Upload folder using huggingface_hub (part 3)

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr18s_512x512_20k_voc12aug.py

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+_base_ = './fcn_hr18_512x512_20k_voc12aug.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w18_small',
+    backbone=dict(
+        extra=dict(
+            stage1=dict(num_blocks=(2, )),
+            stage2=dict(num_blocks=(2, 2)),
+            stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
+            stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py

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+_base_ = './fcn_hr18_512x512_40k_voc12aug.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w18_small',
+    backbone=dict(
+        extra=dict(
+            stage1=dict(num_blocks=(2, )),
+            stage2=dict(num_blocks=(2, 2)),
+            stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
+            stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr18s_512x512_80k_ade20k.py

@@ -0,0 +1,9 @@
+_base_ = './fcn_hr18_512x512_80k_ade20k.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w18_small',
+    backbone=dict(
+        extra=dict(
+            stage1=dict(num_blocks=(2, )),
+            stage2=dict(num_blocks=(2, 2)),
+            stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
+            stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr18s_512x512_80k_loveda.py

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+_base_ = './fcn_hr18_512x512_80k_loveda.py'
+model = dict(
+    backbone=dict(
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://msra/hrnetv2_w18_small'),
+        extra=dict(
+            stage1=dict(num_blocks=(2, )),
+            stage2=dict(num_blocks=(2, 2)),
+            stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
+            stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr18s_512x512_80k_potsdam.py

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+_base_ = './fcn_hr18_512x512_80k_potsdam.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w18_small',
+    backbone=dict(
+        extra=dict(
+            stage1=dict(num_blocks=(2, )),
+            stage2=dict(num_blocks=(2, 2)),
+            stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
+            stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr48_480x360_80k_egohos_handobj1.py

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+_base_ = [
+    '../_base_/models/fcn_hr18.py', '../_base_/datasets/egohos_handobj1.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+]
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w48',
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(48, 96)),
+            stage3=dict(num_channels=(48, 96, 192)),
+            stage4=dict(num_channels=(48, 96, 192, 384)))),
+    decode_head=dict(
+        num_classes=6, in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))
+
+checkpoint_config = dict(by_epoch=False, interval=5000)
+evaluation = dict(interval=2000, metric=['mIoU', 'mFscore'], pre_eval=True, save_best='mIoU')
+
+
+
+

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr48_480x360_80k_egohos_handobj2.py

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+_base_ = [
+    '../_base_/models/fcn_hr18.py', '../_base_/datasets/egohos_handobj2.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+]
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w48',
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(48, 96)),
+            stage3=dict(num_channels=(48, 96, 192)),
+            stage4=dict(num_channels=(48, 96, 192, 384)))),
+    decode_head=dict(
+        num_classes=9, in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))
+
+checkpoint_config = dict(by_epoch=False, interval=5000)
+evaluation = dict(interval=2000, metric=['mIoU', 'mFscore'], pre_eval=True, save_best='mIoU')
+
+
+
+

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr48_480x480_40k_pascal_context.py

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+_base_ = './fcn_hr18_480x480_40k_pascal_context.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w48',
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(48, 96)),
+            stage3=dict(num_channels=(48, 96, 192)),
+            stage4=dict(num_channels=(48, 96, 192, 384)))),
+    decode_head=dict(
+        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr48_480x480_40k_pascal_context_59.py

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+_base_ = './fcn_hr18_480x480_40k_pascal_context_59.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w48',
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(48, 96)),
+            stage3=dict(num_channels=(48, 96, 192)),
+            stage4=dict(num_channels=(48, 96, 192, 384)))),
+    decode_head=dict(
+        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr48_480x480_80k_pascal_context.py

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+_base_ = './fcn_hr18_480x480_80k_pascal_context.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w48',
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(48, 96)),
+            stage3=dict(num_channels=(48, 96, 192)),
+            stage4=dict(num_channels=(48, 96, 192, 384)))),
+    decode_head=dict(
+        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr48_480x480_80k_pascal_context_59.py

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+_base_ = './fcn_hr18_480x480_80k_pascal_context_59.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w48',
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(48, 96)),
+            stage3=dict(num_channels=(48, 96, 192)),
+            stage4=dict(num_channels=(48, 96, 192, 384)))),
+    decode_head=dict(
+        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen.py

@@ -0,0 +1,10 @@
+_base_ = './fcn_hr18_4x4_512x512_80k_vaihingen.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w48',
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(48, 96)),
+            stage3=dict(num_channels=(48, 96, 192)),
+            stage4=dict(num_channels=(48, 96, 192, 384)))),
+    decode_head=dict(
+        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr48_4x4_896x896_80k_isaid.py

@@ -0,0 +1,10 @@
+_base_ = './fcn_hr18_4x4_896x896_80k_isaid.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w48',
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(48, 96)),
+            stage3=dict(num_channels=(48, 96, 192)),
+            stage4=dict(num_channels=(48, 96, 192, 384)))),
+    decode_head=dict(
+        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.py

@@ -0,0 +1,10 @@
+_base_ = './fcn_hr18_512x1024_160k_cityscapes.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w48',
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(48, 96)),
+            stage3=dict(num_channels=(48, 96, 192)),
+            stage4=dict(num_channels=(48, 96, 192, 384)))),
+    decode_head=dict(
+        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr48_512x1024_40k_cityscapes.py

@@ -0,0 +1,10 @@
+_base_ = './fcn_hr18_512x1024_40k_cityscapes.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w48',
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(48, 96)),
+            stage3=dict(num_channels=(48, 96, 192)),
+            stage4=dict(num_channels=(48, 96, 192, 384)))),
+    decode_head=dict(
+        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr48_512x1024_80k_cityscapes.py

@@ -0,0 +1,10 @@
+_base_ = './fcn_hr18_512x1024_80k_cityscapes.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w48',
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(48, 96)),
+            stage3=dict(num_channels=(48, 96, 192)),
+            stage4=dict(num_channels=(48, 96, 192, 384)))),
+    decode_head=dict(
+        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr48_512x512_160k_ade20k.py

@@ -0,0 +1,10 @@
+_base_ = './fcn_hr18_512x512_160k_ade20k.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w48',
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(48, 96)),
+            stage3=dict(num_channels=(48, 96, 192)),
+            stage4=dict(num_channels=(48, 96, 192, 384)))),
+    decode_head=dict(
+        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr48_512x512_20k_voc12aug.py

@@ -0,0 +1,10 @@
+_base_ = './fcn_hr18_512x512_20k_voc12aug.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w48',
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(48, 96)),
+            stage3=dict(num_channels=(48, 96, 192)),
+            stage4=dict(num_channels=(48, 96, 192, 384)))),
+    decode_head=dict(
+        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr48_512x512_40k_voc12aug.py

