| Collections: | |
| - Name: FCN | |
| License: Apache License 2.0 | |
| Metadata: | |
| Training Data: | |
| - Cityscapes | |
| - ADE20K | |
| - Pascal VOC 2012 + Aug | |
| - Pascal Context | |
| - Pascal Context 59 | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| README: configs/fcn/README.md | |
| Frameworks: | |
| - PyTorch | |
| Models: | |
| - Name: fcn_r50-d8_4xb2-40k_cityscapes-512x1024 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 72.25 | |
| mIoU(ms+flip): 73.36 | |
| Config: configs/fcn/fcn_r50-d8_4xb2-40k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 5.7 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r101-d8_4xb2-40k_cityscapes-512x1024 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 75.45 | |
| mIoU(ms+flip): 76.58 | |
| Config: configs/fcn/fcn_r101-d8_4xb2-40k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 9.2 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r50-d8_4xb2-40k_cityscapes-769x769 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 71.47 | |
| mIoU(ms+flip): 72.54 | |
| Config: configs/fcn/fcn_r50-d8_4xb2-40k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 6.5 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r101-d8_4xb2-40k_cityscapes-769x769 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 73.93 | |
| mIoU(ms+flip): 75.14 | |
| Config: configs/fcn/fcn_r101-d8_4xb2-40k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 10.4 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r18-d8_4xb2-80k_cityscapes-512x1024 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 71.11 | |
| mIoU(ms+flip): 72.91 | |
| Config: configs/fcn/fcn_r18-d8_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-18-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 1.7 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes-20201225_021327.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r50-d8_4xb2-80k_cityscapes-512x1024 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 73.61 | |
| mIoU(ms+flip): 74.24 | |
| Config: configs/fcn/fcn_r50-d8_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r101-d8_4xb2-80k_cityscapes-512x1024 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 75.13 | |
| mIoU(ms+flip): 75.94 | |
| Config: configs/fcn/fcn_r101-d8_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r101-d8_4xb2-amp-80k_cityscapes-512x1024 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 76.8 | |
| Config: configs/fcn/fcn_r101-d8_4xb2-amp-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - FCN | |
| - (FP16) | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 5.37 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_fp16_512x1024_80k_cityscapes/fcn_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230921-fb13e883.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_fp16_512x1024_80k_cityscapes/fcn_r101-d8_fp16_512x1024_80k_cityscapes_20200717_230921.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r18-d8_4xb2-80k_cityscapes-769x769 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 70.8 | |
| mIoU(ms+flip): 73.16 | |
| Config: configs/fcn/fcn_r18-d8_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-18-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 1.9 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes-20201225_021451.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r50-d8_4xb2-80k_cityscapes-769x769 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 72.64 | |
| mIoU(ms+flip): 73.32 | |
| Config: configs/fcn/fcn_r50-d8_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r101-d8_4xb2-80k_cityscapes-769x769 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 75.52 | |
| mIoU(ms+flip): 76.61 | |
| Config: configs/fcn/fcn_r101-d8_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r18b-d8_4xb2-80k_cityscapes-512x1024 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 70.24 | |
| mIoU(ms+flip): 72.77 | |
| Config: configs/fcn/fcn_r18b-d8_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-18b-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 1.6 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes-20201225_230143.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r50b-d8_4xb2-80k_cityscapes-512x1024 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 75.65 | |
| mIoU(ms+flip): 77.59 | |
| Config: configs/fcn/fcn_r50b-d8_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50b-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 5.6 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes-20201225_094221.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r101b-d8_4xb2-80k_cityscapes-512x1024 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 77.37 | |
| mIoU(ms+flip): 78.77 | |
| Config: configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101b-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 9.1 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes-20201226_160213.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r18b-d8_4xb2-80k_cityscapes-769x769 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 69.66 | |
| mIoU(ms+flip): 72.07 | |
| Config: configs/fcn/fcn_r18b-d8_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-18b-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 1.7 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes-20201226_004430.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r50b-d8_4xb2-80k_cityscapes-769x769 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 73.83 | |
| mIoU(ms+flip): 76.6 | |
