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mobilenet_v2.yml
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Collections:
- Name: mobilenet_v2
Metadata:
Training Data:
- Cityscapes
- ADE20K
Paper:
URL: https://arxiv.org/abs/1801.04381
Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
README: configs/mobilenet_v2/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14
Version: v0.17.0
Converted From:
Code: https://github.com/tensorflow/models/tree/master/research/deeplab
Models:
- Name: fcn_m-v2-d8_512x1024_80k_cityscapes
In Collection: mobilenet_v2
Metadata:
backbone: M-V2-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 70.42
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 3.4
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 61.54
Config: configs/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes.py
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
- Name: pspnet_m-v2-d8_512x1024_80k_cityscapes
In Collection: mobilenet_v2
Metadata:
backbone: M-V2-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 89.29
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 3.6
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 70.23
Config: configs/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py
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
- Name: deeplabv3_m-v2-d8_512x1024_80k_cityscapes
In Collection: mobilenet_v2
Metadata:
backbone: M-V2-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 119.05
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 3.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 73.84
Config: configs/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py
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
- Name: deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes
In Collection: mobilenet_v2
Metadata:
backbone: M-V2-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 119.05
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 5.1
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.2
Config: configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py
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
- Name: fcn_m-v2-d8_512x512_160k_ade20k
In Collection: mobilenet_v2
Metadata:
backbone: M-V2-D8
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 15.53
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.5
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 19.71
Config: configs/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k.py
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
- Name: pspnet_m-v2-d8_512x512_160k_ade20k
In Collection: mobilenet_v2
Metadata:
backbone: M-V2-D8
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 17.33
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.5
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 29.68
Config: configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.py
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
- Name: deeplabv3_m-v2-d8_512x512_160k_ade20k
In Collection: mobilenet_v2
Metadata:
backbone: M-V2-D8
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 25.06
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.8
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 34.08
Config: configs/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k.py
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
- Name: deeplabv3plus_m-v2-d8_512x512_160k_ade20k
In Collection: mobilenet_v2
Metadata:
backbone: M-V2-D8
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 23.2
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 8.2
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 34.02
Config: configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py
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