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| Collections: | |
| - Name: OCRNet | |
| License: Apache License 2.0 | |
| Metadata: | |
| Training Data: | |
| - Cityscapes | |
| - '# HRNet backbone' | |
| - '# ResNet backbone' | |
| - ADE20K | |
| - Pascal VOC 2012 + Aug | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| README: configs/ocrnet/README.md | |
| Frameworks: | |
| - PyTorch | |
| Models: | |
| - Name: ocrnet_hr18s_4xb2-40k_cityscapes-512x1024 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: '# HRNet backbone' | |
| Metrics: | |
| mIoU: 76.61 | |
| mIoU(ms+flip): 78.01 | |
| Config: configs/ocrnet/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: '# HRNet backbone' | |
| Batch Size: 8 | |
| Architecture: | |
| - HRNetV2p-W18-Small | |
| - OCRNet | |
| Training Resources: 4x A100 GPUS | |
| Memory (GB): 3.5 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024_20230227_145026-6c052a14.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024_20230227_145026.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_hr18_4xb2-40k_cityscapes-512x1024 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: '# HRNet backbone' | |
| Metrics: | |
| mIoU: 77.72 | |
| mIoU(ms+flip): 79.49 | |
| Config: configs/ocrnet/ocrnet_hr18_4xb2-40k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: '# HRNet backbone' | |
| Batch Size: 8 | |
| Architecture: | |
| - HRNetV2p-W18 | |
| - OCRNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 4.7 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320-401c5bdd.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_hr48_4xb2-40k_cityscapes-512x1024 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: '# HRNet backbone' | |
| Metrics: | |
| mIoU: 80.58 | |
| mIoU(ms+flip): 81.79 | |
| Config: configs/ocrnet/ocrnet_hr48_4xb2-40k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: '# HRNet backbone' | |
| Batch Size: 8 | |
| Architecture: | |
| - HRNetV2p-W48 | |
| - OCRNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 8.0 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336-55b32491.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_hr18s_4xb2-80k_cityscapes-512x1024 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: '# HRNet backbone' | |
| Metrics: | |
| mIoU: 77.16 | |
| mIoU(ms+flip): 78.66 | |
| Config: configs/ocrnet/ocrnet_hr18s_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: '# HRNet backbone' | |
| Batch Size: 8 | |
| Architecture: | |
| - HRNetV2p-W18-Small | |
| - OCRNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735-55979e63.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_hr18_4xb2-80k_cityscapes-512x1024 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: '# HRNet backbone' | |
| Metrics: | |
| mIoU: 78.57 | |
| mIoU(ms+flip): 80.46 | |
| Config: configs/ocrnet/ocrnet_hr18_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: '# HRNet backbone' | |
| Batch Size: 8 | |
| Architecture: | |
| - HRNetV2p-W18 | |
| - OCRNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521-c2e1dd4a.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_hr48_4xb2-80k_cityscapes-512x1024 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: '# HRNet backbone' | |
| Metrics: | |
| mIoU: 80.7 | |
| mIoU(ms+flip): 81.87 | |
| Config: configs/ocrnet/ocrnet_hr48_4xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: '# HRNet backbone' | |
| Batch Size: 8 | |
| Architecture: | |
| - HRNetV2p-W48 | |
| - OCRNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752-9076bcdf.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_hr18s_4xb2-160k_cityscapes-512x1024 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: '# HRNet backbone' | |
| Metrics: | |
| mIoU: 78.45 | |
| mIoU(ms+flip): 79.97 | |
| Config: configs/ocrnet/ocrnet_hr18s_4xb2-160k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: '# HRNet backbone' | |
| Batch Size: 8 | |
| Architecture: | |
| - HRNetV2p-W18-Small | |
| - OCRNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005-f4a7af28.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_hr18_4xb2-160k_cityscapes-512x1024 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: '# HRNet backbone' | |
| Metrics: | |
| mIoU: 79.47 | |
| mIoU(ms+flip): 80.91 | |
| Config: configs/ocrnet/ocrnet_hr18_4xb2-160k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: '# HRNet backbone' | |
| Batch Size: 8 | |
| Architecture: | |
| - HRNetV2p-W18 | |
| - OCRNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001-b9172d0c.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_hr48_4xb2-160k_cityscapes-512x1024 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: '# HRNet backbone' | |
| Metrics: | |
| mIoU: 81.35 | |
| mIoU(ms+flip): 82.7 | |
| Config: configs/ocrnet/ocrnet_hr48_4xb2-160k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: '# HRNet backbone' | |
| Batch Size: 8 | |
| Architecture: | |
| - HRNetV2p-W48 | |
| - OCRNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037-dfbf1b0c.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_r101-d8_4xb2-40k_cityscapes-512x1024 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: '# ResNet backbone' | |
| Metrics: | |
| mIoU: 80.09 | |
| Config: configs/ocrnet/ocrnet_r101-d8_4xb2-40k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: '# ResNet backbone' | |
| Batch Size: 8 | |
| Architecture: | |
| - R-101-D8 | |
| - OCRNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes/ocrnet_r101-d8_512x1024_40k_b8_cityscapes_20200717_110721-02ac0f13.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes/ocrnet_r101-d8_512x1024_40k_b8_cityscapes_20200717_110721.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_r101-d8_8xb2-40k_cityscapes-512x1024 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: '# ResNet backbone' | |
| Metrics: | |
| mIoU: 80.3 | |
| Config: configs/ocrnet/ocrnet_r101-d8_8xb2-40k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: '# ResNet backbone' | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - OCRNet | |
| Training Resources: 8x V100 GPUS | |
