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STDC/configs/unet/unet.yml
sennnnn d35fbbdb47
[Enhancement] Add Dev tools to boost develop (#798) 
* Modify default work dir when training.

* Refactor gather_models.py.

* Add train and test matching list.

* Regression benchmark list.

* lower readme name to upper readme name.

* Add url check tool and model inference test tool.

* Modify tool name.

* Support duplicate mode of log json url check.

* Add regression benchmark evaluation automatic tool.

* Add train script generator.

* Only Support script running.

* Add evaluation results gather.

* Add exec Authority.

* Automatically make checkpoint root folder.

* Modify gather results save path.

* Coarse-grained train results gather tool.

* Complete benchmark train script.

* Make some little modifications.

* Fix checkpoint urls.

* Fix unet checkpoint urls.

* Fix fast scnn & fcn checkpoint url.

* Fix fast scnn checkpoint urls.

* Fix fast scnn url.

* Add differential results calculation.

* Add differential results of regression benchmark train results.

* Add an extra argument to select model.

* Update nonlocal_net & hrnet checkpoint url.

* Fix checkpoint url of hrnet and Fix some tta evaluation results and modify gather models tool.

* Modify fast scnn checkpoint url.

* Resolve new comments.

* Fix url check status code bug.

* Resolve some comments.

* Modify train scripts generator.

* Modify work_dir of regression benchmark results.

* model gather tool modification.
2021-09-02 09:44:51 -07:00

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Collections:
- Metadata:
Training Data:
- DRIVE
- STARE
- CHASE_DB1
- HRF
Name: unet
Models:
- Config: configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (64,64)
lr schd: 40000
memory (GB): 0.68
Name: fcn_unet_s5-d16_64x64_40k_drive
Results:
Dataset: DRIVE
Metrics:
mIoU: 78.67
Task: Semantic Segmentation
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
- Config: configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (64,64)
lr schd: 40000
memory (GB): 0.599
Name: pspnet_unet_s5-d16_64x64_40k_drive
Results:
Dataset: DRIVE
Metrics:
mIoU: 78.62
Task: Semantic Segmentation
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
- Config: configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (64,64)
lr schd: 40000
memory (GB): 0.596
Name: deeplabv3_unet_s5-d16_64x64_40k_drive
Results:
Dataset: DRIVE
Metrics:
mIoU: 78.69
Task: Semantic Segmentation
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
- Config: configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.968
Name: fcn_unet_s5-d16_128x128_40k_stare
Results:
Dataset: STARE
Metrics:
mIoU: 81.02
Task: Semantic Segmentation
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
- Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.982
Name: pspnet_unet_s5-d16_128x128_40k_stare
Results:
Dataset: STARE
Metrics:
mIoU: 81.22
Task: Semantic Segmentation
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
- Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.999
Name: deeplabv3_unet_s5-d16_128x128_40k_stare
Results:
Dataset: STARE
Metrics:
mIoU: 80.93
Task: Semantic Segmentation
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
- Config: configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.968
Name: fcn_unet_s5-d16_128x128_40k_chase_db1
Results:
Dataset: CHASE_DB1
Metrics:
mIoU: 80.24
Task: Semantic Segmentation
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
- Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.982
Name: pspnet_unet_s5-d16_128x128_40k_chase_db1
Results:
Dataset: CHASE_DB1
Metrics:
mIoU: 80.36
Task: Semantic Segmentation
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
- Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (128,128)
lr schd: 40000
memory (GB): 0.999
Name: deeplabv3_unet_s5-d16_128x128_40k_chase_db1
Results:
Dataset: CHASE_DB1
Metrics:
mIoU: 80.47
Task: Semantic Segmentation
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
- Config: configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (256,256)
lr schd: 40000
memory (GB): 2.525
Name: fcn_unet_s5-d16_256x256_40k_hrf
Results:
Dataset: HRF
Metrics:
mIoU: 79.45
Task: Semantic Segmentation
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
- Config: configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (256,256)
lr schd: 40000
memory (GB): 2.588
Name: pspnet_unet_s5-d16_256x256_40k_hrf
Results:
Dataset: HRF
Metrics:
mIoU: 80.07
Task: Semantic Segmentation
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
- Config: configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py
In Collection: unet
Metadata:
backbone: UNet-S5-D16
crop size: (256,256)
lr schd: 40000
memory (GB): 2.604
Name: deeplabv3_unet_s5-d16_256x256_40k_hrf
Results:
Dataset: HRF
Metrics:
mIoU: 80.21
Task: Semantic Segmentation
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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