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[Fix] Fix typo ADE20k to ADE20K in metafile open-mmlab#1120 
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‎configs/mobilenet_v2/README.md‎

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| DeepLabV3 | M-V2-D8 | 512x1024 | 80000 | 3.9 | 8.4 | 73.84 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-bef03590.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes-20200825_124836.log.json) |
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| DeepLabV3+ | M-V2-D8 | 512x1024 | 80000 | 5.1 | 8.4 | 75.20 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-d256dd4b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes-20200825_124836.log.json) |
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### ADE20k
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### ADE20K
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| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
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| ---------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |

‎configs/mobilenet_v2/mobilenet_v2.yml‎

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Metadata:
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Training Data:
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- Cityscapes
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- ADE20k
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- ADE20K
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Paper:
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URL: https://arxiv.org/abs/1801.04381
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Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
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Training Memory (GB): 6.5
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20k
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Dataset: ADE20K
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Metrics:
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mIoU: 19.71
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Config: configs/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k.py
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Training Memory (GB): 6.5
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20k
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Dataset: ADE20K
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Metrics:
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mIoU: 29.68
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Config: configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.py
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Training Memory (GB): 6.8
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20k
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Dataset: ADE20K
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mIoU: 34.08
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Config: configs/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k.py
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Training Memory (GB): 8.2
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20k
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Dataset: ADE20K
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mIoU: 34.02
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Config: configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py

‎configs/resnest/README.md‎

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| DeepLabV3 | S-101-D8 | 512x1024 | 80000 | 11.9 | 1.88 | 79.67 | 80.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes_20200807_144429-b73c4270.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes-20200807_144429.log.json) |
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| DeepLabV3+ | S-101-D8 | 512x1024 | 80000 | 13.2 | 2.36 | 79.62 | 80.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes_20200807_144429-1239eb43.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes-20200807_144429.log.json) |
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### ADE20k
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### ADE20K
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| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
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| ---------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |

‎configs/resnest/resnest.yml‎

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Metadata:
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Training Data:
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- Cityscapes
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- ADE20k
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- ADE20K
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Paper:
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URL: https://arxiv.org/abs/2004.08955
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Title: 'ResNeSt: Split-Attention Networks'
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Training Memory (GB): 14.2
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20k
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Dataset: ADE20K
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Metrics:
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mIoU: 45.62
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mIoU(ms+flip): 46.16
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Training Memory (GB): 14.2
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- Task: Semantic Segmentation
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Dataset: ADE20k
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Dataset: ADE20K
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mIoU: 45.44
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mIoU(ms+flip): 46.28
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Training Memory (GB): 14.6
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- Task: Semantic Segmentation
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Dataset: ADE20k
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Dataset: ADE20K
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mIoU: 45.71
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mIoU(ms+flip): 46.59
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Training Memory (GB): 16.2
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- Task: Semantic Segmentation
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Dataset: ADE20K
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mIoU: 46.47
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mIoU(ms+flip): 47.27

‎configs/segformer/README.md‎

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## Results and models
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### ADE20k
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### ADE20K
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| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
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| ------ | -------- | --------- | ------: | -------: | -------------- | ---: | ------------- | ------ | -------- |

‎configs/segformer/segformer.yml‎

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- Name: segformer
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Training Data:
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- ADE20k
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- ADE20K
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URL: https://arxiv.org/abs/2105.15203
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Title: resize image to multiple of 32, improve SegFormer by 0.5-1.0 mIoU.
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Training Memory (GB): 2.1
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- Task: Semantic Segmentation
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Dataset: ADE20K
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mIoU: 37.41
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mIoU(ms+flip): 38.34
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Training Memory (GB): 2.6
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- Task: Semantic Segmentation
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Dataset: ADE20k
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Dataset: ADE20K
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mIoU: 40.97
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mIoU(ms+flip): 42.54
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Training Memory (GB): 3.6
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- Task: Semantic Segmentation
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Dataset: ADE20K
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mIoU: 45.58
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mIoU(ms+flip): 47.03
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Training Memory (GB): 4.8
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- Task: Semantic Segmentation
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Dataset: ADE20K
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mIoU: 47.82
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mIoU(ms+flip): 48.81
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Training Memory (GB): 6.1
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- Task: Semantic Segmentation
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mIoU: 48.46
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mIoU(ms+flip): 49.76
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Training Memory (GB): 7.2
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Dataset: ADE20K
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mIoU: 49.13
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mIoU(ms+flip): 50.22
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- Task: Semantic Segmentation
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Dataset: ADE20K
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mIoU: 49.62
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mIoU(ms+flip): 50.36

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