mmsegmentation/configs/cgnet/cgnet.yml

51 lines
1.4 KiB
YAML

Collections:
- Metadata:
Training Data:
- Cityscapes
Name: cgnet
Models:
- Config: configs/cgnet/cgnet_680x680_60k_cityscapes.py
In Collection: cgnet
Metadata:
backbone: M3N21
crop size: (680,680)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (680,680)
value: 32.78
lr schd: 60000
memory (GB): 7.5
Name: cgnet_680x680_60k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 65.63
mIoU(ms+flip): 68.04
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes_20201101_110253-4c0b2f2d.pth
- Config: configs/cgnet/cgnet_512x1024_60k_cityscapes.py
In Collection: cgnet
Metadata:
backbone: M3N21
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 32.11
lr schd: 60000
memory (GB): 8.3
Name: cgnet_512x1024_60k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 68.27
mIoU(ms+flip): 70.33
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes_20201101_110254-124ea03b.pth