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https://github.com/open-mmlab/mmsegmentation.git
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fixing dice metric in unet (#1041)
This commit is contained in:
MengzhangLI
GitHub
parent
008856a84c
commit
6d88b07084
@ -176,7 +176,7 @@ def parse_md(md_file):
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'Task': 'Semantic Segmentation',
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'Task': 'Semantic Segmentation',
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'Dataset': current_dataset,
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'Dataset': current_dataset,
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'Metrics': {
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'Metrics': {
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'mIoU': float(els[ss_id]),
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cols[ss_id]: float(els[ss_id]),
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},
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},
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},
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},
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],
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],
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@ -27,7 +27,7 @@ Models:
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- Task: Semantic Segmentation
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- Task: Semantic Segmentation
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Dataset: DRIVE
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Dataset: DRIVE
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Metrics:
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Metrics:
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mIoU: 78.67
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Dice: 78.67
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Config: configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py
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Config: configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py
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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
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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
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- Name: pspnet_unet_s5-d16_64x64_40k_drive
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- Name: pspnet_unet_s5-d16_64x64_40k_drive
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@ -41,7 +41,7 @@ Models:
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- Task: Semantic Segmentation
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- Task: Semantic Segmentation
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Dataset: DRIVE
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Dataset: DRIVE
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Metrics:
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Metrics:
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mIoU: 78.62
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Dice: 78.62
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Config: configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py
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Config: configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py
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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
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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
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- Name: deeplabv3_unet_s5-d16_64x64_40k_drive
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- Name: deeplabv3_unet_s5-d16_64x64_40k_drive
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@ -55,7 +55,7 @@ Models:
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- Task: Semantic Segmentation
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- Task: Semantic Segmentation
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Dataset: DRIVE
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Dataset: DRIVE
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Metrics:
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Metrics:
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mIoU: 78.69
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Dice: 78.69
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Config: configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py
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Config: configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py
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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
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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
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- Name: fcn_unet_s5-d16_128x128_40k_stare
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- Name: fcn_unet_s5-d16_128x128_40k_stare
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@ -69,7 +69,7 @@ Models:
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- Task: Semantic Segmentation
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- Task: Semantic Segmentation
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Dataset: STARE
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Dataset: STARE
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Metrics:
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Metrics:
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mIoU: 81.02
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Dice: 81.02
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Config: configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py
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Config: configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py
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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
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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
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- Name: pspnet_unet_s5-d16_128x128_40k_stare
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- Name: pspnet_unet_s5-d16_128x128_40k_stare
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@ -83,7 +83,7 @@ Models:
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- Task: Semantic Segmentation
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- Task: Semantic Segmentation
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Dataset: STARE
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Dataset: STARE
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Metrics:
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Metrics:
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mIoU: 81.22
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Dice: 81.22
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Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py
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Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py
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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
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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
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- Name: deeplabv3_unet_s5-d16_128x128_40k_stare
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- Name: deeplabv3_unet_s5-d16_128x128_40k_stare
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@ -97,7 +97,7 @@ Models:
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- Task: Semantic Segmentation
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- Task: Semantic Segmentation
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Dataset: STARE
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Dataset: STARE
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Metrics:
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Metrics:
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mIoU: 80.93
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Dice: 80.93
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Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py
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Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py
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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
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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
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- Name: fcn_unet_s5-d16_128x128_40k_chase_db1
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- Name: fcn_unet_s5-d16_128x128_40k_chase_db1
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@ -111,7 +111,7 @@ Models:
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- Task: Semantic Segmentation
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- Task: Semantic Segmentation
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Dataset: CHASE_DB1
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Dataset: CHASE_DB1
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Metrics:
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Metrics:
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mIoU: 80.24
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Dice: 80.24
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Config: configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py
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Config: configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py
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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
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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
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- Name: pspnet_unet_s5-d16_128x128_40k_chase_db1
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- Name: pspnet_unet_s5-d16_128x128_40k_chase_db1
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@ -125,7 +125,7 @@ Models:
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- Task: Semantic Segmentation
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- Task: Semantic Segmentation
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Dataset: CHASE_DB1
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Dataset: CHASE_DB1
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Metrics:
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Metrics:
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mIoU: 80.36
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Dice: 80.36
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Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py
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Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py
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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
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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
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- Name: deeplabv3_unet_s5-d16_128x128_40k_chase_db1
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- Name: deeplabv3_unet_s5-d16_128x128_40k_chase_db1
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@ -139,7 +139,7 @@ Models:
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- Task: Semantic Segmentation
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- Task: Semantic Segmentation
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Dataset: CHASE_DB1
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Dataset: CHASE_DB1
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Metrics:
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Metrics:
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mIoU: 80.47
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Dice: 80.47
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Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py
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Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py
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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
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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
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- Name: fcn_unet_s5-d16_256x256_40k_hrf
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- Name: fcn_unet_s5-d16_256x256_40k_hrf
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@ -153,7 +153,7 @@ Models:
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- Task: Semantic Segmentation
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- Task: Semantic Segmentation
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Dataset: HRF
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Dataset: HRF
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Metrics:
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Metrics:
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mIoU: 79.45
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Dice: 79.45
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Config: configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py
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Config: configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py
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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
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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
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- Name: pspnet_unet_s5-d16_256x256_40k_hrf
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- Name: pspnet_unet_s5-d16_256x256_40k_hrf
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@ -167,7 +167,7 @@ Models:
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- Task: Semantic Segmentation
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- Task: Semantic Segmentation
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Dataset: HRF
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Dataset: HRF
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Metrics:
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Metrics:
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mIoU: 80.07
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Dice: 80.07
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Config: configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py
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Config: configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py
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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
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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
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- Name: deeplabv3_unet_s5-d16_256x256_40k_hrf
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- Name: deeplabv3_unet_s5-d16_256x256_40k_hrf
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@ -181,6 +181,6 @@ Models:
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- Task: Semantic Segmentation
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- Task: Semantic Segmentation
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Dataset: HRF
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Dataset: HRF
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Metrics:
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Metrics:
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mIoU: 80.21
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Dice: 80.21
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Config: configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py
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Config: configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py
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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
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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
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