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mobilenet_v2.yml
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Collections:
- Metadata:
Training Data:
- Cityscapes
- ADE20k
Name: mobilenet_v2
Models:
- Config: configs/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes.py
In Collection: mobilenet_v2
Metadata:
backbone: M-V2-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 70.42
lr schd: 80000
memory (GB): 3.4
Name: fcn_m-v2-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 61.54
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-d24c28c1.pth
- Config: configs/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py
In Collection: mobilenet_v2
Metadata:
backbone: M-V2-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 89.29
lr schd: 80000
memory (GB): 3.6
Name: pspnet_m-v2-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 70.23
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-19e81d51.pth
- Config: configs/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py
In Collection: mobilenet_v2
Metadata:
backbone: M-V2-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 119.05
lr schd: 80000
memory (GB): 3.9
Name: deeplabv3_m-v2-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 73.84
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-bef03590.pth
- Config: configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py
In Collection: mobilenet_v2
Metadata:
backbone: M-V2-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 119.05
lr schd: 80000
memory (GB): 5.1
Name: deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 75.2
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-d256dd4b.pth
- Config: configs/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k.py
In Collection: mobilenet_v2
Metadata:
backbone: M-V2-D8
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 15.53
lr schd: 160000
memory (GB): 6.5
Name: fcn_m-v2-d8_512x512_160k_ade20k
Results:
Dataset: ADE20k
Metrics:
mIoU: 19.71
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k_20200825_214953-c40e1095.pth
- Config: configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.py
In Collection: mobilenet_v2
Metadata:
backbone: M-V2-D8
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 17.33
lr schd: 160000
memory (GB): 6.5
Name: pspnet_m-v2-d8_512x512_160k_ade20k
Results:
Dataset: ADE20k
Metrics:
mIoU: 29.68
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k_20200825_214953-f5942f7a.pth
- Config: configs/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k.py
In Collection: mobilenet_v2
Metadata:
backbone: M-V2-D8
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 25.06
lr schd: 160000
memory (GB): 6.8
Name: deeplabv3_m-v2-d8_512x512_160k_ade20k
Results:
Dataset: ADE20k
Metrics:
mIoU: 34.08
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k_20200825_223255-63986343.pth
- Config: configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py
In Collection: mobilenet_v2
Metadata:
backbone: M-V2-D8
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 23.2
lr schd: 160000
memory (GB): 8.2
Name: deeplabv3plus_m-v2-d8_512x512_160k_ade20k
Results:
Dataset: ADE20k
Metrics:
mIoU: 34.02
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k_20200825_223255-465a01d4.pth