@@ -0,0 +1,10 @@
+_base_ = './fcn_hr18_512x512_40k_voc12aug.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w48',
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(48, 96)),
+            stage3=dict(num_channels=(48, 96, 192)),
+            stage4=dict(num_channels=(48, 96, 192, 384)))),
+    decode_head=dict(
+        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr48_512x512_80k_ade20k.py

@@ -0,0 +1,10 @@
+_base_ = './fcn_hr18_512x512_80k_ade20k.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w48',
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(48, 96)),
+            stage3=dict(num_channels=(48, 96, 192)),
+            stage4=dict(num_channels=(48, 96, 192, 384)))),
+    decode_head=dict(
+        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr48_512x512_80k_loveda.py

@@ -0,0 +1,11 @@
+_base_ = './fcn_hr18_512x512_80k_loveda.py'
+model = dict(
+    backbone=dict(
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w48'),
+        extra=dict(
+            stage2=dict(num_channels=(48, 96)),
+            stage3=dict(num_channels=(48, 96, 192)),
+            stage4=dict(num_channels=(48, 96, 192, 384)))),
+    decode_head=dict(
+        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))

EgoHOS/raw/script/mmsegmentation/configs/hrnet/fcn_hr48_512x512_80k_potsdam.py

@@ -0,0 +1,10 @@
+_base_ = './fcn_hr18_512x512_80k_potsdam.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w48',
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(48, 96)),
+            stage3=dict(num_channels=(48, 96, 192)),
+            stage4=dict(num_channels=(48, 96, 192, 384)))),
+    decode_head=dict(
+        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))

