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mmsegmentation/configs/encnet/encnet.yml

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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
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