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metafile.yml
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Collections:
- Name: encnet
Metadata:
Training Data:
- Cityscapes
- Pascal VOC 2012 + Aug
- ADE20K
Models:
- Name: encnet_r50-d8_512x1024_40k_cityscapes
In Collection: encnet
Metadata:
inference time (ms/im): 218.34
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.67
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
Config: configs/encnet/encnet_r50-d8_512x1024_40k_cityscapes.py
- Name: encnet_r101-d8_512x1024_40k_cityscapes
In Collection: encnet
Metadata:
inference time (ms/im): 375.94
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.81
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
Config: configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py
- Name: encnet_r50-d8_769x769_40k_cityscapes
In Collection: encnet
Metadata:
inference time (ms/im): 549.45
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.24
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
Config: configs/encnet/encnet_r50-d8_769x769_40k_cityscapes.py
- Name: encnet_r101-d8_769x769_40k_cityscapes
In Collection: encnet
Metadata:
inference time (ms/im): 793.65
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.25
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
Config: configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py
- Name: encnet_r50-d8_512x1024_80k_cityscapes
In Collection: encnet
Metadata:
inference time (ms/im): 218.34
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.94
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
Config: configs/encnet/encnet_r50-d8_512x1024_80k_cityscapes.py
- Name: encnet_r101-d8_512x1024_80k_cityscapes
In Collection: encnet
Metadata:
inference time (ms/im): 375.94
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.55
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
Config: configs/encnet/encnet_r101-d8_512x1024_80k_cityscapes.py
- Name: encnet_r50-d8_769x769_80k_cityscapes
In Collection: encnet
Metadata:
inference time (ms/im): 549.45
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.44
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
Config: configs/encnet/encnet_r50-d8_769x769_80k_cityscapes.py
- Name: encnet_r101-d8_769x769_80k_cityscapes
In Collection: encnet
Metadata:
inference time (ms/im): 793.65
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.10
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
Config: configs/encnet/encnet_r101-d8_769x769_80k_cityscapes.py
- Name: encnet_r50-d8_512x512_80k_ade20k
In Collection: encnet
Metadata:
inference time (ms/im): 43.84
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 39.53
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
Config: configs/encnet/encnet_r50-d8_512x512_80k_ade20k.py
- Name: encnet_r101-d8_512x512_80k_ade20k
In Collection: encnet
Metadata:
inference time (ms/im): 67.25
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.11
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
Config: configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py
- Name: encnet_r50-d8_512x512_160k_ade20k
In Collection: encnet
Metadata:
inference time (ms/im): 43.84
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 40.10
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
Config: configs/encnet/encnet_r50-d8_512x512_160k_ade20k.py
- Name: encnet_r101-d8_512x512_160k_ade20k
In Collection: encnet
Metadata:
inference time (ms/im): 67.25
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.61
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
Config: configs/encnet/encnet_r101-d8_512x512_160k_ade20k.py