Collections: - Name: DeepLabV3 Metadata: Training Data: - Cityscapes - ADE20K - Pascal VOC 2012 + Aug - Pascal Context - Pascal Context 59 - COCO-Stuff 10k - COCO-Stuff 164k Paper: URL: https://arxiv.org/abs/1706.05587 Title: Rethinking atrous convolution for semantic image segmentation README: configs/deeplabv3/README.md Code: URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/aspp_head.py#L54 Version: v0.17.0 Converted From: Code: https://github.com/tensorflow/models/tree/master/research/deeplab Models: - Name: deeplabv3_r50-d8_512x1024_40k_cityscapes In Collection: DeepLabV3 Metadata: backbone: R-50-D8 crop size: (512,1024) lr schd: 40000 inference time (ms/im): - value: 389.11 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,1024) Training Memory (GB): 6.1 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 79.09 mIoU(ms+flip): 80.45 Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth - Name: deeplabv3_r101-d8_512x1024_40k_cityscapes In Collection: DeepLabV3 Metadata: backbone: R-101-D8 crop size: (512,1024) lr schd: 40000 inference time (ms/im): - value: 520.83 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,1024) Training Memory (GB): 9.6 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 77.12 mIoU(ms+flip): 79.61 Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth - Name: deeplabv3_r50-d8_769x769_40k_cityscapes In Collection: DeepLabV3 Metadata: backbone: R-50-D8 crop size: (769,769) lr schd: 40000 inference time (ms/im): - value: 900.9 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (769,769) Training Memory (GB): 6.9 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 78.58 mIoU(ms+flip): 79.89 Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth - Name: deeplabv3_r101-d8_769x769_40k_cityscapes In Collection: DeepLabV3 Metadata: backbone: R-101-D8 crop size: (769,769) lr schd: 40000 inference time (ms/im): - value: 1204.82 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (769,769) Training Memory (GB): 10.9 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 79.27 mIoU(ms+flip): 80.11 Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth - Name: deeplabv3_r18-d8_512x1024_80k_cityscapes In Collection: DeepLabV3 Metadata: backbone: R-18-D8 crop size: (512,1024) lr schd: 80000 inference time (ms/im): - value: 72.57 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,1024) Training Memory (GB): 1.7 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 76.7 mIoU(ms+flip): 78.27 Config: configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth - Name: deeplabv3_r50-d8_512x1024_80k_cityscapes In Collection: DeepLabV3 Metadata: backbone: R-50-D8 crop size: (512,1024) lr schd: 80000 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 79.32 mIoU(ms+flip): 80.57 Config: configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth - Name: deeplabv3_r101-d8_512x1024_80k_cityscapes In Collection: DeepLabV3 Metadata: backbone: R-101-D8 crop size: (512,1024) lr schd: 80000 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 80.2 mIoU(ms+flip): 81.21 Config: configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth - Name: deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes In Collection: DeepLabV3 Metadata: backbone: R-101-D8 crop size: (512,1024) lr schd: 80000 inference time (ms/im): - value: 259.07 hardware: V100 backend: PyTorch batch size: 1 mode: FP16 resolution: (512,1024) Training Memory (GB): 5.75 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 80.48 Config: configs/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes/deeplabv3_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230920-774d9cec.pth - Name: deeplabv3_r18-d8_769x769_80k_cityscapes In Collection: DeepLabV3 Metadata: backbone: R-18-D8 crop size: (769,769) lr schd: 80000 inference time (ms/im): - value: 180.18 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (769,769) Training Memory (GB): 1.9 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 76.6 mIoU(ms+flip): 78.26 Config: configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth - Name: deeplabv3_r50-d8_769x769_80k_cityscapes In Collection: DeepLabV3 Metadata: backbone: R-50-D8 crop size: (769,769) lr schd: 80000 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 79.89 mIoU(ms+flip): 81.06 Config: configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth - Name: deeplabv3_r101-d8_769x769_80k_cityscapes In Collection: DeepLabV3 Metadata: backbone: R-101-D8 crop size: (769,769) lr schd: 80000 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 79.67 mIoU(ms+flip): 80.81 Config: configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth - Name: deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes In Collection: DeepLabV3 Metadata: backbone: R-101-D16-MG124 crop size: (512,1024) lr schd: 80000 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 78.36 mIoU(ms+flip): 79.84 Config: configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth - Name: deeplabv3_r18b-d8_512x1024_80k_cityscapes In Collection: DeepLabV3 Metadata: backbone: R-18b-D8 crop size: (512,1024) lr schd: 80000 inference time (ms/im): - value: 71.79 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,1024) Training Memory (GB): 1.6 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 76.26 mIoU(ms+flip): 77.88 Config: configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth - Name: deeplabv3_r50b-d8_512x1024_80k_cityscapes In Collection: DeepLabV3 Metadata: backbone: R-50b-D8 crop size: (512,1024) lr schd: 80000 inference time (ms/im): - value: 364.96 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,1024) Training Memory (GB): 6.0 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 79.63 mIoU(ms+flip): 80.98 Config: configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth - Name: deeplabv3_r101b-d8_512x1024_80k_cityscapes In Collection: DeepLabV3 Metadata: backbone: R-101b-D8 crop size: (512,1024) lr schd: 80000 inference time (ms/im): - value: 552.49 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,1024) Training Memory (GB): 9.5 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 80.01 mIoU(ms+flip): 81.21 Config: configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth - Name: deeplabv3_r18b-d8_769x769_80k_cityscapes In Collection: DeepLabV3 Metadata: backbone: R-18b-D8 crop size: (769,769) lr schd: 80000 inference time (ms/im): - value: 172.71 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (769,769) Training Memory (GB): 1.8 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 76.63 mIoU(ms+flip): 77.51 Config: configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth - Name: deeplabv3_r50b-d8_769x769_80k_cityscapes In Collection: DeepLabV3 Metadata: backbone: R-50b-D8 crop size: (769,769) lr schd: 80000 inference time (ms/im): - value: 862.07 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (769,769) Training Memory (GB): 6.8 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 78.8 mIoU(ms+flip): 80.27 Config: configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth - Name: deeplabv3_r101b-d8_769x769_80k_cityscapes In Collection: DeepLabV3 Metadata: backbone: R-101b-D8 crop size: (769,769) lr schd: 80000 inference time (ms/im): - value: 1219.51 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (769,769) Training Memory (GB): 10.7 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 79.41 mIoU(ms+flip): 80.73 Config: configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth - Name: deeplabv3_r50-d8_512x512_80k_ade20k In Collection: DeepLabV3 Metadata: backbone: R-50-D8 crop size: (512,512) lr schd: 80000 inference time (ms/im): - value: 67.75 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,512) Training Memory (GB): 8.9 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 42.42 mIoU(ms+flip): 43.28 Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth - Name: deeplabv3_r101-d8_512x512_80k_ade20k In Collection: DeepLabV3 Metadata: backbone: R-101-D8 crop size: (512,512) lr schd: 80000 inference time (ms/im): - value: 98.62 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,512) Training Memory (GB): 12.4 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 44.08 mIoU(ms+flip): 45.19 Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth - Name: deeplabv3_r50-d8_512x512_160k_ade20k In Collection: DeepLabV3 Metadata: backbone: R-50-D8 crop size: (512,512) lr schd: 160000 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 42.66 mIoU(ms+flip): 44.09 Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth - Name: deeplabv3_r101-d8_512x512_160k_ade20k In Collection: DeepLabV3 Metadata: backbone: R-101-D8 crop size: (512,512) lr schd: 160000 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 45.0 mIoU(ms+flip): 46.66 Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth - Name: deeplabv3_r50-d8_512x512_20k_voc12aug In Collection: DeepLabV3 Metadata: backbone: R-50-D8 crop size: (512,512) lr schd: 20000 inference time (ms/im): - value: 72.05 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,512) Training Memory (GB): 6.1 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug Metrics: mIoU: 76.17 mIoU(ms+flip): 77.42 Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth - Name: deeplabv3_r101-d8_512x512_20k_voc12aug In Collection: DeepLabV3 Metadata: backbone: R-101-D8 crop size: (512,512) lr schd: 20000 inference time (ms/im): - value: 101.94 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,512) Training Memory (GB): 9.6 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug Metrics: mIoU: 78.7 mIoU(ms+flip): 79.95 Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth - Name: deeplabv3_r50-d8_512x512_40k_voc12aug In Collection: DeepLabV3 Metadata: backbone: R-50-D8 crop size: (512,512) lr schd: 40000 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug Metrics: mIoU: 77.68 mIoU(ms+flip): 78.78 Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth - Name: deeplabv3_r101-d8_512x512_40k_voc12aug In Collection: DeepLabV3 Metadata: backbone: R-101-D8 crop size: (512,512) lr schd: 40000 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug Metrics: mIoU: 77.92 mIoU(ms+flip): 79.18 Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth - Name: deeplabv3_r101-d8_480x480_40k_pascal_context In Collection: DeepLabV3 Metadata: backbone: R-101-D8 crop size: (480,480) lr schd: 40000 inference time (ms/im): - value: 141.04 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (480,480) Training Memory (GB): 9.2 Results: - Task: Semantic Segmentation Dataset: Pascal Context Metrics: mIoU: 46.55 mIoU(ms+flip): 47.81 Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth - Name: