Collections: - Name: PSPNet Metadata: Training Data: - Cityscapes - Pascal Context - Pascal VOC 2012 + Aug - ADE20K Models: - Name: pspnet_r50-d8_512x1024_40k_cityscapes In Collection: PSPNet Metadata: inference time (ms/im): - value: 245.7 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 77.85 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth Config: configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py - Name: pspnet_r101-d8_512x1024_40k_cityscapes In Collection: PSPNet Metadata: inference time (ms/im): - value: 373.13 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 78.34 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth Config: configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py - Name: pspnet_r50-d8_769x769_40k_cityscapes In Collection: PSPNet Metadata: inference time (ms/im): - value: 568.18 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 78.26 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725-86638686.pth Config: configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.py - Name: pspnet_r101-d8_769x769_40k_cityscapes In Collection: PSPNet Metadata: inference time (ms/im): - value: 869.57 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/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753-61c6f5be.pth Config: configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes.py - Name: pspnet_r18-d8_512x1024_80k_cityscapes In Collection: PSPNet Metadata: inference time (ms/im): - value: 63.65 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 74.87 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes_20201225_021458-09ffa746.pth Config: configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py - Name: pspnet_r50-d8_512x1024_80k_cityscapes In Collection: PSPNet Metadata: inference time (ms/im): - value: 245.7 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 78.55 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131-2376f12b.pth Config: configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py - Name: pspnet_r101-d8_512x1024_80k_cityscapes In Collection: PSPNet Metadata: inference time (ms/im): - value: 373.13 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 79.76 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth Config: configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py - Name: pspnet_r18-d8_769x769_80k_cityscapes In Collection: PSPNet Metadata: inference time (ms/im): - value: 161.29 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 75.90 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes_20201225_021458-3deefc62.pth Config: configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes.py - Name: pspnet_r50-d8_769x769_80k_cityscapes In Collection: PSPNet Metadata: inference time (ms/im): - value: 568.18 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 79.59 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121-5ccf03dd.pth Config: configs/pspnet/pspnet_r50-d8_769x769_80k_cityscapes.py - Name: pspnet_r101-d8_769x769_80k_cityscapes In Collection: PSPNet Metadata: inference time (ms/im): - value: 869.57 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 79.77 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055-dba412fa.pth Config: configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py - Name: pspnet_r18b-d8_512x1024_80k_cityscapes In Collection: PSPNet Metadata: inference time (ms/im): - value: 61.43 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 74.23 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes_20201226_063116-26928a60.pth Config: configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py - Name: pspnet_r50b-d8_512x1024_80k_cityscapes In Collection: PSPNet Metadata: inference time (ms/im): - value: 232.56 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 78.22 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes_20201225_094315-6344287a.pth Config: configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py - Name: pspnet_r101b-d8_512x1024_80k_cityscapes In Collection: PSPNet Metadata: inference time (ms/im): - value: 362.32 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 79.69 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth Config: configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py - Name: pspnet_r18b-d8_769x769_80k_cityscapes In Collection: PSPNet Metadata: inference time (ms/im): - value: 156.01 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 74.92 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes_20201226_080942-bf98d186.pth Config: configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes.py - Name: pspnet_r50b-d8_769x769_80k_cityscapes In Collection: PSPNet Metadata: inference time (ms/im): - value: 531.91 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 78.50 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes_20201225_094316-4c643cf6.pth Config: configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.py - Name: pspnet_r101b-d8_769x769_80k_cityscapes In Collection: PSPNet Metadata: inference time (ms/im): - value: 854.7 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 78.87 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes_20201226_171823-f0e7c293.pth Config: configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py - Name: pspnet_r50-d8_512x512_80k_ade20k In Collection: PSPNet Metadata: inference time (ms/im): - value: 42.5 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 41.13 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128-15a8b914.pth Config: configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py - Name: pspnet_r101-d8_512x512_80k_ade20k In Collection: PSPNet Metadata: inference time (ms/im): - value: 65.36 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 43.57 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423-b6e782f0.pth Config: configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.py - Name: pspnet_r50-d8_512x512_160k_ade20k In Collection: PSPNet Metadata: inference time (ms/im): - value: 42.5 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 42.48 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358-1890b0bd.pth Config: configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py - Name: pspnet_r101-d8_512x512_160k_ade20k In Collection: PSPNet Metadata: inference time (ms/im): - value: 65.36 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 44.39 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650-967c316f.pth Config: configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py - Name: pspnet_r50-d8_512x512_20k_voc12aug In Collection: PSPNet Metadata: inference time (ms/im): - value: 42.39 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug Metrics: mIoU: 76.78 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958-ed5dfbd9.pth Config: configs/pspnet/pspnet_r50-d8_512x512_20k_voc12aug.py - Name: pspnet_r101-d8_512x512_20k_voc12aug In Collection: PSPNet Metadata: inference time (ms/im): - value: 66.58 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug Metrics: mIoU: 78.47 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003-4aef3c9a.pth Config: configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py - Name: pspnet_r50-d8_512x512_40k_voc12aug In Collection: PSPNet Metadata: inference time (ms/im): - value: 42.39 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug Metrics: mIoU: 77.29 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222-ae9c1b8c.pth Config: configs/pspnet/pspnet_r50-d8_512x512_40k_voc12aug.py - Name: pspnet_r101-d8_512x512_40k_voc12aug In Collection: PSPNet Metadata: inference time (ms/im): - value: 66.58 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug Metrics: mIoU: 78.52 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222-bc933b18.pth Config: configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py - Name: pspnet_r101-d8_480x480_40k_pascal_context In Collection: PSPNet Metadata: inference time (ms/im): - value: 103.31 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context Metrics: mIoU: 46.60 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context_20200911_211210-bf0f5d7c.pth Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py - Name: pspnet_r101-d8_480x480_80k_pascal_context In Collection: PSPNet Metadata: inference time (ms/im): - value: 103.31 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context Metrics: mIoU: 46.03 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context_20200911_190530-c86d6233.pth Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py - Name: pspnet_r101-d8_480x480_40k_pascal_context In Collection: PSPNet Metadata: inference time (ms/im): - value: None hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context Metrics: mIoU: 52.02 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context_59/pspnet_r101-d8_480x480_40k_pascal_context_59_20210416_114524-86d44cd4.pth Config: configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py - Name: pspnet_r101-d8_480x480_80k_pascal_context_59 In Collection: PSPNet Metadata: inference time (ms/im): - value: None hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context Metrics: mIoU: 52.47 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59/pspnet_r101-d8_480x480_80k_pascal_context_59_20210416_114418-fa6caaa2.pth Config: configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context_59.py