| Config: configs/fcn/fcn_r50b-d8_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50b-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 6.3 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes-20201225_094223.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r101b-d8_4xb2-80k_cityscapes-769x769 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 77.02 | |
| mIoU(ms+flip): 78.67 | |
| Config: configs/fcn/fcn_r101b-d8_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101b-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 10.3 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes-20201226_170012.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn-d6_r50-d16_4xb2-40k_cityscapes-512x1024 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 77.06 | |
| mIoU(ms+flip): 78.85 | |
| Config: configs/fcn/fcn-d6_r50-d16_4xb2-40k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D16 | |
| - FCN | |
| - (D6) | |
| Training Resources: 4x TITAN Xp GPUS | |
| Memory (GB): 3.4 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes_20210305_130133-98d5d1bc.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-20210305_130133.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 77.27 | |
| mIoU(ms+flip): 78.88 | |
| Config: configs/fcn/fcn-d6_r50-d16_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D16 | |
| - FCN | |
| - (D6) | |
| Training Resources: 4x TITAN Xp GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_80k_cityscapes_20210306_115604-133c292f.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_80k_cityscapes-20210306_115604.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn-d6_r50-d16_4xb2-40k_cityscapes-769x769 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 76.82 | |
| mIoU(ms+flip): 78.22 | |
| Config: configs/fcn/fcn-d6_r50-d16_4xb2-40k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D16 | |
| - FCN | |
| - (D6) | |
| Training Resources: 4x TITAN Xp GPUS | |
| Memory (GB): 3.7 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes_20210305_185744-1aab18ed.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-20210305_185744.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn-d6_r50-d16_4xb2-80k_cityscapes-769x769 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 77.04 | |
| mIoU(ms+flip): 78.4 | |
| Config: configs/fcn/fcn-d6_r50-d16_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D16 | |
| - FCN | |
| - (D6) | |
| Training Resources: 4x TITAN Xp GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes_20210305_200413-109d88eb.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-20210305_200413.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn-d6_r101-d16_4xb2-40k_cityscapes-512x1024 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 77.36 | |
| mIoU(ms+flip): 79.18 | |
| Config: configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D16 | |
| - FCN | |
| - (D6) | |
| Training Resources: 4x TITAN Xp GPUS | |
| Memory (GB): 4.5 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes_20210305_130337-9cf2b450.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-20210305_130337.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn-d6_r101-d16_4xb2-80k_cityscapes-512x1024 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 78.46 | |
| mIoU(ms+flip): 80.42 | |
| Config: configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D16 | |
| - FCN | |
| - (D6) | |
| Training Resources: 4x TITAN Xp GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes_20210308_102747-cb336445.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-20210308_102747.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn-d6_r101-d16_4xb2-40k_cityscapes-769x769 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 77.28 | |
| mIoU(ms+flip): 78.95 | |
| Config: configs/fcn/fcn-d6_r101-d16_4xb2-40k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D16 | |
| - FCN | |
| - (D6) | |
| Training Resources: 4x TITAN Xp GPUS | |
| Memory (GB): 5.0 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes_20210308_102453-60b114e9.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-20210308_102453.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn-d6_r101-d16_4xb2-80k_cityscapes-769x769 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 78.06 | |
| mIoU(ms+flip): 79.58 | |
| Config: configs/fcn/fcn-d6_r101-d16_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D16 | |
| - FCN | |
| - (D6) | |
| Training Resources: 4x TITAN Xp GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes_20210306_120016-e33adc4f.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-20210306_120016.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn-d6_r50b-d16_4xb2-80k_cityscapes-512x1024 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 76.99 | |
| mIoU(ms+flip): 79.03 | |
| Config: configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50b-D16 | |
| - FCN | |
| - (D6) | |
| Training Resources: 4x TITAN Xp GPUS | |
| Memory (GB): 3.2 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes/fcn_d6_r50b-d16_512x1024_80k_cityscapes_20210311_125550-6a0b62e9.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes/fcn_d6_r50b_d16_512x1024_80k_cityscapes-20210311_125550.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn-d6_r50b-d16_4xb2-80k_cityscapes-769x769 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 76.86 | |
| mIoU(ms+flip): 78.52 | |
| Config: configs/fcn/fcn-d6_r50b-d16_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50b-D16 | |
| - FCN | |
| - (D6) | |
| Training Resources: 4x TITAN Xp GPUS | |
