4003b8f421
* BiSeNetV2 first commit * BiSeNetV2 unittest * remove pytest * add pytest module * fix ConvModule input name * fix pytest error * fix unittest * refactor * BiSeNetV2 Refactory * fix docstrings and add some small changes * use_sigmoid=False * fix potential bugs about upsampling * Use ConvModule instead * Use ConvModule instead * fix typos * fix typos * fix typos * discard nn.conv2d * discard nn.conv2d * discard nn.conv2d * delete **kwargs * uploading markdown and model * final commit * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * Fix README conflict * Fix unittest problem * Fix unittest problem * BiSeNetV2 * Fixing fps * Fixing typpos * bisenetv2
81 lines
2.7 KiB
YAML
81 lines
2.7 KiB
YAML
Collections:
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- Metadata:
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Training Data:
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- Cityscapes
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Name: bisenetv2
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Models:
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- Config: configs/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py
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In Collection: bisenetv2
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Metadata:
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backbone: BiSeNetV2
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crop size: (1024,1024)
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inference time (ms/im):
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- backend: PyTorch
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batch size: 1
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hardware: V100
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mode: FP32
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resolution: (1024,1024)
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value: 31.48
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lr schd: 160000
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memory (GB): 7.64
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Name: bisenetv2_fcn_4x4_1024x1024_160k_cityscapes
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Results:
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Dataset: Cityscapes
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Metrics:
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mIoU: 73.21
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mIoU(ms+flip): 75.74
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Task: Semantic Segmentation
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes_20210902_015551-bcf10f09.pth
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- Config: configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py
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In Collection: bisenetv2
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Metadata:
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backbone: BiSeNetV2
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crop size: (1024,1024)
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lr schd: 160000
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memory (GB): 7.64
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Name: bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes
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Results:
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Dataset: Cityscapes
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Metrics:
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mIoU: 73.57
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mIoU(ms+flip): 75.8
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Task: Semantic Segmentation
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947-5f8103b4.pth
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- Config: configs/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes.py
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In Collection: bisenetv2
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Metadata:
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backbone: BiSeNetV2
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crop size: (1024,1024)
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lr schd: 160000
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memory (GB): 15.05
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Name: bisenetv2_fcn_4x8_1024x1024_160k_cityscapes
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Results:
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Dataset: Cityscapes
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Metrics:
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mIoU: 75.76
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mIoU(ms+flip): 77.79
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Task: Semantic Segmentation
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes_20210903_000032-e1a2eed6.pth
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- Config: configs/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes.py
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In Collection: bisenetv2
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Metadata:
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backbone: BiSeNetV2
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crop size: (1024,1024)
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inference time (ms/im):
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- backend: PyTorch
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batch size: 1
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hardware: V100
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mode: FP32
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resolution: (1024,1024)
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value: 27.29
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lr schd: 160000
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memory (GB): 5.77
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Name: bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes
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Results:
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Dataset: Cityscapes
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Metrics:
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mIoU: 73.07
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mIoU(ms+flip): 75.13
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Task: Semantic Segmentation
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes_20210902_045942-b979777b.pth
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