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| Collections: | |
| - Name: ISANet | |
| License: Apache License 2.0 | |
| Metadata: | |
| Training Data: | |
| - Cityscapes | |
| - ADE20K | |
| - Pascal VOC 2012 + Aug | |
| Paper: | |
| Title: Interlaced Sparse Self-Attention for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.12273 | |
| README: configs/isanet/README.md | |
| Frameworks: | |
| - PyTorch | |
| Models: | |
| - Name: isanet_r50-d8_4xb2-40k_cityscapes-512x1024 | |
| In Collection: ISANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 78.49 | |
| mIoU(ms+flip): 79.44 | |
| Config: configs/isanet/isanet_r50-d8_4xb2-40k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - ISANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 5.869 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_40k_cityscapes/isanet_r50-d8_512x1024_40k_cityscapes_20210901_054739-981bd763.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_40k_cityscapes/isanet_r50-d8_512x1024_40k_cityscapes_20210901_054739.log.json | |
| Paper: | |
| Title: Interlaced Sparse Self-Attention for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.12273 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58 | |
| Framework: PyTorch | |
| - Name: isanet_r50-d8_4xb2-80k_cityscapes-512x1024 | |
| In Collection: ISANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 78.68 | |
| mIoU(ms+flip): 80.25 | |
| Config: configs/isanet/isanet_r50-d8_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - ISANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 5.869 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_80k_cityscapes/isanet_r50-d8_512x1024_80k_cityscapes_20210901_074202-89384497.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_80k_cityscapes/isanet_r50-d8_512x1024_80k_cityscapes_20210901_074202.log.json | |
| Paper: | |
| Title: Interlaced Sparse Self-Attention for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.12273 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58 | |
| Framework: PyTorch | |
| - Name: isanet_r50-d8_4xb2-40k_cityscapes-769x769 | |
| In Collection: ISANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 78.7 | |
| mIoU(ms+flip): 80.28 | |
| Config: configs/isanet/isanet_r50-d8_4xb2-40k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - ISANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 6.759 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_40k_cityscapes/isanet_r50-d8_769x769_40k_cityscapes_20210903_050200-4ae7e65b.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_40k_cityscapes/isanet_r50-d8_769x769_40k_cityscapes_20210903_050200.log.json | |
| Paper: | |
| Title: Interlaced Sparse Self-Attention for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.12273 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58 | |
| Framework: PyTorch | |
| - Name: isanet_r50-d8_4xb2-80k_cityscapes-769x769 | |
| In Collection: ISANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 79.29 | |
| mIoU(ms+flip): 80.53 | |
| Config: configs/isanet/isanet_r50-d8_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - ISANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 6.759 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_80k_cityscapes/isanet_r50-d8_769x769_80k_cityscapes_20210903_101126-99b54519.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_80k_cityscapes/isanet_r50-d8_769x769_80k_cityscapes_20210903_101126.log.json | |
| Paper: | |
| Title: Interlaced Sparse Self-Attention for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.12273 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58 | |
| Framework: PyTorch | |
| - Name: isanet_r101-d8_4xb2-40k_cityscapes-512x1024 | |
| In Collection: ISANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 79.58 | |
| mIoU(ms+flip): 81.05 | |
| Config: configs/isanet/isanet_r101-d8_4xb2-40k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - ISANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 9.425 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_40k_cityscapes/isanet_r101-d8_512x1024_40k_cityscapes_20210901_145553-293e6bd6.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_40k_cityscapes/isanet_r101-d8_512x1024_40k_cityscapes_20210901_145553.log.json | |
| Paper: | |
| Title: Interlaced Sparse Self-Attention for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.12273 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58 | |
| Framework: PyTorch | |
| - Name: isanet_r101-d8_4xb2-80k_cityscapes-512x1024 | |
| In Collection: ISANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 80.32 | |
| mIoU(ms+flip): 81.58 | |
| Config: configs/isanet/isanet_r101-d8_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - ISANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 9.425 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_80k_cityscapes/isanet_r101-d8_512x1024_80k_cityscapes_20210901_145243-5b99c9b2.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_80k_cityscapes/isanet_r101-d8_512x1024_80k_cityscapes_20210901_145243.log.json | |
| Paper: | |
| Title: Interlaced Sparse Self-Attention for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.12273 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58 | |
| Framework: PyTorch | |
| - Name: isanet_r101-d8_4xb2-40k_cityscapes-769x769 | |
| In Collection: ISANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 79.68 | |
| mIoU(ms+flip): 80.95 | |
| Config: configs/isanet/isanet_r101-d8_4xb2-40k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - ISANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 10.815 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_40k_cityscapes/isanet_r101-d8_769x769_40k_cityscapes_20210903_111320-509e7224.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_40k_cityscapes/isanet_r101-d8_769x769_40k_cityscapes_20210903_111320.log.json | |
| Paper: | |
| Title: Interlaced Sparse Self-Attention for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.12273 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58 | |
| Framework: PyTorch | |
| - Name: isanet_r101-d8_4xb2-80k_cityscapes-769x769 | |
| In Collection: ISANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 80.61 | |
| mIoU(ms+flip): 81.59 | |
| Config: configs/isanet/isanet_r101-d8_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - ISANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 10.815 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_80k_cityscapes/isanet_r101-d8_769x769_80k_cityscapes_20210903_111319-24f71dfa.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_80k_cityscapes/isanet_r101-d8_769x769_80k_cityscapes_20210903_111319.log.json | |