EgoHOS/raw/script/mmsegmentation/configs/hrnet/hrnet.yml

@@ -0,0 +1,695 @@
+Models:
+- Name: fcn_hr18s_512x1024_40k_cityscapes
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 42.12
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    Training Memory (GB): 1.7
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.86
+      mIoU(ms+flip): 75.91
+  Config: configs/hrnet/fcn_hr18s_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216-93db27d0.pth
+- Name: fcn_hr18_512x1024_40k_cityscapes
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 77.1
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    Training Memory (GB): 2.9
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.19
+      mIoU(ms+flip): 78.92
+  Config: configs/hrnet/fcn_hr18_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216-f196fb4e.pth
+- Name: fcn_hr48_512x1024_40k_cityscapes
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 155.76
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    Training Memory (GB): 6.2
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.48
+      mIoU(ms+flip): 79.69
+  Config: configs/hrnet/fcn_hr48_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240-a989b146.pth
+- Name: fcn_hr18s_512x1024_80k_cityscapes
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.31
+      mIoU(ms+flip): 77.48
+  Config: configs/hrnet/fcn_hr18s_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth
+- Name: fcn_hr18_512x1024_80k_cityscapes
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.65
+      mIoU(ms+flip): 80.35
+  Config: configs/hrnet/fcn_hr18_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth
+- Name: fcn_hr48_512x1024_80k_cityscapes
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.93
+      mIoU(ms+flip): 80.72
+  Config: configs/hrnet/fcn_hr48_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606-58ea95d6.pth
+- Name: fcn_hr18s_512x1024_160k_cityscapes
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,1024)
+    lr schd: 160000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.31
+      mIoU(ms+flip): 78.31
+  Config: configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901-4a0797ea.pth
+- Name: fcn_hr18_512x1024_160k_cityscapes
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,1024)
+    lr schd: 160000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.8
+      mIoU(ms+flip): 80.74
+  Config: configs/hrnet/fcn_hr18_512x1024_160k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822-221e4a4f.pth
+- Name: fcn_hr48_512x1024_160k_cityscapes
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,1024)
+    lr schd: 160000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.65
+      mIoU(ms+flip): 81.92
+  Config: configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth
+- Name: fcn_hr18s_512x512_80k_ade20k
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 25.87
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 3.8
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 31.38
+      mIoU(ms+flip): 32.45
+  Config: configs/hrnet/fcn_hr18s_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345-77fc814a.pth
+- Name: fcn_hr18_512x512_80k_ade20k
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 44.31
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 4.9
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 36.27
+      mIoU(ms+flip): 37.28
+  Config: configs/hrnet/fcn_hr18_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20210827_114910-6c9382c0.pth
+- Name: fcn_hr48_512x512_80k_ade20k
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 47.1
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 8.2
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.9
+      mIoU(ms+flip): 43.27
+  Config: configs/hrnet/fcn_hr48_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946-7ba5258d.pth
+- Name: fcn_hr18s_512x512_160k_ade20k
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 33.07
+      mIoU(ms+flip): 34.56
+  Config: configs/hrnet/fcn_hr18s_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20210829_174739-f1e7c2e7.pth
+- Name: fcn_hr18_512x512_160k_ade20k
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 36.79
+      mIoU(ms+flip): 38.58
+  Config: configs/hrnet/fcn_hr18_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426-ca961836.pth
+- Name: fcn_hr48_512x512_160k_ade20k
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.02
+      mIoU(ms+flip): 43.86
+  Config: configs/hrnet/fcn_hr48_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407-a52fc02c.pth
+- Name: fcn_hr18s_512x512_20k_voc12aug
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 23.06
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 1.8
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 65.5
+      mIoU(ms+flip): 68.89
+  Config: configs/hrnet/fcn_hr18s_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20210829_174910-0aceadb4.pth
+- Name: fcn_hr18_512x512_20k_voc12aug
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 42.59
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 2.9
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 72.3
+      mIoU(ms+flip): 74.71
+  Config: configs/hrnet/fcn_hr18_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503-488d45f7.pth
+- Name: fcn_hr48_512x512_20k_voc12aug
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 45.35
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 6.2
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 75.87
+      mIoU(ms+flip): 78.58
+  Config: configs/hrnet/fcn_hr48_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419-89de05cd.pth
+- Name: fcn_hr18s_512x512_40k_voc12aug
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 66.61
+      mIoU(ms+flip): 70.0
+  Config: configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648-4f8d6e7f.pth
+- Name: fcn_hr18_512x512_40k_voc12aug
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 72.9
+      mIoU(ms+flip): 75.59
+  Config: configs/hrnet/fcn_hr18_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401-1b4b76cd.pth
+- Name: fcn_hr48_512x512_40k_voc12aug
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,512)
+    lr schd: 40000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.24
+      mIoU(ms+flip): 78.49
+  Config: configs/hrnet/fcn_hr48_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111-1b0f18bc.pth
+- Name: fcn_hr48_480x480_40k_pascal_context
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (480,480)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 112.87
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (480,480)
+    Training Memory (GB): 6.1
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 45.14
+      mIoU(ms+flip): 47.42
+  Config: configs/hrnet/fcn_hr48_480x480_40k_pascal_context.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context_20200911_164852-667d00b0.pth
+- Name: fcn_hr48_480x480_80k_pascal_context
