mirror of
https://github.com/open-mmlab/mmsegmentation.git
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7db1cbb181
* add icnet backbone * add icnet head * add icnet configs * nclass -> num_classes * Support ICNet * ICNet * ICNet * Add ICNeck * Add ICNeck * Add ICNeck * Add ICNeck * Adding unittest * Uploading models & logs * Uploading models & logs * add comment * smaller test_swin.py * try to delete test_swin.py * delete test_unet.py * delete test_unet.py * temp * smaller test_unet.py Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn>
126 lines
4.2 KiB
YAML
126 lines
4.2 KiB
YAML
Collections:
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- Name: bisenetv1
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Metadata:
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Training Data:
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- Cityscapes
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Paper:
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URL: https://arxiv.org/abs/1808.00897
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Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation'
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README: configs/bisenetv1/README.md
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Code:
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URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266
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Version: v0.18.0
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Converted From:
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Code: https://github.com/ycszen/TorchSeg/tree/master/model/bisenet
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Models:
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- Name: bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes
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In Collection: bisenetv1
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Metadata:
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backbone: R-18-D32
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crop size: (1024,1024)
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lr schd: 160000
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inference time (ms/im):
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- value: 31.48
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (1024,1024)
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memory (GB): 5.69
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 74.44
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mIoU(ms+flip): 77.05
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Config: configs/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes_20210922_172239-c55e78e2.pth
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- Name: bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes
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In Collection: bisenetv1
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Metadata:
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backbone: R-18-D32
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crop size: (1024,1024)
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lr schd: 160000
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inference time (ms/im):
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- value: 31.48
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (1024,1024)
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memory (GB): 5.69
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 74.37
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mIoU(ms+flip): 76.91
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Config: configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210905_220251-8ba80eff.pth
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- Name: bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes
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In Collection: bisenetv1
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Metadata:
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backbone: R-18-D32
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crop size: (1024,1024)
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lr schd: 160000
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inference time (ms/im):
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- value: 31.48
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (1024,1024)
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memory (GB): 11.17
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 75.16
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mIoU(ms+flip): 77.24
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Config: configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes_20210905_220322-bb8db75f.pth
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- Name: bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes
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In Collection: bisenetv1
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Metadata:
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backbone: R-50-D32
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crop size: (1024,1024)
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lr schd: 160000
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inference time (ms/im):
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- value: 129.7
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (1024,1024)
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memory (GB): 15.39
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 76.92
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mIoU(ms+flip): 78.87
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Config: configs/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes_20210923_222639-7b28a2a6.pth
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- Name: bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes
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In Collection: bisenetv1
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Metadata:
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backbone: R-50-D32
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crop size: (1024,1024)
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lr schd: 160000
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inference time (ms/im):
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- value: 129.7
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (1024,1024)
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memory (GB): 15.39
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 77.68
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mIoU(ms+flip): 79.57
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Config: configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628-8b304447.pth
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