Collections: - Name: DMNet Metadata: Training Data: - Cityscapes - ADE20K Models: - Name: dmnet_r50-d8_512x1024_40k_cityscapes In Collection: DMNet Metadata: inference time (ms/im): - value: 273.22 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 77.78 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth Config: configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py - Name: dmnet_r101-d8_512x1024_40k_cityscapes In Collection: DMNet Metadata: inference time (ms/im): - value: 393.7 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 78.37 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth Config: configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py - Name: dmnet_r50-d8_769x769_40k_cityscapes In Collection: DMNet Metadata: inference time (ms/im): - value: 636.94 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 78.49 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth Config: configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py - Name: dmnet_r101-d8_769x769_40k_cityscapes In Collection: DMNet Metadata: inference time (ms/im): - value: 990.1 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 77.62 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth Config: configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py - Name: dmnet_r50-d8_512x1024_80k_cityscapes In Collection: DMNet Metadata: inference time (ms/im): - value: 273.22 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 79.07 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth Config: configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py - Name: dmnet_r101-d8_512x1024_80k_cityscapes In Collection: DMNet Metadata: inference time (ms/im): - value: 393.7 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 79.64 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth Config: configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py - Name: dmnet_r50-d8_769x769_80k_cityscapes In Collection: DMNet Metadata: inference time (ms/im): - value: 636.94 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 79.22 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth Config: configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py - Name: dmnet_r101-d8_769x769_80k_cityscapes In Collection: DMNet Metadata: inference time (ms/im): - value: 990.1 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 79.19 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth Config: configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py - Name: dmnet_r50-d8_512x512_80k_ade20k In Collection: DMNet Metadata: inference time (ms/im): - value: 47.73 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 42.37 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth Config: configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py - Name: dmnet_r101-d8_512x512_80k_ade20k In Collection: DMNet Metadata: inference time (ms/im): - value: 72.05 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 45.34 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth Config: configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py - Name: dmnet_r50-d8_512x512_160k_ade20k In Collection: DMNet Metadata: inference time (ms/im): - value: 47.73 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 43.15 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth Config: configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py - Name: dmnet_r101-d8_512x512_160k_ade20k In Collection: DMNet Metadata: inference time (ms/im): - value: 72.05 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 45.42 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth Config: configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py