Collections: - Name: dmnet Metadata: Training Data: - Cityscapes - ADE20K Models: - Name: dmnet_r50-d8_512x1024_40k_cityscapes In Collection: dmnet Metadata: backbone: R-50-D8 crop size: (512,1024) lr schd: 40000 inference time (ms/im): - value: 273.22 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,1024) memory (GB): 7.0 Results: Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 77.78 mIoU(ms+flip): 79.14 Config: configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes_20201215_042326-615373cf.pth - Name: dmnet_r101-d8_512x1024_40k_cityscapes In Collection: dmnet Metadata: backbone: R-101-D8 crop size: (512,1024) lr schd: 40000 inference time (ms/im): - value: 393.7 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,1024) memory (GB): 10.6 Results: Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 78.37 mIoU(ms+flip): 79.72 Config: configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes_20201215_043100-8291e976.pth - Name: dmnet_r50-d8_769x769_40k_cityscapes In Collection: dmnet Metadata: backbone: R-50-D8 crop size: (769,769) lr schd: 40000 inference time (ms/im): - value: 636.94 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (769,769) memory (GB): 7.9 Results: Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 78.49 mIoU(ms+flip): 80.27 Config: configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes_20201215_093706-e7f0e23e.pth - Name: dmnet_r101-d8_769x769_40k_cityscapes In Collection: dmnet Metadata: backbone: R-101-D8 crop size: (769,769) lr schd: 40000 inference time (ms/im): - value: 990.1 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (769,769) memory (GB): 12.0 Results: Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 77.62 mIoU(ms+flip): 78.94 Config: configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes_20201215_081348-a74261f6.pth - Name: dmnet_r50-d8_512x1024_80k_cityscapes In Collection: dmnet Metadata: backbone: R-50-D8 crop size: (512,1024) lr schd: 80000 Results: Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 79.07 mIoU(ms+flip): 80.22 Config: configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes_20201215_053728-3c8893b9.pth - Name: dmnet_r101-d8_512x1024_80k_cityscapes In Collection: dmnet Metadata: backbone: R-101-D8 crop size: (512,1024) lr schd: 80000 Results: Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 79.64 mIoU(ms+flip): 80.67 Config: configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes_20201215_031718-fa081cb8.pth - Name: dmnet_r50-d8_769x769_80k_cityscapes In Collection: dmnet Metadata: backbone: R-50-D8 crop size: (769,769) lr schd: 80000 Results: Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 79.22 mIoU(ms+flip): 80.55 Config: configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes_20201215_034006-6060840e.pth - Name: dmnet_r101-d8_769x769_80k_cityscapes In Collection: dmnet Metadata: backbone: R-101-D8 crop size: (769,769) lr schd: 80000 Results: Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 79.19 mIoU(ms+flip): 80.65 Config: configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes_20201215_082810-7f0de59a.pth - Name: dmnet_r50-d8_512x512_80k_ade20k In Collection: dmnet Metadata: backbone: R-50-D8 crop size: (512,512) lr schd: 80000 inference time (ms/im): - value: 47.73 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,512) memory (GB): 9.4 Results: Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 42.37 mIoU(ms+flip): 43.62 Config: configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k_20201215_144744-f89092a6.pth - Name: dmnet_r101-d8_512x512_80k_ade20k In Collection: dmnet Metadata: backbone: R-101-D8 crop size: (512,512) lr schd: 80000 inference time (ms/im): - value: 72.05 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,512) memory (GB): 13.0 Results: Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 45.34 mIoU(ms+flip): 46.13 Config: configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k_20201215_104812-bfa45311.pth - Name: dmnet_r50-d8_512x512_160k_ade20k In Collection: dmnet Metadata: backbone: R-50-D8 crop size: (512,512) lr schd: 160000 Results: Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 43.15 mIoU(ms+flip): 44.17 Config: configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k_20201215_115313-025ab3f9.pth - Name: dmnet_r101-d8_512x512_160k_ade20k In Collection: dmnet Metadata: backbone: R-101-D8 crop size: (512,512) lr schd: 160000 Results: Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 45.42 mIoU(ms+flip): 46.76 Config: configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k_20201215_111145-a0bc02ef.pth