fix ADE20K typo

open-mmlab · Dec 9, 2021 · c061907 · c061907
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Showing 6 changed files with 21 additions and 21 deletions.
diff --git a/configs/mobilenet_v2/README.md b/configs/mobilenet_v2/README.md
@@ -44,7 +44,7 @@ The MobileNetV2 architecture is based on an inverted residual structure where th
 | 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) &#124; [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) |
 | 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) &#124; [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) |
 
-### ADE20k
+### ADE20K
 
 | Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
 | ---------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |

diff --git a/configs/mobilenet_v2/mobilenet_v2.yml b/configs/mobilenet_v2/mobilenet_v2.yml
@@ -3,7 +3,7 @@ Collections:
  Metadata:
  Training Data:
  - Cityscapes
- - ADE20k
+ - ADE20K
  Paper:
  URL: https://arxiv.org/abs/1801.04381
  Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
@@ -114,7 +114,7 @@ Models:
  Training Memory (GB): 6.5
  Results:
  - Task: Semantic Segmentation
- Dataset: ADE20k
+ Dataset: ADE20K
  Metrics:
  mIoU: 19.71
  Config: configs/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k.py
@@ -135,7 +135,7 @@ Models:
  Training Memory (GB): 6.5
  Results:
  - Task: Semantic Segmentation
- Dataset: ADE20k
+ Dataset: ADE20K
  Metrics:
  mIoU: 29.68
  Config: configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.py
@@ -156,7 +156,7 @@ Models:
  Training Memory (GB): 6.8
  Results:
  - Task: Semantic Segmentation
- Dataset: ADE20k
+ Dataset: ADE20K
  Metrics:
  mIoU: 34.08
  Config: configs/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k.py
@@ -177,7 +177,7 @@ Models:
  Training Memory (GB): 8.2
  Results:
  - Task: Semantic Segmentation
- Dataset: ADE20k
+ Dataset: ADE20K
  Metrics:
  mIoU: 34.02
  Config: configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py

diff --git a/configs/resnest/README.md b/configs/resnest/README.md
@@ -42,7 +42,7 @@ year={2020}
 | 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) &#124; [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) |
 | 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) &#124; [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) |
 
-### ADE20k
+### ADE20K
 
 | Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
 | ---------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |

diff --git a/configs/resnest/resnest.yml b/configs/resnest/resnest.yml
@@ -3,7 +3,7 @@ Collections:
  Metadata:
  Training Data:
  - Cityscapes
- - ADE20k
+ - ADE20K
  Paper:
  URL: https://arxiv.org/abs/2004.08955
  Title: 'ResNeSt: Split-Attention Networks'
@@ -118,7 +118,7 @@ Models:
  Training Memory (GB): 14.2
  Results:
  - Task: Semantic Segmentation
- Dataset: ADE20k
+ Dataset: ADE20K
  Metrics:
  mIoU: 45.62
  mIoU(ms+flip): 46.16
@@ -140,7 +140,7 @@ Models:
  Training Memory (GB): 14.2
  Results:
  - Task: Semantic Segmentation
- Dataset: ADE20k
+ Dataset: ADE20K
  Metrics:
  mIoU: 45.44
  mIoU(ms+flip): 46.28
@@ -162,7 +162,7 @@ Models:
  Training Memory (GB): 14.6
  Results:
  - Task: Semantic Segmentation
- Dataset: ADE20k
+ Dataset: ADE20K
  Metrics:
  mIoU: 45.71
  mIoU(ms+flip): 46.59
@@ -184,7 +184,7 @@ Models:
  Training Memory (GB): 16.2
  Results:
  - Task: Semantic Segmentation
- Dataset: ADE20k
+ Dataset: ADE20K
  Metrics:
  mIoU: 46.47
  mIoU(ms+flip): 47.27

diff --git a/configs/segformer/README.md b/configs/segformer/README.md
@@ -45,7 +45,7 @@ This script convert model from `PRETRAIN_PATH` and store the converted model in
 
 ## Results and models
 
-### ADE20k
+### ADE20K
 
 | Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
 | ------ | -------- | --------- | ------: | -------: | -------------- | ---: | ------------- | ------ | -------- |

diff --git a/configs/segformer/segformer.yml b/configs/segformer/segformer.yml
@@ -2,7 +2,7 @@ Collections:
 - Name: segformer
  Metadata:
  Training Data:
- - ADE20k
+ - ADE20K
  Paper:
  URL: https://arxiv.org/abs/2105.15203
  Title: resize image to multiple of 32, improve SegFormer by 0.5-1.0 mIoU.
@@ -29,7 +29,7 @@ Models:
  Training Memory (GB): 2.1
  Results:
  - Task: Semantic Segmentation
- Dataset: ADE20k
+ Dataset: ADE20K
  Metrics:
  mIoU: 37.41
  mIoU(ms+flip): 38.34
@@ -51,7 +51,7 @@ Models:
  Training Memory (GB): 2.6
  Results:
  - Task: Semantic Segmentation
- Dataset: ADE20k
+ Dataset: ADE20K
  Metrics:
  mIoU: 40.97
  mIoU(ms+flip): 42.54
@@ -73,7 +73,7 @@ Models:
  Training Memory (GB): 3.6
  Results:
  - Task: Semantic Segmentation
- Dataset: ADE20k
+ Dataset: ADE20K
  Metrics:
  mIoU: 45.58
  mIoU(ms+flip): 47.03
@@ -95,7 +95,7 @@ Models:
  Training Memory (GB): 4.8
  Results:
  - Task: Semantic Segmentation
- Dataset: ADE20k
+ Dataset: ADE20K
  Metrics:
  mIoU: 47.82
  mIoU(ms+flip): 48.81
@@ -117,7 +117,7 @@ Models:
  Training Memory (GB): 6.1
  Results:
  - Task: Semantic Segmentation
- Dataset: ADE20k
+ Dataset: ADE20K
  Metrics:
  mIoU: 48.46
  mIoU(ms+flip): 49.76
@@ -139,7 +139,7 @@ Models:
  Training Memory (GB): 7.2
  Results:
  - Task: Semantic Segmentation
- Dataset: ADE20k
+ Dataset: ADE20K
  Metrics:
  mIoU: 49.13
  mIoU(ms+flip): 50.22
@@ -161,7 +161,7 @@ Models:
  Training Memory (GB): 11.5
  Results:
  - Task: Semantic Segmentation
- Dataset: ADE20k
+ Dataset: ADE20K
  Metrics:
  mIoU: 49.62
  mIoU(ms+flip): 50.36