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (480,480)
+    lr schd: 80000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal Context
+    Metrics:
+      mIoU: 45.84
+      mIoU(ms+flip): 47.84
+  Config: configs/hrnet/fcn_hr48_480x480_80k_pascal_context.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context_20200911_155322-847a6711.pth
+- Name: fcn_hr48_480x480_40k_pascal_context_59
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (480,480)
+    lr schd: 40000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 50.33
+      mIoU(ms+flip): 52.83
+  Config: configs/hrnet/fcn_hr48_480x480_40k_pascal_context_59.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59_20210410_122738-b808b8b2.pth
+- Name: fcn_hr48_480x480_80k_pascal_context_59
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (480,480)
+    lr schd: 80000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal Context 59
+    Metrics:
+      mIoU: 51.12
+      mIoU(ms+flip): 53.56
+  Config: configs/hrnet/fcn_hr48_480x480_80k_pascal_context_59.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59_20210411_003240-3ae7081e.pth
+- Name: fcn_hr18s_512x512_80k_loveda
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 40.21
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 1.59
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: LoveDA
+    Metrics:
+      mIoU: 49.28
+      mIoU(ms+flip): 49.42
+  Config: configs/hrnet/fcn_hr18s_512x512_80k_loveda.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_loveda/fcn_hr18s_512x512_80k_loveda_20211210_203228-60a86a7a.pth
+- Name: fcn_hr18_512x512_80k_loveda
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 77.4
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 2.76
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: LoveDA
+    Metrics:
+      mIoU: 50.81
+      mIoU(ms+flip): 50.95
+  Config: configs/hrnet/fcn_hr18_512x512_80k_loveda.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_loveda/fcn_hr18_512x512_80k_loveda_20211210_203952-93d9c3b3.pth
+- Name: fcn_hr48_512x512_80k_loveda
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 104.06
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 6.2
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: LoveDA
+    Metrics:
+      mIoU: 51.42
+      mIoU(ms+flip): 51.64
+  Config: configs/hrnet/fcn_hr48_512x512_80k_loveda.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211211_044756-67072f55.pth
+- Name: fcn_hr18s_512x512_80k_potsdam
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 27.78
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 1.58
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Potsdam
+    Metrics:
+      mIoU: 77.64
+      mIoU(ms+flip): 78.8
+  Config: configs/hrnet/fcn_hr18s_512x512_80k_potsdam.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_potsdam/fcn_hr18s_512x512_80k_potsdam_20211218_205517-ba32af63.pth
+- Name: fcn_hr18_512x512_80k_potsdam
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 51.95
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 2.76
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Potsdam
+    Metrics:
+      mIoU: 78.26
+      mIoU(ms+flip): 79.24
+  Config: configs/hrnet/fcn_hr18_512x512_80k_potsdam.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_potsdam/fcn_hr18_512x512_80k_potsdam_20211218_205517-5d0387ad.pth
+- Name: fcn_hr48_512x512_80k_potsdam
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 60.9
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 6.2
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Potsdam
+    Metrics:
+      mIoU: 78.39
+      mIoU(ms+flip): 79.34
+  Config: configs/hrnet/fcn_hr48_512x512_80k_potsdam.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_potsdam/fcn_hr48_512x512_80k_potsdam_20211219_020601-97434c78.pth
+- Name: fcn_hr18s_4x4_512x512_80k_vaihingen
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 26.24
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 1.58
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Vaihingen
+    Metrics:
+      mIoU: 71.81
+      mIoU(ms+flip): 73.1
+  Config: configs/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_512x512_80k_vaihingen/fcn_hr18s_4x4_512x512_80k_vaihingen_20211231_230909-b23aae02.pth
+- Name: fcn_hr18_4x4_512x512_80k_vaihingen
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 51.15
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 2.76
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Vaihingen
+    Metrics:
+      mIoU: 72.57
+      mIoU(ms+flip): 74.09
+  Config: configs/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_512x512_80k_vaihingen/fcn_hr18_4x4_512x512_80k_vaihingen_20211231_231216-2ec3ae8a.pth
+- Name: fcn_hr48_4x4_512x512_80k_vaihingen
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 57.97
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 6.2
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Vaihingen
+    Metrics:
+      mIoU: 72.5
+      mIoU(ms+flip): 73.52
+  Config: configs/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_512x512_80k_vaihingen/fcn_hr48_4x4_512x512_80k_vaihingen_20211231_231244-7133cb22.pth
+- Name: fcn_hr18s_4x4_896x896_80k_isaid
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18-Small
+    crop size: (896,896)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 72.25
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (896,896)
+    Training Memory (GB): 4.95
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: iSAID
+    Metrics:
+      mIoU: 62.3
+      mIoU(ms+flip): 62.97
+  Config: configs/hrnet/fcn_hr18s_4x4_896x896_80k_isaid.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_4x4_896x896_80k_isaid/fcn_hr18s_4x4_896x896_80k_isaid_20220118_001603-3cc0769b.pth
+- Name: fcn_hr18_4x4_896x896_80k_isaid
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W18
+    crop size: (896,896)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 129.7
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (896,896)
+    Training Memory (GB): 8.3
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: iSAID
+    Metrics:
+      mIoU: 65.06
+      mIoU(ms+flip): 65.6
+  Config: configs/hrnet/fcn_hr18_4x4_896x896_80k_isaid.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_4x4_896x896_80k_isaid/fcn_hr18_4x4_896x896_80k_isaid_20220110_182230-49bf752e.pth
+- Name: fcn_hr48_4x4_896x896_80k_isaid
+  In Collection: FCN
+  Metadata:
+    backbone: HRNetV2p-W48
+    crop size: (896,896)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 136.24
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (896,896)
+    Training Memory (GB): 16.89
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: iSAID
+    Metrics:
+      mIoU: 67.8
+      mIoU(ms+flip): 68.53
+  Config: configs/hrnet/fcn_hr48_4x4_896x896_80k_isaid.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_4x4_896x896_80k_isaid/fcn_hr48_4x4_896x896_80k_isaid_20220114_174643-547fc420.pth