| Paper: | |
| Title: Interlaced Sparse Self-Attention for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.12273 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58 | |
| Framework: PyTorch | |
| - Name: isanet_r50-d8_4xb4-80k_ade20k-512x512 | |
| In Collection: ISANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 41.12 | |
| mIoU(ms+flip): 42.35 | |
| Config: configs/isanet/isanet_r50-d8_4xb4-80k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D8 | |
| - ISANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 9.0 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_80k_ade20k/isanet_r50-d8_512x512_80k_ade20k_20210903_124557-6ed83a0c.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_80k_ade20k/isanet_r50-d8_512x512_80k_ade20k_20210903_124557.log.json | |
| Paper: | |
| Title: Interlaced Sparse Self-Attention for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.12273 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58 | |
| Framework: PyTorch | |
| - Name: isanet_r50-d8_4xb4-160k_ade20k-512x512 | |
| In Collection: ISANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 42.59 | |
| mIoU(ms+flip): 43.07 | |
| Config: configs/isanet/isanet_r50-d8_4xb4-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D8 | |
| - ISANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 9.0 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_160k_ade20k/isanet_r50-d8_512x512_160k_ade20k_20210903_104850-f752d0a3.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_160k_ade20k/isanet_r50-d8_512x512_160k_ade20k_20210903_104850.log.json | |
| Paper: | |
| Title: Interlaced Sparse Self-Attention for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.12273 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58 | |
| Framework: PyTorch | |
| - Name: isanet_r101-d8_4xb4-80k_ade20k-512x512 | |
| In Collection: ISANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 43.51 | |
| mIoU(ms+flip): 44.38 | |
| Config: configs/isanet/isanet_r101-d8_4xb4-80k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - ISANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 12.562 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_80k_ade20k/isanet_r101-d8_512x512_80k_ade20k_20210903_162056-68b235c2.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_80k_ade20k/isanet_r101-d8_512x512_80k_ade20k_20210903_162056.log.json | |
| Paper: | |
| Title: Interlaced Sparse Self-Attention for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.12273 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58 | |
| Framework: PyTorch | |
| - Name: isanet_r101-d8_4xb4-160k_ade20k-512x512 | |
| In Collection: ISANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 43.8 | |
| mIoU(ms+flip): 45.4 | |
| Config: configs/isanet/isanet_r101-d8_4xb4-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - ISANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 12.562 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_160k_ade20k/isanet_r101-d8_512x512_160k_ade20k_20210903_211431-a7879dcd.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_160k_ade20k/isanet_r101-d8_512x512_160k_ade20k_20210903_211431.log.json | |
| Paper: | |
| Title: Interlaced Sparse Self-Attention for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.12273 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58 | |
| Framework: PyTorch | |
| - Name: isanet_r50-d8_4xb4-20k_voc12aug-512x512 | |
| In Collection: ISANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 76.78 | |
| mIoU(ms+flip): 77.79 | |
| Config: configs/isanet/isanet_r50-d8_4xb4-20k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D8 | |
| - ISANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 5.9 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_20k_voc12aug/isanet_r50-d8_512x512_20k_voc12aug_20210901_164838-79d59b80.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_20k_voc12aug/isanet_r50-d8_512x512_20k_voc12aug_20210901_164838.log.json | |
| Paper: | |
| Title: Interlaced Sparse Self-Attention for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.12273 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58 | |
| Framework: PyTorch | |
| - Name: isanet_r50-d8_4xb4-40k_voc12aug-512x512 | |
| In Collection: ISANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 76.2 | |
| mIoU(ms+flip): 77.22 | |
| Config: configs/isanet/isanet_r50-d8_4xb4-40k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D8 | |
| - ISANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 5.9 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_40k_voc12aug/isanet_r50-d8_512x512_40k_voc12aug_20210901_151349-7d08a54e.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_40k_voc12aug/isanet_r50-d8_512x512_40k_voc12aug_20210901_151349.log.json | |
| Paper: | |
| Title: Interlaced Sparse Self-Attention for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.12273 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58 | |
| Framework: PyTorch | |
| - Name: isanet_r101-d8_4xb4-20k_voc12aug-512x512 | |
| In Collection: ISANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 78.46 | |
| mIoU(ms+flip): 79.16 | |
| Config: configs/isanet/isanet_r101-d8_4xb4-20k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - ISANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 9.465 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_20k_voc12aug/isanet_r101-d8_512x512_20k_voc12aug_20210901_115805-3ccbf355.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_20k_voc12aug/isanet_r101-d8_512x512_20k_voc12aug_20210901_115805.log.json | |
| Paper: | |
| Title: Interlaced Sparse Self-Attention for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.12273 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58 | |
| Framework: PyTorch | |
| - Name: isanet_r101-d8_4xb4-40k_voc12aug-512x512 | |
| In Collection: ISANet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 78.12 | |
| mIoU(ms+flip): 79.04 | |
| Config: configs/isanet/isanet_r101-d8_4xb4-40k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - ISANet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 9.465 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_40k_voc12aug/isanet_r101-d8_512x512_40k_voc12aug_20210901_145814-bc71233b.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_40k_voc12aug/isanet_r101-d8_512x512_40k_voc12aug_20210901_145814.log.json | |
| Paper: | |
| Title: Interlaced Sparse Self-Attention for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1907.12273 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58 | |
| Framework: PyTorch | |