Collections: - Name: APCNet Metadata: Training Data: - Cityscapes - ADE20K Models: - Name: apcnet_r50-d8_512x1024_40k_cityscapes In Collection: APCNet Metadata: inference time (ms/im): - value: 280.11 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 78.02 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth Config: configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py - Name: apcnet_r101-d8_512x1024_40k_cityscapes In Collection: APCNet Metadata: inference time (ms/im): - value: 465.12 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 79.08 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth Config: configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py - Name: apcnet_r50-d8_769x769_40k_cityscapes In Collection: APCNet Metadata: inference time (ms/im): - value: 657.89 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 77.89 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth Config: configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py - Name: apcnet_r101-d8_769x769_40k_cityscapes In Collection: APCNet Metadata: inference time (ms/im): - value: 970.87 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 77.96 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth Config: configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py - Name: apcnet_r50-d8_512x1024_80k_cityscapes In Collection: APCNet Metadata: inference time (ms/im): - value: 280.11 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 78.96 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth Config: configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py - Name: apcnet_r101-d8_512x1024_80k_cityscapes In Collection: APCNet Metadata: inference time (ms/im): - value: 465.12 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/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth Config: configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py - Name: apcnet_r50-d8_769x769_80k_cityscapes In Collection: APCNet Metadata: inference time (ms/im): - value: 657.89 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 78.79 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth Config: configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py - Name: apcnet_r101-d8_769x769_80k_cityscapes In Collection: APCNet Metadata: inference time (ms/im): - value: 970.87 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 78.45 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth Config: configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py - Name: apcnet_r50-d8_512x512_80k_ade20k In Collection: APCNet Metadata: inference time (ms/im): - value: 50.99 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 42.20 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth Config: configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py - Name: apcnet_r101-d8_512x512_80k_ade20k In Collection: APCNet Metadata: inference time (ms/im): - value: 76.34 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 45.54 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth Config: configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py - Name: apcnet_r50-d8_512x512_160k_ade20k In Collection: APCNet Metadata: inference time (ms/im): - value: 50.99 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 43.40 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth Config: configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.py - Name: apcnet_r101-d8_512x512_160k_ade20k In Collection: APCNet Metadata: inference time (ms/im): - value: 76.34 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 45.41 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth Config: configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py