Collections: - Name: FCN Metadata: Training Data: - Cityscapes - Pascal Context - Pascal VOC 2012 + Aug - ADE20K - Name: FCN-D6 Metadata: Training Data: - Cityscapes - Pascal Context - Pascal VOC 2012 + Aug - ADE20K Models: - Name: fcn_r50-d8_512x1024_40k_cityscapes In Collection: FCN Metadata: inference time (ms/im): - value: 239.81 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 72.25 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth Config: configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py - Name: fcn_r101-d8_512x1024_40k_cityscapes In Collection: FCN Metadata: inference time (ms/im): - value: 375.94 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 75.45 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852-a883d3a1.pth Config: configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes.py - Name: fcn_r50-d8_769x769_40k_cityscapes In Collection: FCN Metadata: inference time (ms/im): - value: 555.56 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 71.47 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104-977b5d02.pth Config: configs/fcn/fcn_r50-d8_769x769_40k_cityscapes.py - Name: fcn_r101-d8_769x769_40k_cityscapes In Collection: FCN Metadata: inference time (ms/im): - value: 840.34 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 73.93 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208-7d4ab69c.pth Config: configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py - Name: fcn_r18-d8_512x1024_80k_cityscapes In Collection: FCN Metadata: inference time (ms/im): - value: 68.26 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 71.11 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth Config: configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py - Name: fcn_r50-d8_512x1024_80k_cityscapes In Collection: FCN Metadata: inference time (ms/im): - value: 239.81 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 73.61 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019-03aa804d.pth Config: configs/fcn/fcn_r50-d8_512x1024_80k_cityscapes.py - Name: fcn_r101-d8_512x1024_80k_cityscapes In Collection: FCN Metadata: inference time (ms/im): - value: 375.94 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 75.13 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038-3fb937eb.pth Config: configs/fcn/fcn_r101-d8_512x1024_80k_cityscapes.py - Name: fcn_r18-d8_769x769_80k_cityscapes In Collection: FCN Metadata: inference time (ms/im): - value: 156.25 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 70.80 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes_20201225_021451-9739d1b8.pth Config: configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py - Name: fcn_r50-d8_769x769_80k_cityscapes In Collection: FCN Metadata: inference time (ms/im): - value: 555.56 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 72.64 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749-f5caeabc.pth Config: configs/fcn/fcn_r50-d8_769x769_80k_cityscapes.py - Name: fcn_r101-d8_769x769_80k_cityscapes In Collection: FCN Metadata: inference time (ms/im): - value: 840.34 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 75.52 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354-45cbac68.pth Config: configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.py - Name: fcn_r18b-d8_512x1024_80k_cityscapes In Collection: FCN Metadata: inference time (ms/im): - value: 59.74 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 70.24 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth Config: configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py - Name: fcn_r50b-d8_512x1024_80k_cityscapes In Collection: FCN Metadata: inference time (ms/im): - value: 238.1 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 75.65 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes_20201225_094221-82957416.pth Config: configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes.py - Name: fcn_r101b-d8_512x1024_80k_cityscapes In Collection: FCN Metadata: inference time (ms/im): - value: 366.3 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 77.37 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes_20201226_160213-4543858f.pth Config: configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py - Name: fcn_r18b-d8_769x769_80k_cityscapes In Collection: FCN Metadata: inference time (ms/im): - value: 149.25 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 69.66 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes_20201226_004430-32d504e5.pth Config: configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes.py - Name: fcn_r50b-d8_769x769_80k_cityscapes In Collection: FCN Metadata: inference time (ms/im): - value: 549.45 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 73.83 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes_20201225_094223-94552d38.pth Config: configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py - Name: fcn_r101b-d8_769x769_80k_cityscapes In Collection: FCN 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: 77.02 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes_20201226_170012-82be37e2.pth Config: configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py - Name: fcn_d6_r50-d16_512x1024_40k_cityscapes In Collection: FCN-D6 Metadata: inference time (ms/im): - value: 97.85 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 77.06 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth Config: configs/fcn-d6/fcn_d6_r50-d16_512x1024_40k_cityscapes.py - Name: fcn_d6_r50-d16_512x1024_80k_cityscapes In Collection: FCN-D6 Metadata: inference time (ms/im): - value: 96.62 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 77.27 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth Config: