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STDC/configs/unet/unet.yml
sennnnn 2aa632ebe7
[Enhancement] Change readme style and Update metafiles. (#895) 
* [Enhancement] Change readme style and prepare for metafiles update.

* Update apcnet github repo url.

* add code snippet.

* split code snippet & official repo.

* update md2yml hook.

* Update metafiles.

* Add converted from attribute.

* process conflict.

* Put defualt variable value.

* update bisenet v2 metafile.

* checkout to ubuntu environment.

* pop empty attribute & make task attribute list.

* update readme style

* update readme style

* update metafiles

Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>
2021-09-28 16:25:37 +08:00

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Collections:
- Name: unet
Metadata:
Training Data:
- DRIVE
- STARE
- CHASE_DB1
- HRF
Paper:
URL: https://arxiv.org/abs/1505.04597
Title: 'U-Net: Convolutional Networks for Biomedical Image Segmentation'
README: configs/unet/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/unet.py#L225
Version: v0.17.0
Converted From:
Code: http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net
Models:
- Name: fcn_unet_s5-d16_64x64_40k_drive
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (64,64)
lr schd: 40000
memory (GB): 0.68
Results:
- Task: Semantic Segmentation
Dataset: DRIVE
Metrics:
mIoU: 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: pspnet_unet_s5-d16_64x64_40k_drive
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (64,64)
lr schd: 40000
memory (GB): 0.599
Results:
- Task: Semantic Segmentation
Dataset: DRIVE
Metrics:
mIoU: 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: deeplabv3_unet_s5-d16_64x64_40k_drive
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (64,64)
lr schd: 40000
memory (GB): 0.596
Results:
- Task: Semantic Segmentation
Dataset: DRIVE
Metrics:
mIoU: 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: fcn_unet_s5-d16_128x128_40k_stare
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.968
Results:
- Task: Semantic Segmentation
Dataset: STARE
Metrics:
mIoU: 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: pspnet_unet_s5-d16_128x128_40k_stare
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.982
Results:
- Task: Semantic Segmentation
Dataset: STARE
Metrics:
mIoU: 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: deeplabv3_unet_s5-d16_128x128_40k_stare
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.999
Results:
- Task: Semantic Segmentation
Dataset: STARE
Metrics:
mIoU: 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: fcn_unet_s5-d16_128x128_40k_chase_db1
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.968
Results:
- Task: Semantic Segmentation
Dataset: CHASE_DB1
Metrics:
mIoU: 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: pspnet_unet_s5-d16_128x128_40k_chase_db1
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.982
Results:
- Task: Semantic Segmentation
Dataset: CHASE_DB1
Metrics:
mIoU: 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: deeplabv3_unet_s5-d16_128x128_40k_chase_db1
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.999
Results:
- Task: Semantic Segmentation
Dataset: CHASE_DB1
Metrics:
mIoU: 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: fcn_unet_s5-d16_256x256_40k_hrf
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (256,256)
lr schd: 40000
memory (GB): 2.525
Results:
- Task: Semantic Segmentation
Dataset: HRF
Metrics:
mIoU: 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: pspnet_unet_s5-d16_256x256_40k_hrf
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (256,256)
lr schd: 40000
memory (GB): 2.588
Results:
- Task: Semantic Segmentation
Dataset: HRF
Metrics:
mIoU: 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: deeplabv3_unet_s5-d16_256x256_40k_hrf
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (256,256)
lr schd: 40000
memory (GB): 2.604
Results:
- Task: Semantic Segmentation
Dataset: HRF
Metrics:
mIoU: 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
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