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+Collections:
+- Name: encnet
+  Metadata:
+    Training Data:
+    - Cityscapes
+    - ADE20K
+  Paper:
+    URL: https://arxiv.org/abs/1803.08904
+    Title: Context Encoding for Semantic Segmentation
+  README: configs/encnet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/enc_head.py#L63
+    Version: v0.17.0
+  Converted From:
+    Code: https://github.com/zhanghang1989/PyTorch-Encoding
+Models:
+- Name: encnet_r50-d8_512x1024_40k_cityscapes
+  In Collection: encnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 218.34
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    Training Memory (GB): 8.6
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.67
+      mIoU(ms+flip): 77.08
+  Config: configs/encnet/encnet_r50-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes_20200621_220958-68638a47.pth
+- Name: encnet_r101-d8_512x1024_40k_cityscapes
+  In Collection: encnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 375.94
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    Training Memory (GB): 12.1
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.81
+      mIoU(ms+flip): 77.21
+  Config: configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes_20200621_220933-35e0a3e8.pth
+- Name: encnet_r50-d8_769x769_40k_cityscapes
+  In Collection: encnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 549.45
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    Training Memory (GB): 9.8
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.24
+      mIoU(ms+flip): 77.85
+  Config: configs/encnet/encnet_r50-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes_20200621_220958-3bcd2884.pth
+- Name: encnet_r101-d8_769x769_40k_cityscapes
+  In Collection: encnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 40000
+    inference time (ms/im):
+    - value: 793.65
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (769,769)
+    Training Memory (GB): 13.7
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.25
+      mIoU(ms+flip): 76.25
+  Config: configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes_20200621_220933-2fafed55.pth
+- Name: encnet_r50-d8_512x1024_80k_cityscapes
+  In Collection: encnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.94
+      mIoU(ms+flip): 79.13
+  Config: configs/encnet/encnet_r50-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes_20200622_003554-fc5c5624.pth
+- Name: encnet_r101-d8_512x1024_80k_cityscapes
+  In Collection: encnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,1024)
+    lr schd: 80000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 78.55
+      mIoU(ms+flip): 79.47
+  Config: configs/encnet/encnet_r101-d8_512x1024_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes_20200622_003555-1de64bec.pth
+- Name: encnet_r50-d8_769x769_80k_cityscapes
+  In Collection: encnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.44
+      mIoU(ms+flip): 78.72
+  Config: configs/encnet/encnet_r50-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes_20200622_003554-55096dcb.pth
+- Name: encnet_r101-d8_769x769_80k_cityscapes
+  In Collection: encnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (769,769)
+    lr schd: 80000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.1
+      mIoU(ms+flip): 76.97
+  Config: configs/encnet/encnet_r101-d8_769x769_80k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes_20200622_003555-470ef79d.pth
+- Name: encnet_r50-d8_512x512_80k_ade20k
+  In Collection: encnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 43.84
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 10.1
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 39.53
+      mIoU(ms+flip): 41.17
+  Config: configs/encnet/encnet_r50-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k_20200622_042412-44b46b04.pth
+- Name: encnet_r101-d8_512x512_80k_ade20k
+  In Collection: encnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 67.25
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 13.6
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.11
+      mIoU(ms+flip): 43.61
+  Config: configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k_20200622_101128-dd35e237.pth
+- Name: encnet_r50-d8_512x512_160k_ade20k
+  In Collection: encnet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 40.1
+      mIoU(ms+flip): 41.71
+  Config: configs/encnet/encnet_r50-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k_20200622_101059-b2db95e0.pth
+- Name: encnet_r101-d8_512x512_160k_ade20k
+  In Collection: encnet
+  Metadata:
+    backbone: R-101-D8
+    crop size: (512,512)
+    lr schd: 160000
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 42.61
+      mIoU(ms+flip): 44.01
+  Config: configs/encnet/encnet_r101-d8_512x512_160k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k_20200622_073348-7989641f.pth