configs/fcn-d6/fcn_d6_r50-d16_512x1024_80k_cityscapes.py - Name: fcn_d6_r50-d16_769x769_40k_cityscapes In Collection: FCN-D6 Metadata: inference time (ms/im): - value: 239.81 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 76.82 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-1aab18ed.pth Config: configs/fcn-d6/fcn_d6_r50-d16_769x769_40k_cityscapes.py - Name: fcn_d6_r50-d16_769x769_80k_cityscapes In Collection: FCN-D6 Metadata: inference time (ms/im): - value: 240.96 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 77.04 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-109d88eb.pth Config: configs/fcn-d6/fcn_d6_r50-d16_769x769_80k_cityscapes.py - Name: fcn_d6_r101-d16_512x1024_40k_cityscapes In Collection: FCN-D6 Metadata: inference time (ms/im): - value: 124.38 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 77.36 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-9cf2b450.pth Config: configs/fcn-d6/fcn_d6_r101-d16_512x1024_40k_cityscapes.py - Name: fcn_d6_r101-d16_512x1024_80k_cityscapes In Collection: FCN-D6 Metadata: inference time (ms/im): - value: 121.07 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 78.46 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-cb336445.pth Config: configs/fcn-d6/fcn_d6_r101-d16_512x1024_80k_cityscapes.py - Name: fcn_d6_r101-d16_769x769_40k_cityscapes In Collection: FCN-D6 Metadata: inference time (ms/im): - value: 320.51 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 77.28 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-60b114e9.pth Config: configs/fcn-d6/fcn_d6_r101-d16_769x769_40k_cityscapes.py - Name: fcn_d6_r101-d16_769x769_80k_cityscapes In Collection: FCN-D6 Metadata: inference time (ms/im): - value: 311.53 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 78.06 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-e33adc4f.pth Config: configs/fcn-d6/fcn_d6_r101-d16_769x769_80k_cityscapes.py - Name: fcn_r50-d8_512x512_80k_ade20k In Collection: FCN Metadata: inference time (ms/im): - value: 42.57 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 35.94 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016-f8ac5082.pth Config: configs/fcn/fcn_r50-d8_512x512_80k_ade20k.py - Name: fcn_r101-d8_512x512_80k_ade20k In Collection: FCN Metadata: inference time (ms/im): - value: 67.66 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 39.61 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143-bc1809f7.pth Config: configs/fcn/fcn_r101-d8_512x512_80k_ade20k.py - Name: fcn_r50-d8_512x512_160k_ade20k In Collection: FCN Metadata: inference time (ms/im): - value: 42.57 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 36.10 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713-4edbc3b4.pth Config: configs/fcn/fcn_r50-d8_512x512_160k_ade20k.py - Name: fcn_r101-d8_512x512_160k_ade20k In Collection: FCN Metadata: inference time (ms/im): - value: 67.66 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 39.91 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816-fd192bd5.pth Config: configs/fcn/fcn_r101-d8_512x512_160k_ade20k.py - Name: fcn_r50-d8_512x512_20k_voc12aug In Collection: FCN Metadata: inference time (ms/im): - value: 42.96 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug Metrics: mIoU: 67.08 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715-52dc5306.pth Config: configs/fcn/fcn_r50-d8_512x512_20k_voc12aug.py - Name: fcn_r101-d8_512x512_20k_voc12aug In Collection: FCN Metadata: inference time (ms/im): - value: 67.52 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug Metrics: mIoU: 71.16 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842-0bb4e798.pth Config: configs/fcn/fcn_r101-d8_512x512_20k_voc12aug.py - Name: fcn_r50-d8_512x512_40k_voc12aug In Collection: FCN Metadata: inference time (ms/im): - value: 42.96 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug Metrics: mIoU: 66.97 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222-5e2dbf40.pth Config: configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.py - Name: fcn_r101-d8_512x512_40k_voc12aug In Collection: FCN Metadata: inference time (ms/im): - value: 67.52 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal VOC 2012 + Aug Metrics: mIoU: 69.91 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240-4c8bcefd.pth Config: configs/fcn/fcn_r101-d8_512x512_40k_voc12aug.py - Name: fcn_r101-d8_480x480_40k_pascal_context In Collection: FCN Metadata: inference time (ms/im): - value: 100.7 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context Metrics: mIoU: 44.43 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context-20210421_154757-b5e97937.pth Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py - Name: fcn_r101-d8_480x480_80k_pascal_context In Collection: FCN Metadata: inference time (ms/im): - value: 100.7 hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: Pascal Context Metrics: mIoU: 44.13 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context-20210421_163310-4711813f.pth Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context.py - Name: fcn_r101-d8_480x480_40k_pascal_context_59 In Collection: FCN 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: 48.42 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context_59/fcn_r101-d8_480x480_40k_pascal_context_59_20210415_230724-8cf83682.pth Config: configs/fcn/fcn_r101-d8_480x480_40k_pascal_context_59.py - Name: fcn_r101-d8_480x480_80k_pascal_context_59 In Collection: FCN 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: 49.35 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context_59/fcn_r101-d8_480x480_80k_pascal_context_59_20210416_110804-9a6f2c94.pth Config: configs/fcn/fcn_r101-d8_480x480_80k_pascal_context_59.py