EgoHOS/raw/script/mmsegmentation/configs/icnet/README.md

@@ -0,0 +1,55 @@
+# ICNet
+
+[ICNet for Real-time Semantic Segmentation on High-resolution Images](https://arxiv.org/abs/1704.08545)
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+<a href="https://github.com/hszhao/ICNet">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77">Code Snippet</a>
+
+## Abstract
+
+<!-- [ABSTRACT] -->
+
+We focus on the challenging task of real-time semantic segmentation in this paper. It finds many practical applications and yet is with fundamental difficulty of reducing a large portion of computation for pixel-wise label inference. We propose an image cascade network (ICNet) that incorporates multi-resolution branches under proper label guidance to address this challenge. We provide in-depth analysis of our framework and introduce the cascade feature fusion unit to quickly achieve high-quality segmentation. Our system yields real-time inference on a single GPU card with decent quality results evaluated on challenging datasets like Cityscapes, CamVid and COCO-Stuff.
+
+<!-- [IMAGE] -->
+<div align=center>
+<img src="https://user-images.githubusercontent.com/24582831/142901772-4570455d-7b27-44ae-a690-47dd9fde8445.png" width="70%"/>
+</div>
+
+## Citation
+
+```bibtext
+@inproceedings{zhao2018icnet,
+  title={Icnet for real-time semantic segmentation on high-resolution images},
+  author={Zhao, Hengshuang and Qi, Xiaojuan and Shen, Xiaoyong and Shi, Jianping and Jia, Jiaya},
+  booktitle={Proceedings of the European conference on computer vision (ECCV)},
+  pages={405--420},
+  year={2018}
+}
+```
+
+## Results and models
+
+### Cityscapes
+
+| Method | Backbone   | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                     | download                                                                                                                                                                                                                                                                                                                                       |
+| ------ | ---------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ICNet    | R-18-D8    | 832x832  |   80000 | 1.70      | 27.12          | 68.14 |         70.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/icnet/icnet_r18-d8_832x832_80k_cityscapes.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_80k_cityscapes/icnet_r18-d8_832x832_80k_cityscapes_20210925_225521-2e36638d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_80k_cityscapes/icnet_r18-d8_832x832_80k_cityscapes_20210925_225521.log.json)         |
+| ICNet    | R-18-D8   | 832x832  |   160000 | -     | -           | 71.64 |         74.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/icnet/icnet_r18-d8_832x832_160k_cityscapes.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_160k_cityscapes/icnet_r18-d8_832x832_160k_cityscapes_20210925_230153-2c6eb6e0.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_160k_cityscapes/icnet_r18-d8_832x832_160k_cityscapes_20210925_230153.log.json)     |
+| ICNet (in1k-pre)    | R-18-D8    | 832x832   |   80000 | -      | -           | 72.51 |         74.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes_20210925_230354-1cbe3022.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes_20210925_230354.log.json)             |
+| ICNet (in1k-pre)    | R-18-D8   | 832x832   |   160000 | -     | -           | 74.43 |         76.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes_20210926_052702-619c8ae1.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes_20210926_052702.log.json)         |
+| ICNet    | R-50-D8    | 832x832  |   80000 | 2.53      | 20.08          | 68.91 |         69.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/icnet/icnet_r50-d8_832x832_80k_cityscapes.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_80k_cityscapes/icnet_r50-d8_832x832_80k_cityscapes_20210926_044625-c6407341.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_80k_cityscapes/icnet_r50-d8_832x832_80k_cityscapes_20210926_044625.log.json)         |
+| ICNet    | R-50-D8    | 832x832  |   160000 | -       | -               | 73.82 |         75.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/icnet/icnet_r50-d8_832x832_160k_cityscapes.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_160k_cityscapes/icnet_r50-d8_832x832_160k_cityscapes_20210925_232612-a95f0d4e.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_160k_cityscapes/icnet_r50-d8_832x832_160k_cityscapes_20210925_232612.log.json)         |
+| ICNet (in1k-pre)    | R-50-D8   | 832x832  |   80000 | -        | -              | 74.58 |         76.41 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes_20210926_032943-1743dc7b.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes_20210926_032943.log.json)     |
+| ICNet (in1k-pre)    | R-50-D8    | 832x832   |   160000 | -      | -           | 76.29 |         78.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes_20210926_042715-ce310aea.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes_20210926_042715.log.json)             |
+| ICNet    | R-101-D8    | 832x832  |   80000 | 3.08      | 16.95          | 70.28 |         71.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/icnet/icnet_r101-d8_832x832_80k_cityscapes.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_80k_cityscapes/icnet_r101-d8_832x832_80k_cityscapes_20210926_072447-b52f936e.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_80k_cityscapes/icnet_r101-d8_832x832_80k_cityscapes_20210926_072447.log.json)         |
+| ICNet    | R-101-D8    | 832x832  |   160000 | -        | -               | 73.80 |         76.10 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/icnet/icnet_r101-d8_832x832_160k_cityscapes.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_160k_cityscapes/icnet_r101-d8_832x832_160k_cityscapes_20210926_092350-3a1ebf1a.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_160k_cityscapes/icnet_r101-d8_832x832_160k_cityscapes_20210926_092350.log.json)         |
+| ICNet (in1k-pre)    | R-101-D8   | 832x832  |   80000 | -       | -              | 75.57 |         77.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes.py)      | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes_20210926_020414-7ceb12c5.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes_20210926_020414.log.json)     |
+| ICNet (in1k-pre)    | R-101-D8    | 832x832   |   160000 | -      | -           | 76.15 |         77.98 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes_20210925_232612-9484ae8a.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes_20210925_232612.log.json)             |
+
+Note: `in1k-pre` means pretrained model is used.