deeplabv3_r101-d8_480x480_80k_pascal_context In Collection: DeepLabV3 Metadata: backbone: R-101-D8 crop size: (480,480) lr schd: 80000 Results: - Task: Semantic Segmentation Dataset: Pascal Context Metrics: mIoU: 46.42 mIoU(ms+flip): 47.53 Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth - Name: deeplabv3_r101-d8_480x480_40k_pascal_context_59 In Collection: DeepLabV3 Metadata: backbone: R-101-D8 crop size: (480,480) lr schd: 40000 Results: - Task: Semantic Segmentation Dataset: Pascal Context 59 Metrics: mIoU: 52.61 mIoU(ms+flip): 54.28 Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59/deeplabv3_r101-d8_480x480_40k_pascal_context_59_20210416_110332-cb08ea46.pth - Name: deeplabv3_r101-d8_480x480_80k_pascal_context_59 In Collection: DeepLabV3 Metadata: backbone: R-101-D8 crop size: (480,480) lr schd: 80000 Results: - Task: Semantic Segmentation Dataset: Pascal Context 59 Metrics: mIoU: 52.46 mIoU(ms+flip): 54.09 Config: configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context_59/deeplabv3_r101-d8_480x480_80k_pascal_context_59_20210416_113002-26303993.pth - Name: deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k In Collection: DeepLabV3 Metadata: backbone: R-50-D8 crop size: (512,512) lr schd: 20000 inference time (ms/im): - value: 92.59 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,512) Training Memory (GB): 9.6 Results: - Task: Semantic Segmentation Dataset: COCO-Stuff 10k Metrics: mIoU: 34.66 mIoU(ms+flip): 36.08 Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-b35f789d.pth - Name: deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k In Collection: DeepLabV3 Metadata: backbone: R-101-D8 crop size: (512,512) lr schd: 20000 inference time (ms/im): - value: 114.94 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,512) Training Memory (GB): 13.2 Results: - Task: Semantic Segmentation Dataset: COCO-Stuff 10k Metrics: mIoU: 37.3 mIoU(ms+flip): 38.42 Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_20k_coco-stuff10k_20210821_043025-c49752cb.pth - Name: deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k In Collection: DeepLabV3 Metadata: backbone: R-50-D8 crop size: (512,512) lr schd: 40000 Results: - Task: Semantic Segmentation Dataset: COCO-Stuff 10k Metrics: mIoU: 35.73 mIoU(ms+flip): 37.09 Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r50-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-dc76f3ff.pth - Name: deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k In Collection: DeepLabV3 Metadata: backbone: R-101-D8 crop size: (512,512) lr schd: 40000 Results: - Task: Semantic Segmentation Dataset: COCO-Stuff 10k Metrics: mIoU: 37.81 mIoU(ms+flip): 38.8 Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k/deeplabv3_r101-d8_512x512_4x4_40k_coco-stuff10k_20210821_043305-636cb433.pth - Name: deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k In Collection: DeepLabV3 Metadata: backbone: R-50-D8 crop size: (512,512) lr schd: 80000 inference time (ms/im): - value: 92.59 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,512) Training Memory (GB): 9.6 Results: - Task: Semantic Segmentation Dataset: COCO-Stuff 164k Metrics: mIoU: 39.38 mIoU(ms+flip): 40.03 Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_80k_coco-stuff164k_20210709_163016-88675c24.pth - Name: deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k In Collection: DeepLabV3 Metadata: backbone: R-101-D8 crop size: (512,512) lr schd: 80000 inference time (ms/im): - value: 114.94 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 resolution: (512,512) Training Memory (GB): 13.2 Results: - Task: Semantic Segmentation Dataset: COCO-Stuff 164k Metrics: mIoU: 40.87 mIoU(ms+flip): 41.5 Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_80k_coco-stuff164k_20210709_201252-13600dc2.pth - Name: deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k In Collection: DeepLabV3 Metadata: backbone: R-50-D8 crop size: (512,512) lr schd: 160000 Results: - Task: Semantic Segmentation Dataset: COCO-Stuff 164k Metrics: mIoU: 41.09 mIoU(ms+flip): 41.69 Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_160k_coco-stuff164k_20210709_163016-49f2812b.pth - Name: deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k In Collection: DeepLabV3 Metadata: backbone: R-101-D8 crop size: (512,512) lr schd: 160000 Results: - Task: Semantic Segmentation Dataset: COCO-Stuff 164k Metrics: mIoU: 41.82 mIoU(ms+flip): 42.49 Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_160k_coco-stuff164k_20210709_155402-f035acfd.pth - Name: deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k In Collection: DeepLabV3 Metadata: backbone: R-50-D8 crop size: (512,512) lr schd: 320000 Results: - Task: Semantic Segmentation Dataset: COCO-Stuff 164k Metrics: mIoU: 41.37 mIoU(ms+flip): 42.22 Config: configs/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r50-d8_512x512_4x4_320k_coco-stuff164k_20210709_155403-51b21115.pth - Name: deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k In Collection: DeepLabV3 Metadata: backbone: R-101-D8 crop size: (512,512) lr schd: 320000 Results: - Task: Semantic Segmentation Dataset: COCO-Stuff 164k Metrics: mIoU: 42.61 mIoU(ms+flip): 43.42 Config: configs/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k/deeplabv3_r101-d8_512x512_4x4_320k_coco-stuff164k_20210709_155402-3cbca14d.pth