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| Collections: | |
| - Name: CCNet | |
| License: Apache License 2.0 | |
| Metadata: | |
| Training Data: | |
| - Cityscapes | |
| - ADE20K | |
| - Pascal VOC 2012 + Aug | |
| Paper: | |
| Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' | |
| URL: https://arxiv.org/abs/1811.11721 | |
| README: configs/ccnet/README.md | |
| Frameworks: | |
| - PyTorch | |
| Models: | |
| - Name: ccnet_r50-d8_4xb2-40k_cityscapes-512x1024 | |
| In Collection: CCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 77.76 | |
| mIoU(ms+flip): 78.87 | |
| Config: configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - CCNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 6.0 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517-4123f401.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517.log.json | |
| Paper: | |
| Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' | |
| URL: https://arxiv.org/abs/1811.11721 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: ccnet_r101-d8_4xb2-40k_cityscapes-512x1024 | |
| In Collection: CCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 76.35 | |
| mIoU(ms+flip): 78.19 | |
| Config: configs/ccnet/ccnet_r101-d8_4xb2-40k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - CCNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 9.5 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540-a3b84ba6.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540.log.json | |
| Paper: | |
| Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' | |
| URL: https://arxiv.org/abs/1811.11721 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: ccnet_r50-d8_4xb2-40k_cityscapes-769x769 | |
| In Collection: CCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 78.46 | |
| mIoU(ms+flip): 79.93 | |
| Config: configs/ccnet/ccnet_r50-d8_4xb2-40k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - CCNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 6.8 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125-76d11884.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125.log.json | |
| Paper: | |
| Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' | |
| URL: https://arxiv.org/abs/1811.11721 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: ccnet_r101-d8_4xb2-40k_cityscapes-769x769 | |
| In Collection: CCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 76.94 | |
| mIoU(ms+flip): 78.62 | |
| Config: configs/ccnet/ccnet_r101-d8_4xb2-40k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - CCNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 10.7 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428-4f57c8d0.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428.log.json | |
| Paper: | |
| Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' | |
| URL: https://arxiv.org/abs/1811.11721 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: ccnet_r50-d8_4xb2-80k_cityscapes-512x1024 | |
| In Collection: CCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 79.03 | |
| mIoU(ms+flip): 80.16 | |
| Config: configs/ccnet/ccnet_r50-d8_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - CCNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421-869a3423.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421.log.json | |
| Paper: | |
| Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' | |
| URL: https://arxiv.org/abs/1811.11721 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: ccnet_r101-d8_4xb2-80k_cityscapes-512x1024 | |
| In Collection: CCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 78.87 | |
| mIoU(ms+flip): 79.9 | |
| Config: configs/ccnet/ccnet_r101-d8_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - CCNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935-ffae8917.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935.log.json | |
| Paper: | |
| Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' | |
| URL: https://arxiv.org/abs/1811.11721 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: ccnet_r50-d8_4xb2-80k_cityscapes-769x769 | |
| In Collection: CCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 79.29 | |
| mIoU(ms+flip): 81.08 | |
| Config: configs/ccnet/ccnet_r50-d8_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - CCNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421-73eed8ca.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421.log.json | |
| Paper: | |
| Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' | |
| URL: https://arxiv.org/abs/1811.11721 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: ccnet_r101-d8_4xb2-80k_cityscapes-769x769 | |
| In Collection: CCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 79.45 | |
| mIoU(ms+flip): 80.66 | |
| Config: configs/ccnet/ccnet_r101-d8_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - CCNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502-ad3cd481.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502.log.json | |
| Paper: | |
| Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' | |
| URL: https://arxiv.org/abs/1811.11721 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: ccnet_r50-d8_4xb4-80k_ade20k-512x512 | |
| In Collection: CCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 41.78 | |
| mIoU(ms+flip): 42.98 | |
| Config: configs/ccnet/ccnet_r50-d8_4xb4-80k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D8 | |
| - CCNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 8.8 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848-aa37f61e.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848.log.json | |
| Paper: | |
| Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' | |
| URL: https://arxiv.org/abs/1811.11721 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: ccnet_r101-d8_4xb4-80k_ade20k-512x512 | |
| In Collection: CCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 43.97 | |
| mIoU(ms+flip): 45.13 | |
| Config: configs/ccnet/ccnet_r101-d8_4xb4-80k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - CCNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 12.2 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848-1f4929a3.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848.log.json | |
| Paper: | |
| Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' | |
| URL: https://arxiv.org/abs/1811.11721 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: ccnet_r50-d8_4xb4-160k_ade20k-512x512 | |
| In Collection: CCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 42.08 | |
| mIoU(ms+flip): 43.13 | |
| Config: configs/ccnet/ccnet_r50-d8_4xb4-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D8 | |
| - CCNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435-7c97193b.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435.log.json | |
| Paper: | |
| Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' | |
| URL: https://arxiv.org/abs/1811.11721 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: ccnet_r101-d8_4xb4-160k_ade20k-512x512 | |
| In Collection: CCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 43.71 | |
| mIoU(ms+flip): 45.04 | |
| Config: configs/ccnet/ccnet_r101-d8_4xb4-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - CCNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644-e849e007.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644.log.json | |
| Paper: | |
| Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' | |
| URL: https://arxiv.org/abs/1811.11721 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: ccnet_r50-d8_4xb4-20k_voc12aug-512x512 | |
| In Collection: CCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 76.17 | |
| mIoU(ms+flip): 77.51 | |
| Config: configs/ccnet/ccnet_r50-d8_4xb4-20k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D8 | |
| - CCNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 6.0 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212-fad81784.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212.log.json | |
| Paper: | |
| Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' | |
| URL: https://arxiv.org/abs/1811.11721 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: ccnet_r101-d8_4xb4-20k_voc12aug-512x512 | |
| In Collection: CCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 77.27 | |
| mIoU(ms+flip): 79.02 | |
| Config: configs/ccnet/ccnet_r101-d8_4xb4-20k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - CCNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 9.5 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212-0007b61d.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212.log.json | |
| Paper: | |
| Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' | |
| URL: https://arxiv.org/abs/1811.11721 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: ccnet_r50-d8_4xb4-40k_voc12aug-512x512 | |
| In Collection: CCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 75.96 | |
| mIoU(ms+flip): 77.04 | |
| Config: configs/ccnet/ccnet_r50-d8_4xb4-40k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D8 | |
| - CCNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127-c2a15f02.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127.log.json | |
| Paper: | |
| Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' | |
| URL: https://arxiv.org/abs/1811.11721 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: ccnet_r101-d8_4xb4-40k_voc12aug-512x512 | |
| In Collection: CCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 77.87 | |
| mIoU(ms+flip): 78.9 | |
| Config: configs/ccnet/ccnet_r101-d8_4xb4-40k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - CCNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127-c30da577.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127.log.json | |
| Paper: | |
| Title: 'CCNet: Criss-Cross Attention for Semantic Segmentation' | |
| URL: https://arxiv.org/abs/1811.11721 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |