-D16 | Cross Entropy | 584x565 | 64x64 | 42x42 | 40000 | 0.596 | - | 88.38 |78.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive-20201226_094047.log.json) |
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| DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 584x565 | 64x64 | 42x42 | 40000 | 0.582 | - | 88.84 | 79.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825-6bf0efd7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825.log.json) |
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### STARE
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| Method | Backbone | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Dice | config | download |
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| ----------- | --------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ----: | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| FCN | UNet-S5-D16 | 605x700 | 128x128 | 85x85 | 40000 | 0.968 | - | 81.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-7d77e78b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_stare/unet_s5-d16_128x128_40k_stare-20201223_191051.log.json) |
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| PSPNet | UNet-S5-D16 | 605x700 | 128x128 | 85x85 | 40000 | 0.982 | - | 81.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare-20201227_181818.log.json) |
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| DeepLabV3 | UNet-S5-D16 | 605x700 | 128x128 | 85x85 | 40000 | 0.999 | - | 80.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare-20201226_094047.log.json) |
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| Method | Backbone | Loss | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | mDice | Dice | config | download |
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| ----------- | --------| --------------- | ---------- | --------- | -----: | ------- | -------- | -------------: | --: |----: | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| FCN | UNet-S5-D16 | Cross Entropy | 605x700 | 128x128 | 85x85 | 40000 | 0.968 | - | 89.78 | 81.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-7d77e78b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_stare/unet_s5-d16_128x128_40k_stare-20201223_191051.log.json) |
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| FCN | UNet-S5-D16 | Cross Entropy + Dice | 605x700 | 128x128 | 85x85 | 40000 | 0.986 | - | 90.65 | 82.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821-f75705a9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821.log.json) |
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| PSPNet | UNet-S5-D16 | Cross Entropy | 605x700 | 128x128 | 85x85 | 40000 | 0.982 | - | 89.89 | 81.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare-20201227_181818.log.json) |
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| PSPNet | UNet-S5-D16 | Cross Entropy + Dice | 605x700 | 128x128 | 85x85 | 40000 | 1.028 | - | 90.72 | 82.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823-f1063ef7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823.log.json) |
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| DeepLabV3 | UNet-S5-D16 | Cross Entropy | 605x700 | 128x128 | 85x85 | 40000 | 0.999 | - | 89.73 | 80.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare-20201226_094047.log.json) |
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| DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 605x700 | 128x128 | 85x85 | 40000 | 1.010 | - | 90.65 | 82.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825-21db614c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825.log.json) |
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### CHASE_DB1
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| Method | Backbone | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Dice | config | download |
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| ----------- | --------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ----: | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| FCN | UNet-S5-D16 | 960x999 | 128x128 | 85x85 | 40000 | 0.968 | - | 80.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-11543527.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_chase_db1/unet_s5-d16_128x128_40k_chase_db1-20201223_191051.log.json) |
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| PSPNet | UNet-S5-D16 | 960x999 | 128x128 | 85x85 | 40000 | 0.982 | - | 80.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1-20201227_181818.log.json) |
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| DeepLabV3 | UNet-S5-D16 | 960x999 | 128x128 | 85x85 | 40000 | 0.999 | - | 80.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1-20201226_094047.log.json) |
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| Method | Backbone | Loss | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | mDice | Dice | config | download |