EgoHOS/raw/script/mmsegmentation/configs/icnet/icnet.yml

@@ -0,0 +1,207 @@
+Collections:
+- Name: ICNet
+  Metadata:
+    Training Data:
+    - Cityscapes
+  Paper:
+    URL: https://arxiv.org/abs/1704.08545
+    Title: ICNet for Real-time Semantic Segmentation on High-resolution Images
+  README: configs/icnet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/ic_neck.py#L77
+    Version: v0.18.0
+  Converted From:
+    Code: https://github.com/hszhao/ICNet
+Models:
+- Name: icnet_r18-d8_832x832_80k_cityscapes
+  In Collection: ICNet
+  Metadata:
+    backbone: R-18-D8
+    crop size: (832,832)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 36.87
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (832,832)
+    Training Memory (GB): 1.7
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 68.14
+      mIoU(ms+flip): 70.16
+  Config: configs/icnet/icnet_r18-d8_832x832_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_80k_cityscapes/icnet_r18-d8_832x832_80k_cityscapes_20210925_225521-2e36638d.pth
+- Name: icnet_r18-d8_832x832_160k_cityscapes
+  In Collection: ICNet
+  Metadata:
+    backbone: R-18-D8
+    crop size: (832,832)
+    lr schd: 160000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 71.64
+      mIoU(ms+flip): 74.18
+  Config: configs/icnet/icnet_r18-d8_832x832_160k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_832x832_160k_cityscapes/icnet_r18-d8_832x832_160k_cityscapes_20210925_230153-2c6eb6e0.pth
+- Name: icnet_r18-d8_in1k-pre_832x832_80k_cityscapes
+  In Collection: ICNet
+  Metadata:
+    backbone: R-18-D8
+    crop size: (832,832)
+    lr schd: 80000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 72.51
+      mIoU(ms+flip): 74.78
+  Config: configs/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes_20210925_230354-1cbe3022.pth
+- Name: icnet_r18-d8_in1k-pre_832x832_160k_cityscapes
+  In Collection: ICNet
+  Metadata:
+    backbone: R-18-D8
+    crop size: (832,832)
+    lr schd: 160000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.43
+      mIoU(ms+flip): 76.72
+  Config: configs/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes_20210926_052702-619c8ae1.pth
+- Name: icnet_r50-d8_832x832_80k_cityscapes
+  In Collection: ICNet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (832,832)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 49.8
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (832,832)
+    Training Memory (GB): 2.53
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 68.91
+      mIoU(ms+flip): 69.72
+  Config: configs/icnet/icnet_r50-d8_832x832_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_80k_cityscapes/icnet_r50-d8_832x832_80k_cityscapes_20210926_044625-c6407341.pth
+- Name: icnet_r50-d8_832x832_160k_cityscapes
+  In Collection: ICNet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (832,832)
+    lr schd: 160000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.82
+      mIoU(ms+flip): 75.67
+  Config: configs/icnet/icnet_r50-d8_832x832_160k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_832x832_160k_cityscapes/icnet_r50-d8_832x832_160k_cityscapes_20210925_232612-a95f0d4e.pth
+- Name: icnet_r50-d8_in1k-pre_832x832_80k_cityscapes
+  In Collection: ICNet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (832,832)
+    lr schd: 80000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.58
+      mIoU(ms+flip): 76.41
+  Config: configs/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes_20210926_032943-1743dc7b.pth
+- Name: icnet_r50-d8_in1k-pre_832x832_160k_cityscapes
+  In Collection: ICNet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (832,832)
+    lr schd: 160000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.29
+      mIoU(ms+flip): 78.09
+  Config: configs/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes_20210926_042715-ce310aea.pth
+- Name: icnet_r101-d8_832x832_80k_cityscapes
+  In Collection: ICNet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (832,832)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 59.0
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (832,832)
+    Training Memory (GB): 3.08
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 70.28
+      mIoU(ms+flip): 71.95
+  Config: configs/icnet/icnet_r101-d8_832x832_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_80k_cityscapes/icnet_r101-d8_832x832_80k_cityscapes_20210926_072447-b52f936e.pth
+- Name: icnet_r101-d8_832x832_160k_cityscapes
+  In Collection: ICNet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (832,832)
+    lr schd: 160000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.8
+      mIoU(ms+flip): 76.1
+  Config: configs/icnet/icnet_r101-d8_832x832_160k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_832x832_160k_cityscapes/icnet_r101-d8_832x832_160k_cityscapes_20210926_092350-3a1ebf1a.pth
+- Name: icnet_r101-d8_in1k-pre_832x832_80k_cityscapes
+  In Collection: ICNet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (832,832)
+    lr schd: 80000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.57
+      mIoU(ms+flip): 77.86
+  Config: configs/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes_20210926_020414-7ceb12c5.pth
+- Name: icnet_r101-d8_in1k-pre_832x832_160k_cityscapes
+  In Collection: ICNet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (832,832)
+    lr schd: 160000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.15
+      mIoU(ms+flip): 77.98
+  Config: configs/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes_20210925_232612-9484ae8a.pth

EgoHOS/raw/script/mmsegmentation/configs/icnet/icnet_r101-d8_832x832_160k_cityscapes.py

@@ -0,0 +1,2 @@
+_base_ = './icnet_r50-d8_832x832_160k_cityscapes.py'
+model = dict(backbone=dict(backbone_cfg=dict(depth=101)))

EgoHOS/raw/script/mmsegmentation/configs/icnet/icnet_r101-d8_832x832_80k_cityscapes.py

@@ -0,0 +1,2 @@
+_base_ = './icnet_r50-d8_832x832_80k_cityscapes.py'
+model = dict(backbone=dict(backbone_cfg=dict(depth=101)))

EgoHOS/raw/script/mmsegmentation/configs/icnet/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes.py

@@ -0,0 +1,7 @@
+_base_ = './icnet_r50-d8_832x832_160k_cityscapes.py'
+model = dict(
+    backbone=dict(
+        backbone_cfg=dict(
+            depth=101,
+            init_cfg=dict(
+                type='Pretrained', checkpoint='open-mmlab://resnet101_v1c'))))

EgoHOS/raw/script/mmsegmentation/configs/icnet/icnet_r101-d8_in1k-pre_832x832_80k_cityscapes.py

@@ -0,0 +1,7 @@
+_base_ = './icnet_r50-d8_832x832_80k_cityscapes.py'
+model = dict(
+    backbone=dict(
+        backbone_cfg=dict(
+            depth=101,
+            init_cfg=dict(
+                type='Pretrained', checkpoint='open-mmlab://resnet101_v1c'))))

EgoHOS/raw/script/mmsegmentation/configs/icnet/icnet_r18-d8_832x832_160k_cityscapes.py

@@ -0,0 +1,3 @@
+_base_ = './icnet_r50-d8_832x832_160k_cityscapes.py'
+model = dict(
+    backbone=dict(layer_channels=(128, 512), backbone_cfg=dict(depth=18)))

EgoHOS/raw/script/mmsegmentation/configs/icnet/icnet_r18-d8_832x832_80k_cityscapes.py

@@ -0,0 +1,3 @@
+_base_ = './icnet_r50-d8_832x832_80k_cityscapes.py'
+model = dict(
+    backbone=dict(layer_channels=(128, 512), backbone_cfg=dict(depth=18)))

EgoHOS/raw/script/mmsegmentation/configs/icnet/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes.py

@@ -0,0 +1,8 @@
+_base_ = './icnet_r50-d8_832x832_160k_cityscapes.py'
+model = dict(
+    backbone=dict(
+        layer_channels=(128, 512),
+        backbone_cfg=dict(
+            depth=18,
+            init_cfg=dict(
+                type='Pretrained', checkpoint='open-mmlab://resnet18_v1c'))))

EgoHOS/raw/script/mmsegmentation/configs/icnet/icnet_r18-d8_in1k-pre_832x832_80k_cityscapes.py

@@ -0,0 +1,8 @@
+_base_ = './icnet_r50-d8_832x832_80k_cityscapes.py'
+model = dict(
+    backbone=dict(
+        layer_channels=(128, 512),
+        backbone_cfg=dict(
+            depth=18,
+            init_cfg=dict(
+                type='Pretrained', checkpoint='open-mmlab://resnet18_v1c'))))

EgoHOS/raw/script/mmsegmentation/configs/icnet/icnet_r50-d8_832x832_160k_cityscapes.py

