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STDC/configs/vit/vit.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:
- ADE20K
Name: vit
Models:
- Config: configs/vit/upernet_vit-b16_mln_512x512_80k_ade20k.py
In Collection: vit
Metadata:
backbone: ViT-B + MLN
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 144.09
lr schd: 80000
memory (GB): 9.2
Name: upernet_vit-b16_mln_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 47.71
mIoU(ms+flip): 49.51
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_80k_ade20k/upernet_vit-b16_mln_512x512_80k_ade20k_20210624_130547-0403cee1.pth
- Config: configs/vit/upernet_vit-b16_mln_512x512_160k_ade20k.py
In Collection: vit
Metadata:
backbone: ViT-B + MLN
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 131.93
lr schd: 160000
memory (GB): 9.2
Name: upernet_vit-b16_mln_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 46.75
mIoU(ms+flip): 48.46
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_160k_ade20k/upernet_vit-b16_mln_512x512_160k_ade20k_20210624_130547-852fa768.pth
- Config: configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py
In Collection: vit
Metadata:
backbone: ViT-B + LN + MLN
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 146.63
lr schd: 160000
memory (GB): 9.21
Name: upernet_vit-b16_ln_mln_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 47.73
mIoU(ms+flip): 49.95
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k/upernet_vit-b16_ln_mln_512x512_160k_ade20k_20210621_172828-f444c077.pth
- Config: configs/vit/upernet_deit-s16_512x512_80k_ade20k.py
In Collection: vit
Metadata:
backbone: DeiT-S
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 33.5
lr schd: 80000
memory (GB): 4.68
Name: upernet_deit-s16_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 42.96
mIoU(ms+flip): 43.79
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_80k_ade20k/upernet_deit-s16_512x512_80k_ade20k_20210624_095228-afc93ec2.pth
- Config: configs/vit/upernet_deit-s16_512x512_160k_ade20k.py
In Collection: vit
Metadata:
backbone: DeiT-S
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 34.26
lr schd: 160000
memory (GB): 4.68
Name: upernet_deit-s16_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 42.87
mIoU(ms+flip): 43.79
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_160k_ade20k/upernet_deit-s16_512x512_160k_ade20k_20210621_160903-5110d916.pth
- Config: configs/vit/upernet_deit-s16_mln_512x512_160k_ade20k.py
In Collection: vit
Metadata:
backbone: DeiT-S + MLN
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 89.45
lr schd: 160000
memory (GB): 5.69
Name: upernet_deit-s16_mln_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 43.82
mIoU(ms+flip): 45.07
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_mln_512x512_160k_ade20k/upernet_deit-s16_mln_512x512_160k_ade20k_20210621_161021-fb9a5dfb.pth
- Config: configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py
In Collection: vit
Metadata:
backbone: DeiT-S + LN + MLN
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 80.71
lr schd: 160000
memory (GB): 5.69
Name: upernet_deit-s16_ln_mln_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 43.52
mIoU(ms+flip): 45.01
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k/upernet_deit-s16_ln_mln_512x512_160k_ade20k_20210621_161021-c0cd652f.pth
- Config: configs/vit/upernet_deit-b16_512x512_80k_ade20k.py
In Collection: vit
Metadata:
backbone: DeiT-B
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 103.2
lr schd: 80000
memory (GB): 7.75
Name: upernet_deit-b16_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 45.24
mIoU(ms+flip): 46.73
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_80k_ade20k/upernet_deit-b16_512x512_80k_ade20k_20210624_130529-1e090789.pth
- Config: configs/vit/upernet_deit-b16_512x512_160k_ade20k.py
In Collection: vit
Metadata:
backbone: DeiT-B
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 96.25
lr schd: 160000
memory (GB): 7.75
Name: upernet_deit-b16_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 45.36
mIoU(ms+flip): 47.16
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_160k_ade20k/upernet_deit-b16_512x512_160k_ade20k_20210621_180100-828705d7.pth
- Config: configs/vit/upernet_deit-b16_mln_512x512_160k_ade20k.py
In Collection: vit
Metadata:
backbone: DeiT-B + MLN
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 128.53
lr schd: 160000
memory (GB): 9.21
Name: upernet_deit-b16_mln_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 45.46
mIoU(ms+flip): 47.16
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_mln_512x512_160k_ade20k/upernet_deit-b16_mln_512x512_160k_ade20k_20210621_191949-4e1450f3.pth
- Config: configs/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k.py
In Collection: vit
Metadata:
backbone: DeiT-B + LN + MLN
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 129.03
lr schd: 160000
memory (GB): 9.21
Name: upernet_deit-b16_ln_mln_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 45.37
mIoU(ms+flip): 47.23
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k/upernet_deit-b16_ln_mln_512x512_160k_ade20k_20210623_153535-8a959c14.pth
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