| Memory (GB): 8.8 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes/ocrnet_r101-d8_512x1024_40k_b16_cityscapes_20200723_193726-db500f80.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes/ocrnet_r101-d8_512x1024_40k_b16_cityscapes_20200723_193726.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_r101-d8_8xb2-80k_cityscapes-512x1024 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: '# ResNet backbone' | |
| Metrics: | |
| mIoU: 80.81 | |
| Config: configs/ocrnet/ocrnet_r101-d8_8xb2-80k_cityscapes-512x1024.py | |
| Metadata: | |
| Training Data: '# ResNet backbone' | |
| Batch Size: 16 | |
| Architecture: | |
| - R-101-D8 | |
| - OCRNet | |
| Training Resources: 8x V100 GPUS | |
| Memory (GB): 8.8 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes/ocrnet_r101-d8_512x1024_80k_b16_cityscapes_20200723_192421-78688424.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes/ocrnet_r101-d8_512x1024_80k_b16_cityscapes_20200723_192421.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_hr18s_4xb4-80k_ade20k-512x512 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 35.06 | |
| mIoU(ms+flip): 35.8 | |
| Config: configs/ocrnet/ocrnet_hr18s_4xb4-80k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - HRNetV2p-W18-Small | |
| - OCRNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 6.7 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600-e80b62af.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_hr18_4xb4-80k_ade20k-512x512 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 37.79 | |
| mIoU(ms+flip): 39.16 | |
| Config: configs/ocrnet/ocrnet_hr18_4xb4-80k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - HRNetV2p-W18 | |
| - OCRNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 7.9 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157-d173d83b.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_hr48_4xb4-80k_ade20k-512x512 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 43.0 | |
| mIoU(ms+flip): 44.3 | |
| Config: configs/ocrnet/ocrnet_hr48_4xb4-80k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - HRNetV2p-W48 | |
| - OCRNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 11.2 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518-d168c2d1.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_hr18s_4xb4-80k_ade20k-512x512 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 37.19 | |
| mIoU(ms+flip): 38.4 | |
| Config: configs/ocrnet/ocrnet_hr18s_4xb4-80k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - HRNetV2p-W18-Small | |
| - OCRNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505-8e913058.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_hr18_4xb4-80k_ade20k-512x512 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 39.32 | |
| mIoU(ms+flip): 40.8 | |
| Config: configs/ocrnet/ocrnet_hr18_4xb4-80k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - HRNetV2p-W18 | |
| - OCRNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940-d8fcd9d1.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_hr48_4xb4-160k_ade20k-512x512 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: ADE20K | |
| Metrics: | |
| mIoU: 43.25 | |
| mIoU(ms+flip): 44.88 | |
| Config: configs/ocrnet/ocrnet_hr48_4xb4-160k_ade20k-512x512.py | |
| Metadata: | |
| Training Data: ADE20K | |
| Batch Size: 16 | |
| Architecture: | |
| - HRNetV2p-W48 | |
| - OCRNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705-a073726d.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_hr18s_4xb4-20k_voc12aug-512x512 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 71.7 | |
| mIoU(ms+flip): 73.84 | |
| Config: configs/ocrnet/ocrnet_hr18s_4xb4-20k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - HRNetV2p-W18-Small | |
| - OCRNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 3.5 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913-02b04fcb.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_hr18_4xb4-20k_voc12aug-512x512 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 74.75 | |
| mIoU(ms+flip): 77.11 | |
| Config: configs/ocrnet/ocrnet_hr18_4xb4-20k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - HRNetV2p-W18 | |
| - OCRNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 4.7 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932-8954cbb7.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_hr48_4xb4-20k_voc12aug-512x512 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 77.72 | |
| mIoU(ms+flip): 79.87 | |
| Config: configs/ocrnet/ocrnet_hr48_4xb4-20k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - HRNetV2p-W48 | |
| - OCRNet | |
| Training Resources: 4x V100 GPUS | |
| Memory (GB): 8.1 | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932-9e82080a.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_hr18s_4xb4-40k_voc12aug-512x512 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 72.76 | |
| mIoU(ms+flip): 74.6 | |
| Config: configs/ocrnet/ocrnet_hr18s_4xb4-40k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - HRNetV2p-W18-Small | |
| - OCRNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025-42b587ac.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_hr18_4xb4-40k_voc12aug-512x512 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 74.98 | |
| mIoU(ms+flip): 77.4 | |
| Config: configs/ocrnet/ocrnet_hr18_4xb4-40k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - HRNetV2p-W18 | |
| - OCRNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958-714302be.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |
| - Name: ocrnet_hr48_4xb4-40k_voc12aug-512x512 | |
| In Collection: OCRNet | |
| Results: | |
| Task: Semantic Segmentation | |
| Dataset: Pascal VOC 2012 + Aug | |
| Metrics: | |
| mIoU: 77.14 | |
| mIoU(ms+flip): 79.71 | |
| Config: configs/ocrnet/ocrnet_hr48_4xb4-40k_voc12aug-512x512.py | |
| Metadata: | |
| Training Data: Pascal VOC 2012 + Aug | |
| Batch Size: 16 | |
| Architecture: | |
| - HRNetV2p-W48 | |
| - OCRNet | |
| Training Resources: 4x V100 GPUS | |
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958-255bc5ce.pth | |
| Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958.log.json | |
| Paper: | |
| Title: Object-Contextual Representations for Semantic Segmentation | |
| URL: https://arxiv.org/abs/1909.11065 | |
| Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86 | |
| Framework: PyTorch | |