mirrors
/
mmsegmentation
mirror of https://github.com/open-mmlab/mmsegmentation.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity
mmsegmentation/configs/ccnet/metafile.yml

312 lines
9.3 KiB
YAML
Raw Blame History

Collections:
- Name: CCNet
Metadata:
Training Data:
- Cityscapes
- Pascal VOC 2012 + Aug
- ADE20K
Models:
- Name: ccnet_r50-d8_512x1024_40k_cityscapes
In Collection: CCNet
Metadata:
inference time (ms/im):
- value: 301.2
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.76
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517-4123f401.pth
Config: configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py
- Name: ccnet_r101-d8_512x1024_40k_cityscapes
In Collection: CCNet
Metadata:
inference time (ms/im):
- value: 432.9
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.35
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540-a3b84ba6.pth
Config: configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py
- Name: ccnet_r50-d8_769x769_40k_cityscapes
In Collection: CCNet
Metadata:
inference time (ms/im):
- value: 699.3
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.46
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125-76d11884.pth
Config: configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py
- Name: ccnet_r101-d8_769x769_40k_cityscapes
In Collection: CCNet
Metadata:
inference time (ms/im):
- value: 990.1
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.94
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428-4f57c8d0.pth
Config: configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py
- Name: ccnet_r50-d8_512x1024_80k_cityscapes
In Collection: CCNet
Metadata:
inference time (ms/im):
- value: 301.2
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.03
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421-869a3423.pth
Config: configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py
- Name: ccnet_r101-d8_512x1024_80k_cityscapes
In Collection: CCNet
Metadata:
inference time (ms/im):
- value: 432.9
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.87
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935-ffae8917.pth
Config: configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py
- Name: ccnet_r50-d8_769x769_80k_cityscapes
In Collection: CCNet
Metadata:
inference time (ms/im):
- value: 699.3
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.29
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421-73eed8ca.pth
Config: configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py
- Name: ccnet_r101-d8_769x769_80k_cityscapes
In Collection: CCNet
Metadata:
inference time (ms/im):
- value: 990.1
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.45
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502-ad3cd481.pth
Config: configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py
- Name: ccnet_r50-d8_512x512_80k_ade20k
In Collection: CCNet
Metadata:
inference time (ms/im):
- value: 47.87
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.78
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848-aa37f61e.pth
Config: configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py
- Name: ccnet_r101-d8_512x512_80k_ade20k
In Collection: CCNet
Metadata:
inference time (ms/im):
- value: 70.87
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.97
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848-1f4929a3.pth
Config: configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py
- Name: ccnet_r50-d8_512x512_160k_ade20k
In Collection: CCNet
Metadata:
inference time (ms/im):
- value: 47.87
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.08
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435-7c97193b.pth
Config: configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py
- Name: ccnet_r101-d8_512x512_160k_ade20k
In Collection: CCNet
Metadata:
inference time (ms/im):
- value: 70.87
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.71
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644-e849e007.pth
Config: configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py
- Name: ccnet_r50-d8_512x512_20k_voc12aug
In Collection: CCNet
Metadata:
inference time (ms/im):
- value: 48.9
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.17
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212-fad81784.pth
Config: configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py
- Name: ccnet_r101-d8_512x512_20k_voc12aug
In Collection: CCNet
Metadata:
inference time (ms/im):
- value: 73.31
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.27
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212-0007b61d.pth
Config: configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py
- Name: ccnet_r50-d8_512x512_40k_voc12aug
In Collection: CCNet
Metadata:
inference time (ms/im):
- value: 48.9
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 75.96
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127-c2a15f02.pth
Config: configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py
- Name: ccnet_r101-d8_512x512_40k_voc12aug
In Collection: CCNet
Metadata:
inference time (ms/im):
- value: 73.31
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.87
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127-c30da577.pth
Config: configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py
Reference in New Issue View Git Blame Copy Permalink