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| ----------- | --------- | --------------- | ---------- | --------- | -----: | ------- | -------- | -------------: | --: |----: | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| FCN | UNet-S5-D16 | Cross Entropy | 960x999 | 128x128 | 85x85 | 40000 | 0.968 | - | 89.46 |80.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-11543527.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_128x128_40k_chase_db1/unet_s5-d16_128x128_40k_chase_db1-20201223_191051.log.json) |
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| FCN | UNet-S5-D16 | Cross Entropy + Dice | 960x999 | 128x128 | 85x85 | 40000 | 0.986 | - | 89.52 | 80.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821-1c4eb7cf.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821.log.json) |
|
||||
| PSPNet | UNet-S5-D16 | Cross Entropy | 960x999 | 128x128 | 85x85 | 40000 | 0.982 | - | 89.52 |80.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1-20201227_181818.log.json) |
|
||||
| PSPNet | UNet-S5-D16 | Cross Entropy + Dice | 960x999 | 128x128 | 85x85 | 40000 | 1.028 | - | 89.45 | 80.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823-c0802c4d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823.log.json) |
|
||||
| DeepLabV3 | UNet-S5-D16 | Cross Entropy | 960x999 | 128x128 | 85x85 | 40000 | 0.999 | - | 89.57 |80.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1-20201226_094047.log.json) |
|
||||
| DeepLabV3 | UNet-S5-D16 | Cross Entropy + Dice | 960x999 | 128x128 | 85x85 | 40000 | 1.010 | - | 89.49 | 80.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825-4ef29df5.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825.log.json) |
|
||||
|
||||
### HRF
|
||||
|
||||
| Method | Backbone | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Dice | config | download |
|
||||
| ----------- | --------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ----: | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| FCN | UNet-S5-D16 | 2336x3504 | 256x256 | 170x170 | 40000 | 2.525 | - | 79.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-d89cf1ed.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_256x256_40k_hrf/unet_s5-d16_256x256_40k_hrf-20201223_173724.log.json) |
|
||||
| PSPNet | UNet-S5-D16 | 2336x3504 | 256x256 | 170x170 | 40000 | 2.588 | - | 80.07 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf-20201227_181818.log.json) |
|
||||
| DeepLabV3 | UNet-S5-D16 | 2336x3504 | 256x256 | 170x170 | 40000 | 2.604 | - | 80.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf-20201226_094047.log.json) |
|
||||
| Method | Backbone | Loss | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | mDice | Dice | config | download |
|
||||
| ----------- | --------- | --------------- | ---------- | --------- | -----: | ------- | -------- | -------------: | --: |----: | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| FCN | UNet-S5-D16 | Cross Entropy | 2336x3504 | 256x256 | 170x170 | 40000 | 2.525 | - | 88.92 |79.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-d89cf1ed.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/unet_s5-d16_256x256_40k_hrf/unet_s5-d16_256x256_40k_hrf-20201223_173724.log.json) |
|
||||
| FCN | UNet-S5-D16 | Cross Entropy + Dice | 2336x3504 | 256x256 | 170x170 | 40000 | 2.623 | - | 89.64 | 80.87 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821-c314da8a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821.log.json) |
|
||||
| PSPNet | UNet-S5-D16 | Cross Entropy | 2336x3504 | 256x256 | 170x170 | 40000 | 2.588 | - | 89.24 |80.07 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf-20201227_181818.log.json) |
|
||||
| PSPNet | UNet-S5-D16 | Cross Entropy + Dice | 2336x3504 | 256x256 | 170x170 | 40000 | 2.798 | - | 89.69 | 80.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823-53d492fa.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823.log.json) |
|
||||
| DeepLabV3 | UNet-S5-D16| Cross Entropy | 2336x3504 | 256x256 | 170x170 | 40000 | 2.604 | - | 89.32 |80.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf-20201226_094047.log.json) |
|
||||
| DeepLabV3 | UNet-S5-D16| Cross Entropy + Dice | 2336x3504 | 256x256 | 170x170 | 40000 | 2.607 | - | 89.56 | 80.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032-59daf7a4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032.log.json) |
|
||||
|
||||
@ -0,0 +1,6 @@
|
||||
_base_ = './deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@ -0,0 +1,6 @@
|
||||
_base_ = './deeplabv3_unet_s5-d16_128x128_40k_stare.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@ -0,0 +1,6 @@
|
||||
_base_ = './deeplabv3_unet_s5-d16_256x256_40k_hrf.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@ -0,0 +1,6 @@
|
||||
_base_ = './deeplabv3_unet_s5-d16_64x64_40k_drive.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
16
configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py
Normal file
16
configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py
Normal file
@ -0,0 +1,16 @@
|
||||
_base_ = [
|
||||
'../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/cityscapes.py',
|
||||
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
|
||||
]
|
||||
|
||||
model = dict(
|
||||
decode_head=dict(num_classes=19),
|
||||
auxiliary_head=dict(num_classes=19),
|
||||
# model training and testing settings
|
||||
train_cfg=dict(),
|
||||
test_cfg=dict(mode='whole'))
|
||||
|
||||
data = dict(
|
||||
samples_per_gpu=4,
|
||||
workers_per_gpu=4,
|
||||
)
|
||||
@ -0,0 +1,6 @@
|
||||
_base_ = './fcn_unet_s5-d16_128x128_40k_chase_db1.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@ -0,0 +1,6 @@
|
||||
_base_ = './fcn_unet_s5-d16_128x128_40k_stare.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@ -0,0 +1,6 @@
|
||||
_base_ = './fcn_unet_s5-d16_256x256_40k_hrf.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@ -0,0 +1,6 @@
|
||||
_base_ = './fcn_unet_s5-d16_64x64_40k_drive.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@ -0,0 +1,6 @@
|
||||
_base_ = './pspnet_unet_s5-d16_128x128_40k_chase_db1.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@ -0,0 +1,6 @@
|
||||
_base_ = './pspnet_unet_s5-d16_128x128_40k_stare.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@ -0,0 +1,6 @@
|
||||
_base_ = './pspnet_unet_s5-d16_256x256_40k_hrf.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@ -0,0 +1,6 @@
|
||||
_base_ = './pspnet_unet_s5-d16_64x64_40k_drive.py'
|
||||
model = dict(
|
||||
decode_head=dict(loss_decode=[
|
||||
dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=1.0),
|
||||
dict(type='DiceLoss', loss_name='loss_dice', loss_weight=3.0)
|
||||
]))
|
||||
@ -2,6 +2,7 @@ Collections:
|
||||
- Name: unet
|
||||
Metadata:
|
||||
Training Data:
|
||||
- Cityscapes
|
||||
- DRIVE
|
||||
- STARE
|
||||
- CHASE_DB1
|
||||
@ -16,6 +17,28 @@ Collections:
|
||||
Converted From:
|
||||
Code: http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net
|
||||
Models:
|
||||
- Name: fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
backbone: UNet-S5-D16
|
||||
crop size: (512,1024)
|
||||
lr schd: 160000
|
||||
inference time (ms/im):
|
||||
- value: 327.87
|
||||
hardware: V100
|
||||
backend: PyTorch
|
||||
batch size: 1
|
||||
mode: FP32
|
||||
resolution: (512,1024)
|
||||
Training Memory (GB): 17.91
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: Cityscapes
|
||||
Metrics:
|
||||
mIoU: 69.1
|
||||
mIoU(ms+flip): 71.05
|
||||
Config: configs/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes/fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes_20211210_145204-6860854e.pth
|
||||
- Name: fcn_unet_s5-d16_64x64_40k_drive
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
@ -30,6 +53,20 @@ Models:
|
||||
Dice: 78.67
|
||||
Config: configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-5daf6d3b.pth
|
||||
- Name: fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
backbone: UNet-S5-D16
|
||||
crop size: (64,64)
|
||||
lr schd: 40000
|
||||
Training Memory (GB): 0.582
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: DRIVE
|
||||
Metrics:
|
||||
Dice: 79.32
|
||||
Config: configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/fcn_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201820-785de5c2.pth
|
||||
- Name: pspnet_unet_s5-d16_64x64_40k_drive
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
@ -44,6 +81,20 @@ Models:
|
||||
Dice: 78.62
|
||||
Config: configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth
|
||||
- Name: pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
backbone: UNet-S5-D16
|
||||
crop size: (64,64)
|
||||
lr schd: 40000
|
||||
Training Memory (GB): 0.585
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: DRIVE
|
||||
Metrics:
|
||||
Dice: 79.42
|
||||
Config: configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/pspnet_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201821-22b3e3ba.pth
|
||||
- Name: deeplabv3_unet_s5-d16_64x64_40k_drive
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
@ -58,6 +109,20 @@ Models:
|
||||
Dice: 78.69
|
||||
Config: configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth
|
||||
- Name: deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
backbone: UNet-S5-D16
|
||||
crop size: (64,64)
|
||||
lr schd: 40000
|
||||
Training Memory (GB): 0.582
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: DRIVE
|
||||
Metrics:
|
||||
Dice: 79.56
|
||||
Config: configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_64x64_40k_drive_20211210_201825-6bf0efd7.pth
|
||||
- Name: fcn_unet_s5-d16_128x128_40k_stare
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
@ -72,6 +137,20 @@ Models:
|
||||
Dice: 81.02
|
||||
Config: configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-7d77e78b.pth
|
||||
- Name: fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
backbone: UNet-S5-D16
|
||||
crop size: (128,128)
|
||||
lr schd: 40000
|
||||
Training Memory (GB): 0.986
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: STARE
|
||||
Metrics:
|
||||
Dice: 82.7
|
||||
Config: configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201821-f75705a9.pth
|
||||
- Name: pspnet_unet_s5-d16_128x128_40k_stare
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
@ -86,6 +165,20 @@ Models:
|
||||
Dice: 81.22
|
||||
Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth
|
||||
- Name: pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
backbone: UNet-S5-D16
|
||||
crop size: (128,128)
|
||||
lr schd: 40000
|
||||
Training Memory (GB): 1.028
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: STARE
|
||||
Metrics:
|
||||
Dice: 82.84
|
||||
Config: configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201823-f1063ef7.pth
|
||||
- Name: deeplabv3_unet_s5-d16_128x128_40k_stare
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
@ -100,6 +193,20 @@ Models:
|
||||
Dice: 80.93
|
||||
Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth
|
||||
- Name: deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
backbone: UNet-S5-D16
|
||||
crop size: (128,128)
|
||||
lr schd: 40000
|
||||
Training Memory (GB): 1.01
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: STARE
|
||||
Metrics:
|
||||
Dice: 82.71
|
||||
Config: configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_stare_20211210_201825-21db614c.pth
|
||||
- Name: fcn_unet_s5-d16_128x128_40k_chase_db1
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
@ -114,6 +221,20 @@ Models:
|
||||
Dice: 80.24
|
||||
Config: configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-11543527.pth
|
||||
- Name: fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
backbone: UNet-S5-D16
|
||||
crop size: (128,128)
|
||||
lr schd: 40000
|
||||
Training Memory (GB): 0.986
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: CHASE_DB1
|
||||
Metrics:
|
||||
Dice: 80.4
|
||||
Config: configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/fcn_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201821-1c4eb7cf.pth
|
||||
- Name: pspnet_unet_s5-d16_128x128_40k_chase_db1
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
@ -128,6 +249,20 @@ Models:
|
||||
Dice: 80.36
|
||||
Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth
|
||||
- Name: pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
backbone: UNet-S5-D16
|
||||
crop size: (128,128)
|
||||
lr schd: 40000
|
||||
Training Memory (GB): 1.028
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: CHASE_DB1
|
||||
Metrics:
|
||||
Dice: 80.28
|
||||
Config: configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/pspnet_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201823-c0802c4d.pth
|
||||
- Name: deeplabv3_unet_s5-d16_128x128_40k_chase_db1
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
@ -142,6 +277,20 @@ Models:
|
||||
Dice: 80.47
|
||||
Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth
|
||||
- Name: deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
backbone: UNet-S5-D16
|
||||
crop size: (128,128)
|
||||
lr schd: 40000
|
||||
Training Memory (GB): 1.01
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: CHASE_DB1
|
||||
Metrics:
|
||||
Dice: 80.37
|
||||
Config: configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_128x128_40k_chase-db1_20211210_201825-4ef29df5.pth
|
||||
- Name: fcn_unet_s5-d16_256x256_40k_hrf
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
@ -156,6 +305,20 @@ Models:
|
||||
Dice: 79.45
|
||||
Config: configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-d89cf1ed.pth
|
||||
- Name: fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
backbone: UNet-S5-D16
|
||||
crop size: (256,256)
|
||||
lr schd: 40000
|
||||
Training Memory (GB): 2.623
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: HRF
|
||||
Metrics:
|
||||
Dice: 80.87
|
||||
Config: configs/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/unet/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/fcn_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201821-c314da8a.pth
|
||||
- Name: pspnet_unet_s5-d16_256x256_40k_hrf
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
@ -170,6 +333,20 @@ Models:
|
||||
Dice: 80.07
|
||||
Config: configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth
|
||||
- Name: pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
backbone: UNet-S5-D16
|
||||
crop size: (256,256)
|
||||
lr schd: 40000
|
||||
Training Memory (GB): 2.798
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: HRF
|
||||
Metrics:
|
||||
Dice: 80.96
|
||||
Config: configs/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/pspnet_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_201823-53d492fa.pth
|
||||
- Name: deeplabv3_unet_s5-d16_256x256_40k_hrf
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
@ -184,3 +361,17 @@ Models:
|
||||
Dice: 80.21
|
||||
Config: configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth
|
||||
- Name: deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf
|
||||
In Collection: unet
|
||||
Metadata:
|
||||
backbone: UNet-S5-D16
|
||||
crop size: (256,256)
|
||||
lr schd: 40000
|
||||
Training Memory (GB): 2.607
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: HRF
|
||||
Metrics:
|
||||
Dice: 80.71
|
||||
Config: configs/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf/deeplabv3_unet_s5-d16_ce-1.0-dice-3.0_256x256_40k_hrf_20211210_202032-59daf7a4.pth
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user