| Memory (GB): 3.6 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes/fcn_d6_r50b-d16_769x769_80k_cityscapes_20210311_131012-d665f231.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes/fcn_d6_r50b_d16_769x769_80k_cityscapes-20210311_131012.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn-d6_r101b-d16_4xb2-80k_cityscapes-512x1024 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 77.72 | |
| mIoU(ms+flip): 79.53 | |
| Config: configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101b-D16 | |
| - FCN | |
| - (D6) | |
| Training Resources: 4x TITAN Xp GPUS | |
| Memory (GB): 4.3 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes/fcn_d6_r101b-d16_512x1024_80k_cityscapes_20210311_144305-3f2eb5b4.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes/fcn_d6_r101b_d16_512x1024_80k_cityscapes-20210311_144305.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn-d6_r101b-d16_4xb2-80k_cityscapes-769x769 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 77.34 | |
| mIoU(ms+flip): 78.91 | |
| Config: configs/fcn/fcn-d6_r101b-d16_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101b-D16 | |
| - FCN | |
| - (D6) | |
| Training Resources: 4x TITAN Xp GPUS | |
| Memory (GB): 4.8 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes/fcn_d6_r101b-d16_769x769_80k_cityscapes_20210311_154527-c4d8bfbc.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes/fcn_d6_r101b_d16_769x769_80k_cityscapes-20210311_154527.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r50-d8_4xb4-80k_ade20k-512x512 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 35.94 | |
| mIoU(ms+flip): 37.94 | |
| Config: configs/fcn/fcn_r50-d8_4xb4-80k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 8.5 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r101-d8_4xb4-80k_ade20k-512x512 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 39.61 | |
| mIoU(ms+flip): 40.83 | |
| Config: configs/fcn/fcn_r101-d8_4xb4-80k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 12.0 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r50-d8_4xb4-160k_ade20k-512x512 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 36.1 | |
| mIoU(ms+flip): 38.08 | |
| Config: configs/fcn/fcn_r50-d8_4xb4-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r101-d8_4xb4-160k_ade20k-512x512 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 39.91 | |
| mIoU(ms+flip): 41.4 | |
| Config: configs/fcn/fcn_r101-d8_4xb4-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r50-d8_4xb4-20k_voc12aug-512x512 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 67.08 | |
| mIoU(ms+flip): 69.94 | |
| Config: configs/fcn/fcn_r50-d8_4xb4-20k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 5.7 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r101-d8_4xb4-20k_voc12aug-512x512 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 71.16 | |
| mIoU(ms+flip): 73.57 | |
| Config: configs/fcn/fcn_r101-d8_4xb4-20k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 9.2 | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r50-d8_4xb4-40k_voc12aug-512x512 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 66.97 | |
| mIoU(ms+flip): 69.04 | |
| Config: configs/fcn/fcn_r50-d8_4xb4-40k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r101-d8_4xb4-40k_voc12aug-512x512 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 69.91 | |
| mIoU(ms+flip): 72.38 | |
| Config: configs/fcn/fcn_r101-d8_4xb4-40k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r101-d8_4xb4-40k_pascal-context-480x480 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal Context | |
| Metrics: | |
| mIoU: 44.43 | |
| mIoU(ms+flip): 45.63 | |
| Config: configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-480x480.py | |
| Metadata: | |
| Training Data: Pascal Context | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context-20210421_154757.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r101-d8_4xb4-80k_pascal-context-480x480 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal Context | |
| Metrics: | |
| mIoU: 44.13 | |
| mIoU(ms+flip): 45.26 | |
| Config: configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-480x480.py | |
| Metadata: | |
| Training Data: Pascal Context | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| 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 | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context-20210421_163310.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r101-d8_4xb4-40k_pascal-context-59-480x480 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal Context 59 | |
| Metrics: | |
| mIoU: 48.42 | |
| mIoU(ms+flip): 50.4 | |
| Config: configs/fcn/fcn_r101-d8_4xb4-40k_pascal-context-59-480x480.py | |
| Metadata: | |
| Training Data: Pascal Context 59 | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| 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 | |
| Training log: 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.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |
| - Name: fcn_r101-d8_4xb4-80k_pascal-context-59-480x480 | |
| In Collection: FCN | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal Context 59 | |
| Metrics: | |
| mIoU: 49.35 | |
| mIoU(ms+flip): 51.38 | |
| Config: configs/fcn/fcn_r101-d8_4xb4-80k_pascal-context-59-480x480.py | |
| Metadata: | |
| Training Data: Pascal Context 59 | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - FCN | |
| Training Resources: 4x V100 GPUS | |
| 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 | |
| Training log: 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.log.json | |
| Paper: | |
| Title: Fully Convolutional Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1411.4038 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/fcn_head.py#L11 | |
| Framework: PyTorch | |