@@ -0,0 +1,5 @@
+_base_ = [
+    '../_base_/models/icnet_r50-d8.py',
+    '../_base_/datasets/cityscapes_832x832.py', '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_160k.py'
+]

EgoHOS/raw/script/mmsegmentation/configs/icnet/icnet_r50-d8_832x832_80k_cityscapes.py

@@ -0,0 +1,5 @@
+_base_ = [
+    '../_base_/models/icnet_r50-d8.py',
+    '../_base_/datasets/cityscapes_832x832.py', '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_80k.py'
+]

EgoHOS/raw/script/mmsegmentation/configs/icnet/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes.py

@@ -0,0 +1,6 @@
+_base_ = './icnet_r50-d8_832x832_160k_cityscapes.py'
+model = dict(
+    backbone=dict(
+        backbone_cfg=dict(
+            init_cfg=dict(
+                type='Pretrained', checkpoint='open-mmlab://resnet50_v1c'))))

EgoHOS/raw/script/mmsegmentation/configs/icnet/icnet_r50-d8_in1k-pre_832x832_80k_cityscapes.py

@@ -0,0 +1,6 @@
+_base_ = './icnet_r50-d8_832x832_80k_cityscapes.py'
+model = dict(
+    backbone=dict(
+        backbone_cfg=dict(
+            init_cfg=dict(
+                type='Pretrained', checkpoint='open-mmlab://resnet50_v1c'))))

EgoHOS/raw/script/mmsegmentation/configs/isanet/README.md

@@ -0,0 +1,79 @@
+# ISANet
+
+[Interlaced Sparse Self-Attention for Semantic Segmentation](https://arxiv.org/abs/1907.12273)
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+<a href="https://github.com/openseg-group/openseg.pytorch">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58">Code Snippet</a>
+
+## Abstract
+
+<!-- [ABSTRACT] -->
+
+In this paper, we present a so-called interlaced sparse self-attention approach to improve the efficiency of the \emph{self-attention} mechanism for semantic segmentation. The main idea is that we factorize the dense affinity matrix as the product of two sparse affinity matrices. There are two successive attention modules each estimating a sparse affinity matrix. The first attention module is used to estimate the affinities within a subset of positions that have long spatial interval distances and the second attention module is used to estimate the affinities within a subset of positions that have short spatial interval distances. These two attention modules are designed so that each position is able to receive the information from all the other positions. In contrast to the original self-attention module, our approach decreases the computation and memory complexity substantially especially when processing high-resolution feature maps. We empirically verify the effectiveness of our approach on six challenging semantic segmentation benchmarks.
+
+<!-- [IMAGE] -->
+<div align=center>
+<img src="https://user-images.githubusercontent.com/24582831/142901868-03d80da4-b9c0-4df9-8509-5f684ba9dadc.png" width="80%"/>
+</div>
+
+## Citation
+
+```bibetex
+@article{huang2019isa,
+  title={Interlaced Sparse Self-Attention for Semantic Segmentation},
+  author={Huang, Lang and Yuan, Yuhui and Guo, Jianyuan and Zhang, Chao and Chen, Xilin and Wang, Jingdong},
+  journal={arXiv preprint arXiv:1907.12273},
+  year={2019}
+}
+```
+
+The technical report above is also presented at:
+
+```bibetex
+@article{yuan2021ocnet,
+  title={OCNet: Object Context for Semantic Segmentation},
+  author={Yuan, Yuhui and Huang, Lang and Guo, Jianyuan and Zhang, Chao and Chen, Xilin and Wang, Jingdong},
+  journal={International Journal of Computer Vision},
+  pages={1--24},
+  year={2021},
+  publisher={Springer}
+}
+```
+
+## Results and models
+
+### Cityscapes
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config |download |
+| --------|----------|-----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| ISANet | R-50-D8  | 512x1024  |  40000  |  5.869   |  2.91   | 78.49 |   79.44   |[config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/isanet/isanet_r50-d8_512x1024_40k_cityscapes.py)|[model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_40k_cityscapes/isanet_r50-d8_512x1024_40k_cityscapes_20210901_054739-981bd763.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_40k_cityscapes/isanet_r50-d8_512x1024_40k_cityscapes_20210901_054739.log.json)                                         |
+| ISANet | R-50-D8  | 512x1024  |  80000  |  5.869  |  2.91          | 78.68 |   80.25   |[config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/isanet/isanet_r50-d8_512x1024_80k_cityscapes.py)|[model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_80k_cityscapes/isanet_r50-d8_512x1024_80k_cityscapes_20210901_074202-89384497.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_80k_cityscapes/isanet_r50-d8_512x1024_80k_cityscapes_20210901_074202.log.json)                                         |
+| ISANet | R-50-D8  | 769x769   |  40000  |  6.759   |  1.54          | 78.70 |   80.28      |[config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/isanet/isanet_r50-d8_769x769_40k_cityscapes.py)|[model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_40k_cityscapes/isanet_r50-d8_769x769_40k_cityscapes_20210903_050200-4ae7e65b.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_40k_cityscapes/isanet_r50-d8_769x769_40k_cityscapes_20210903_050200.log.json)                                         |
+| ISANet | R-50-D8  | 769x769   |  80000  |  6.759   |  1.54          | 79.29 |   80.53    |[config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/isanet/isanet_r50-d8_769x769_80k_cityscapes.py)|[model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_80k_cityscapes/isanet_r50-d8_769x769_80k_cityscapes_20210903_101126-99b54519.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_80k_cityscapes/isanet_r50-d8_769x769_80k_cityscapes_20210903_101126.log.json)                                         |
+| ISANet | R-101-D8 | 512x1024  |  40000  |  9.425   |  2.35         | 79.58 |   81.05  |[config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/isanet/isanet_r101-d8_512x1024_40k_cityscapes.py)|[model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_40k_cityscapes/isanet_r101-d8_512x1024_40k_cityscapes_20210901_145553-293e6bd6.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_40k_cityscapes/isanet_r101-d8_512x1024_40k_cityscapes_20210901_145553.log.json)                                         |
+| ISANet | R-101-D8 | 512x1024  |  80000  |  9.425   |  2.35          | 80.32 |  81.58  |[config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/isanet/isanet_r101-d8_512x1024_80k_cityscapes.py)|[model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_80k_cityscapes/isanet_r101-d8_512x1024_80k_cityscapes_20210901_145243-5b99c9b2.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_80k_cityscapes/isanet_r101-d8_512x1024_80k_cityscapes_20210901_145243.log.json)                                         |
+| ISANet | R-101-D8 | 769x769   |  40000  |  10.815  |  0.92         | 79.68 |    80.95     |[config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/isanet/isanet_r101-d8_769x769_40k_cityscapes.py)|[model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_40k_cityscapes/isanet_r101-d8_769x769_40k_cityscapes_20210903_111320-509e7224.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_40k_cityscapes/isanet_r101-d8_769x769_40k_cityscapes_20210903_111320.log.json)                                         |
+| ISANet | R-101-D8 | 769x769   |  80000  |  10.815  |  0.92          | 80.61 |   81.59  |[config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/isanet/isanet_r101-d8_769x769_80k_cityscapes.py)|[model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_80k_cityscapes/isanet_r101-d8_769x769_80k_cityscapes_20210903_111319-24f71dfa.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_80k_cityscapes/isanet_r101-d8_769x769_80k_cityscapes_20210903_111319.log.json)                                         |
+
+### ADE20K
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config |download |
+| --------|----------|-----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| ISANet | R-50-D8  | 512x512  |  80000  |  9.0    |    22.55      | 41.12 |   42.35     |[config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/isanet/isanet_r50-d8_512x512_80k_ade20k.py)|[model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_80k_ade20k/isanet_r50-d8_512x512_80k_ade20k_20210903_124557-6ed83a0c.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_80k_ade20k/isanet_r50-d8_512x512_80k_ade20k_20210903_124557.log.json)|
+| ISANet | R-50-D8  | 512x512  |  160000 |  9.0     |    22.55        | 42.59 |  43.07      |[config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/isanet/isanet_r50-d8_512x512_160k_ade20k.py)|[model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_160k_ade20k/isanet_r50-d8_512x512_160k_ade20k_20210903_104850-f752d0a3.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_160k_ade20k/isanet_r50-d8_512x512_160k_ade20k_20210903_104850.log.json)|
+| ISANet | R-101-D8 | 512x512  |  80000  |  12.562   |    10.56       | 43.51 |  44.38        |[config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/isanet/isanet_r101-d8_512x512_80k_ade20k.py)|[model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_80k_ade20k/isanet_r101-d8_512x512_80k_ade20k_20210903_162056-68b235c2.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_80k_ade20k/isanet_r101-d8_512x512_80k_ade20k_20210903_162056.log.json)|
+| ISANet | R-101-D8 | 512x512  |  160000 |  12.562  |    10.56       | 43.80 |   45.4       |[config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/isanet/isanet_r101-d8_512x512_160k_ade20k.py)|[model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_160k_ade20k/isanet_r101-d8_512x512_160k_ade20k_20210903_211431-a7879dcd.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_160k_ade20k/isanet_r101-d8_512x512_160k_ade20k_20210903_211431.log.json)|
+
+### Pascal VOC 2012 + Aug
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config |download |
+| --------|----------|-----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| ISANet | R-50-D8  | 512x512  |  20000  |   5.9  |   23.08       | 76.78 |   77.79    |[config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/isanet/isanet_r50-d8_512x512_20k_voc12aug.py)|[model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_20k_voc12aug/isanet_r50-d8_512x512_20k_voc12aug_20210901_164838-79d59b80.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_20k_voc12aug/isanet_r50-d8_512x512_20k_voc12aug_20210901_164838.log.json)|
+| ISANet | R-50-D8  | 512x512  |  40000  |   5.9  |   23.08        | 76.20 |    77.22       |[config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/isanet/isanet_r50-d8_512x512_40k_voc12aug.py)|[model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_40k_voc12aug/isanet_r50-d8_512x512_40k_voc12aug_20210901_151349-7d08a54e.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_40k_voc12aug/isanet_r50-d8_512x512_40k_voc12aug_20210901_151349.log.json)|
+| ISANet | R-101-D8 | 512x512  |  20000  |   9.465  |   7.42        | 78.46 |     79.16    |[config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/isanet/isanet_r101-d8_512x512_20k_voc12aug.py)|[model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_20k_voc12aug/isanet_r101-d8_512x512_20k_voc12aug_20210901_115805-3ccbf355.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_20k_voc12aug/isanet_r101-d8_512x512_20k_voc12aug_20210901_115805.log.json)|
+| ISANet | R-101-D8 | 512x512  |  40000  |   9.465  |   7.42         | 78.12 |     79.04    |[config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/isanet/isanet_r101-d8_512x512_40k_voc12aug.py)|[model](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_40k_voc12aug/isanet_r101-d8_512x512_40k_voc12aug_20210901_145814-bc71233b.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_40k_voc12aug/isanet_r101-d8_512x512_40k_voc12aug_20210901_145814.log.json)|

