| Collections: | |
| - Name: APCNet | |
| License: Apache License 2.0 | |
| Metadata: | |
| Training Data: | |
| - Cityscapes | |
| - ADE20K | |
| Paper: | |
| Title: Adaptive Pyramid Context Network for Semantic Segmentation | |
| URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html | |
| README: configs/apcnet/README.md | |
| Frameworks: | |
| - PyTorch | |
| Models: | |
| - Name: apcnet_r50-d8_4xb2-40k_cityscapes-512x1024 | |
| In Collection: APCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 78.02 | |
| mIoU(ms+flip): 79.26 | |
| Config: configs/apcnet/apcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - APCNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 7.7 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes-20201214_115717.log.json | |
| Paper: | |
| Title: Adaptive Pyramid Context Network for Semantic Segmentation | |
| URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: apcnet_r101-d8_4xb2-40k_cityscapes-512x1024 | |
| In Collection: APCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 79.08 | |
| mIoU(ms+flip): 80.34 | |
| Config: configs/apcnet/apcnet_r101-d8_4xb2-40k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - APCNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 11.2 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes-20201214_115716.log.json | |
| Paper: | |
| Title: Adaptive Pyramid Context Network for Semantic Segmentation | |
| URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: apcnet_r50-d8_4xb2-40k_cityscapes-769x769 | |
| In Collection: APCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 77.89 | |
| mIoU(ms+flip): 79.75 | |
| Config: configs/apcnet/apcnet_r50-d8_4xb2-40k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - APCNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 8.7 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes-20201214_115717.log.json | |
| Paper: | |
| Title: Adaptive Pyramid Context Network for Semantic Segmentation | |
| URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: apcnet_r101-d8_4xb2-40k_cityscapes-769x769 | |
| In Collection: APCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 77.96 | |
| mIoU(ms+flip): 79.24 | |
| Config: configs/apcnet/apcnet_r101-d8_4xb2-40k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - APCNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 12.7 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes-20201214_115718.log.json | |
| Paper: | |
| Title: Adaptive Pyramid Context Network for Semantic Segmentation | |
| URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: apcnet_r50-d8_4xb2-80k_cityscapes-512x1024 | |
| In Collection: APCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 78.96 | |
| mIoU(ms+flip): 79.94 | |
| Config: configs/apcnet/apcnet_r50-d8_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - APCNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes-20201214_115716.log.json | |
| Paper: | |
| Title: Adaptive Pyramid Context Network for Semantic Segmentation | |
| URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: apcnet_r101-d8_4xb2-80k_cityscapes-512x1024 | |
| In Collection: APCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 79.64 | |
| mIoU(ms+flip): 80.61 | |
| Config: configs/apcnet/apcnet_r101-d8_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - APCNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes-20201214_115705.log.json | |
| Paper: | |
| Title: Adaptive Pyramid Context Network for Semantic Segmentation | |
| URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: apcnet_r50-d8_4xb2-80k_cityscapes-769x769 | |
| In Collection: APCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 78.79 | |
| mIoU(ms+flip): 80.35 | |
| Config: configs/apcnet/apcnet_r50-d8_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-50-D8 | |
| - APCNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes-20201214_115718.log.json | |
| Paper: | |
| Title: Adaptive Pyramid Context Network for Semantic Segmentation | |
| URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: apcnet_r101-d8_4xb2-80k_cityscapes-769x769 | |
| In Collection: APCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Cityscapes | |
| Metrics: | |
| mIoU: 78.45 | |
| mIoU(ms+flip): 79.91 | |
| Config: configs/apcnet/apcnet_r101-d8_4xb2-80k_cityscapes-769x769.py | |
| Metadata: | |
| Training Data: Cityscapes | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - APCNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes-20201214_115716.log.json | |
| Paper: | |
| Title: Adaptive Pyramid Context Network for Semantic Segmentation | |
| URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: apcnet_r50-d8_4xb4-80k_ade20k-512x512 | |
| In Collection: APCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 42.2 | |
| mIoU(ms+flip): 43.3 | |
| Config: configs/apcnet/apcnet_r50-d8_4xb4-80k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D8 | |
| - APCNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 10.1 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k-20201214_115705.log.json | |
| Paper: | |
| Title: Adaptive Pyramid Context Network for Semantic Segmentation | |
| URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: apcnet_r101-d8_4xb4-80k_ade20k-512x512 | |
| In Collection: APCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 45.54 | |
| mIoU(ms+flip): 46.65 | |
| Config: configs/apcnet/apcnet_r101-d8_4xb4-80k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - APCNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 13.6 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k-20201214_115704.log.json | |
| Paper: | |
| Title: Adaptive Pyramid Context Network for Semantic Segmentation | |
| URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: apcnet_r50-d8_4xb4-160k_ade20k-512x512 | |
| In Collection: APCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 43.4 | |
| mIoU(ms+flip): 43.94 | |
| Config: configs/apcnet/apcnet_r50-d8_4xb4-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-50-D8 | |
| - APCNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k-20201214_115706.log.json | |
| Paper: | |
| Title: Adaptive Pyramid Context Network for Semantic Segmentation | |
| URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |
| - Name: apcnet_r101-d8_4xb4-160k_ade20k-512x512 | |
| In Collection: APCNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 45.41 | |
| mIoU(ms+flip): 46.63 | |
| Config: configs/apcnet/apcnet_r101-d8_4xb4-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - APCNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k-20201214_115705.log.json | |
| Paper: | |
| Title: Adaptive Pyramid Context Network for Semantic Segmentation | |
| URL: https://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/apc_head.py#L111 | |
| Framework: PyTorch | |