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metafile.yaml
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metafile.yaml
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Models:
- Name: mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024
In Collection: FCN
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 71.19
mIoU(ms+flip): 73.34
Config: configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- M-V2-D8
- FCN
Training Resources: 4x A100 GPUS
Memory (GB): 3.4
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024-20230224_185436-13fef4ea.pth
Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024_20230224_185436.json
Paper:
Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
URL: https://arxiv.org/abs/1801.04381
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14
Framework: PyTorch
- Name: mobilenet-v2-d8_pspnet_4xb2-80k_cityscapes-512x1024
In Collection: PSPNet
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 70.23
Config: configs/mobilenet_v2/mobilenet-v2-d8_pspnet_4xb2-80k_cityscapes-512x1024.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- M-V2-D8
- PSPNet
Training Resources: 4x V100 GPUS
Memory (GB): 3.6
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
Training log: 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.log.json
Paper:
Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
URL: https://arxiv.org/abs/1801.04381
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14
Framework: PyTorch
- Name: mobilenet-v2-d8_deeplabv3_4xb2-80k_cityscapes-512x1024
In Collection: DeepLabV3
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 73.84
Config: configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- M-V2-D8
- DeepLabV3
Training Resources: 4x V100 GPUS
Memory (GB): 3.9
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
Training log: 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.log.json
Paper:
Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
URL: https://arxiv.org/abs/1801.04381
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14
Framework: PyTorch
- Name: mobilenet-v2-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024
In Collection: DeepLabV3+
Results:
Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.2
Config: configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py
Metadata:
Training Data: Cityscapes
Batch Size: 8
Architecture:
- M-V2-D8
- DeepLabV3+
Training Resources: 4x V100 GPUS
Memory (GB): 5.1
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
Training log: 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.log.json
Paper:
Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
URL: https://arxiv.org/abs/1801.04381
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14
Framework: PyTorch
- Name: mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512
In Collection: FCN
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 19.71
Config: configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- M-V2-D8
- FCN
Training Resources: 4x V100 GPUS
Memory (GB): 6.5
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
Training log: 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.log.json
Paper:
Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
URL: https://arxiv.org/abs/1801.04381
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14
Framework: PyTorch
- Name: mobilenet-v2-d8_pspnet_4xb4-160k_ade20k-512x512
In Collection: PSPNet
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 29.68
Config: configs/mobilenet_v2/mobilenet-v2-d8_pspnet_4xb4-160k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- M-V2-D8
- PSPNet
Training Resources: 4x V100 GPUS
Memory (GB): 6.5
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
Training log: 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.log.json
Paper:
Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
URL: https://arxiv.org/abs/1801.04381
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14
Framework: PyTorch
- Name: mobilenet-v2-d8_deeplabv3_4xb4-160k_ade20k-512x512
In Collection: DeepLabV3
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 34.08
Config: configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3_4xb4-160k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- M-V2-D8
- DeepLabV3
Training Resources: 4x V100 GPUS
Memory (GB): 6.8
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
Training log: 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.log.json
Paper:
Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
URL: https://arxiv.org/abs/1801.04381
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14
Framework: PyTorch
- Name: mobilenet-v2-d8_deeplabv3plus_4xb4-160k_ade20k-512x512
In Collection: DeepLabV3+
Results:
Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 34.02
Config: configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py
Metadata:
Training Data: ADE20K
Batch Size: 16
Architecture:
- M-V2-D8
- DeepLabV3+
Training Resources: 4x V100 GPUS
Memory (GB): 8.2
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
Training log: 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.log.json
Paper:
Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
URL: https://arxiv.org/abs/1801.04381
Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14
Framework: PyTorch