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| Collections: | |
| - Name: EMANet | |
| License: Apache License 2.0 | |
| Metadata: | |
| Training Data: | |
| - Cityscapes | |
| Paper: | |
| Title: Expectation-Maximization Attention Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.13426 | |
| README: configs/emanet/README.md | |
| Frameworks: | |
| - PyTorch | |
| Models: | |
| - Name: eemanet_r50-d8_4xb2-80k_cityscapes-512x1024 | |
| In Collection: EMANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 77.59 | |
| mIoU(ms+flip): 79.44 | |
| Config: configs/emanet/eemanet_r50-d8_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - EMANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 5.4 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes_20200901_100301-c43fcef1.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes-20200901_100301.log.json | |
| Paper: | |
| Title: Expectation-Maximization Attention Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.13426 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ema_head.py#L80 | |
| Framework: PyTorch | |
| - Name: emanet_r101-d8_4xb2-80k_cityscapes-512x1024 | |
| In Collection: EMANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 79.1 | |
| mIoU(ms+flip): 81.21 | |
| Config: configs/emanet/emanet_r101-d8_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - EMANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 6.2 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes_20200901_100301-2d970745.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes-20200901_100301.log.json | |
| Paper: | |
| Title: Expectation-Maximization Attention Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.13426 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ema_head.py#L80 | |
| Framework: PyTorch | |
| - Name: emanet_r50-d8_4xb2-80k_cityscapes-769x769 | |
| In Collection: EMANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 79.33 | |
| mIoU(ms+flip): 80.49 | |
| Config: configs/emanet/emanet_r50-d8_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - EMANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 8.9 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes_20200901_100301-16f8de52.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes-20200901_100301.log.json | |
| Paper: | |
| Title: Expectation-Maximization Attention Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.13426 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ema_head.py#L80 | |
| Framework: PyTorch | |
| - Name: emanet_r101-d8_4xb2-80k_cityscapes-769x769 | |
| In Collection: EMANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 79.62 | |
| mIoU(ms+flip): 81.0 | |
| Config: configs/emanet/emanet_r101-d8_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - EMANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 10.1 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes_20200901_100301-47a324ce.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes-20200901_100301.log.json | |
| Paper: | |
| Title: Expectation-Maximization Attention Networks for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.13426 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ema_head.py#L80 | |
| Framework: PyTorch | |