EgoHOS/raw/script/mmsegmentation/configs/isanet/isanet.yml

@@ -0,0 +1,369 @@
+Collections:
+- Name: ISANet
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+    - Pascal VOC 2012 + Aug
+  Paper:
+    URL: https://arxiv.org/abs/1907.12273
+    Title: Interlaced Sparse Self-Attention for Semantic Segmentation
+  README: configs/isanet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58
+    Version: v0.18.0
+  Converted From:
+    Code: https://github.com/openseg-group/openseg.pytorch
+Models:
+- Name: isanet_r50-d8_512x1024_40k_cityscapes
+  In Collection: ISANet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 343.64
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    Training Memory (GB): 5.869
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.49
+      mIoU(ms+flip): 79.44
+  Config: configs/isanet/isanet_r50-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_40k_cityscapes/isanet_r50-d8_512x1024_40k_cityscapes_20210901_054739-981bd763.pth
+- Name: isanet_r50-d8_512x1024_80k_cityscapes
+  In Collection: ISANet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 343.64
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    Training Memory (GB): 5.869
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.68
+      mIoU(ms+flip): 80.25
+  Config: configs/isanet/isanet_r50-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_80k_cityscapes/isanet_r50-d8_512x1024_80k_cityscapes_20210901_074202-89384497.pth
+- Name: isanet_r50-d8_769x769_40k_cityscapes
+  In Collection: ISANet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 649.35
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    Training Memory (GB): 6.759
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.7
+      mIoU(ms+flip): 80.28
+  Config: configs/isanet/isanet_r50-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_40k_cityscapes/isanet_r50-d8_769x769_40k_cityscapes_20210903_050200-4ae7e65b.pth
+- Name: isanet_r50-d8_769x769_80k_cityscapes
+  In Collection: ISANet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 649.35
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    Training Memory (GB): 6.759
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.29
+      mIoU(ms+flip): 80.53
+  Config: configs/isanet/isanet_r50-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_80k_cityscapes/isanet_r50-d8_769x769_80k_cityscapes_20210903_101126-99b54519.pth
+- Name: isanet_r101-d8_512x1024_40k_cityscapes
+  In Collection: ISANet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 425.53
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    Training Memory (GB): 9.425
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.58
+      mIoU(ms+flip): 81.05
+  Config: configs/isanet/isanet_r101-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_40k_cityscapes/isanet_r101-d8_512x1024_40k_cityscapes_20210901_145553-293e6bd6.pth
+- Name: isanet_r101-d8_512x1024_80k_cityscapes
+  In Collection: ISANet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 425.53
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    Training Memory (GB): 9.425
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.32
+      mIoU(ms+flip): 81.58
+  Config: configs/isanet/isanet_r101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_80k_cityscapes/isanet_r101-d8_512x1024_80k_cityscapes_20210901_145243-5b99c9b2.pth
+- Name: isanet_r101-d8_769x769_40k_cityscapes
+  In Collection: ISANet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 1086.96
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    Training Memory (GB): 10.815
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 79.68
+      mIoU(ms+flip): 80.95
+  Config: configs/isanet/isanet_r101-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_40k_cityscapes/isanet_r101-d8_769x769_40k_cityscapes_20210903_111320-509e7224.pth
+- Name: isanet_r101-d8_769x769_80k_cityscapes
+  In Collection: ISANet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 1086.96
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    Training Memory (GB): 10.815
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 80.61
+      mIoU(ms+flip): 81.59
+  Config: configs/isanet/isanet_r101-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_80k_cityscapes/isanet_r101-d8_769x769_80k_cityscapes_20210903_111319-24f71dfa.pth
+- Name: isanet_r50-d8_512x512_80k_ade20k
+  In Collection: ISANet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 44.35
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 9.0
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.12
+      mIoU(ms+flip): 42.35
+  Config: configs/isanet/isanet_r50-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_80k_ade20k/isanet_r50-d8_512x512_80k_ade20k_20210903_124557-6ed83a0c.pth
+- Name: isanet_r50-d8_512x512_160k_ade20k
+  In Collection: ISANet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 44.35
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 9.0
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.59
+      mIoU(ms+flip): 43.07
+  Config: configs/isanet/isanet_r50-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_160k_ade20k/isanet_r50-d8_512x512_160k_ade20k_20210903_104850-f752d0a3.pth
+- Name: isanet_r101-d8_512x512_80k_ade20k
+  In Collection: ISANet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 94.7
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 12.562
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.51
+      mIoU(ms+flip): 44.38
+  Config: configs/isanet/isanet_r101-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_80k_ade20k/isanet_r101-d8_512x512_80k_ade20k_20210903_162056-68b235c2.pth
+- Name: isanet_r101-d8_512x512_160k_ade20k
+  In Collection: ISANet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 94.7
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 12.562
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.8
+      mIoU(ms+flip): 45.4
+  Config: configs/isanet/isanet_r101-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_160k_ade20k/isanet_r101-d8_512x512_160k_ade20k_20210903_211431-a7879dcd.pth
+- Name: isanet_r50-d8_512x512_20k_voc12aug
+  In Collection: ISANet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 43.33
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 5.9
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.78
+      mIoU(ms+flip): 77.79
+  Config: configs/isanet/isanet_r50-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_20k_voc12aug/isanet_r50-d8_512x512_20k_voc12aug_20210901_164838-79d59b80.pth
+- Name: isanet_r50-d8_512x512_40k_voc12aug
+  In Collection: ISANet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 43.33
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 5.9
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 76.2
+      mIoU(ms+flip): 77.22
+  Config: configs/isanet/isanet_r50-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_40k_voc12aug/isanet_r50-d8_512x512_40k_voc12aug_20210901_151349-7d08a54e.pth
+- Name: isanet_r101-d8_512x512_20k_voc12aug
+  In Collection: ISANet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 20000
+    inference time (ms/im):
+    - value: 134.77
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 9.465
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 78.46
+      mIoU(ms+flip): 79.16
+  Config: configs/isanet/isanet_r101-d8_512x512_20k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_20k_voc12aug/isanet_r101-d8_512x512_20k_voc12aug_20210901_115805-3ccbf355.pth
+- Name: isanet_r101-d8_512x512_40k_voc12aug
+  In Collection: ISANet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 134.77
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 9.465
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Pascal VOC 2012 + Aug
+    Metrics:
+      mIoU: 78.12
+      mIoU(ms+flip): 79.04
+  Config: configs/isanet/isanet_r101-d8_512x512_40k_voc12aug.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_40k_voc12aug/isanet_r101-d8_512x512_40k_voc12aug_20210901_145814-bc71233b.pth

EgoHOS/raw/script/mmsegmentation/configs/isanet/isanet_r101-d8_512x1024_40k_cityscapes.py

@@ -0,0 +1,2 @@
+_base_ = './isanet_r50-d8_512x1024_40k_cityscapes.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/isanet/isanet_r101-d8_512x1024_80k_cityscapes.py

@@ -0,0 +1,2 @@
+_base_ = './isanet_r50-d8_512x1024_80k_cityscapes.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/isanet/isanet_r101-d8_512x512_160k_ade20k.py

@@ -0,0 +1,2 @@
+_base_ = './isanet_r50-d8_512x512_160k_ade20k.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/isanet/isanet_r101-d8_512x512_20k_voc12aug.py

@@ -0,0 +1,2 @@
+_base_ = './isanet_r50-d8_512x512_20k_voc12aug.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/isanet/isanet_r101-d8_512x512_40k_voc12aug.py

@@ -0,0 +1,2 @@
+_base_ = './isanet_r50-d8_512x512_40k_voc12aug.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/isanet/isanet_r101-d8_512x512_80k_ade20k.py

@@ -0,0 +1,2 @@
+_base_ = './isanet_r50-d8_512x512_80k_ade20k.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/isanet/isanet_r101-d8_769x769_40k_cityscapes.py

@@ -0,0 +1,2 @@
+_base_ = './isanet_r50-d8_769x769_40k_cityscapes.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/isanet/isanet_r101-d8_769x769_80k_cityscapes.py

@@ -0,0 +1,2 @@
+_base_ = './isanet_r50-d8_769x769_80k_cityscapes.py'
+model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

EgoHOS/raw/script/mmsegmentation/configs/isanet/isanet_r50-d8_512x1024_40k_cityscapes.py

@@ -0,0 +1,4 @@
+_base_ = [
+    '../_base_/models/isanet_r50-d8.py', '../_base_/datasets/cityscapes.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
+]

EgoHOS/raw/script/mmsegmentation/configs/isanet/isanet_r50-d8_512x1024_80k_cityscapes.py

@@ -0,0 +1,4 @@
+_base_ = [
+    '../_base_/models/isanet_r50-d8.py', '../_base_/datasets/cityscapes.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+]

EgoHOS/raw/script/mmsegmentation/configs/isanet/isanet_r50-d8_512x512_160k_ade20k.py

@@ -0,0 +1,6 @@
+_base_ = [
+    '../_base_/models/isanet_r50-d8.py', '../_base_/datasets/ade20k.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
+]
+model = dict(
+    decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))