mmsegmentation/configs/fp16/fp16.yml

91 lines
2.7 KiB
YAML

Collections:
- Metadata:
Training Data:
- Cityscapes
Name: fp16
Models:
- Config: configs/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes.py
In Collection: fp16
Metadata:
backbone: R-101-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 115.74
lr schd: 80000
memory (GB): 5.37
Name: fcn_r101-d8_512x1024_80k_fp16_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 76.8
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes/fcn_r101-d8_512x1024_80k_fp16_cityscapes-50245227.pth
- Config: configs/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes.py
In Collection: fp16
Metadata:
backbone: R-101-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 114.03
lr schd: 80000
memory (GB): 5.34
Name: pspnet_r101-d8_512x1024_80k_fp16_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.46
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes/pspnet_r101-d8_512x1024_80k_fp16_cityscapes-ade37931.pth
- Config: configs/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes.py
In Collection: fp16
Metadata:
backbone: R-101-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 259.07
lr schd: 80000
memory (GB): 5.75
Name: deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 80.48
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes-bc86dc84.pth
- Config: configs/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes.py
In Collection: fp16
Metadata:
backbone: R-101-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 127.06
lr schd: 80000
memory (GB): 6.35
Name: deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 80.46
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes-cc58bc8d.pth