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- Collections:
- - Name: OCRNet
- Metadata:
- Training Data:
- - Cityscapes
- - ADE20K
- - Pascal VOC 2012 + Aug
- Paper:
- URL: https://arxiv.org/abs/1909.11065
- Title: Object-Contextual Representations for Semantic Segmentation
- README: configs/ocrnet/README.md
- Code:
- URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/ocr_head.py#L86
- Version: v0.17.0
- Converted From:
- Code: https://github.com/openseg-group/OCNet.pytorch
- Models:
- - Name: ocrnet_hr18s_512x1024_40k_cityscapes
- In Collection: OCRNet
- Metadata:
- backbone: HRNetV2p-W18-Small
- crop size: (512,1024)
- lr schd: 40000
- inference time (ms/im):
- - value: 95.69
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,1024)
- Training Memory (GB): 3.5
- Results:
- - Task: Semantic Segmentation
- Dataset: Cityscapes
- Metrics:
- mIoU: 74.3
- mIoU(ms+flip): 75.95
- Config: configs/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes/ocrnet_hr18s_512x1024_40k_cityscapes_20200601_033304-fa2436c2.pth
- - Name: ocrnet_hr18_512x1024_40k_cityscapes
- In Collection: OCRNet
- Metadata:
- backbone: HRNetV2p-W18
- crop size: (512,1024)
- lr schd: 40000
- inference time (ms/im):
- - value: 133.33
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,1024)
- Training Memory (GB): 4.7
- Results:
- - Task: Semantic Segmentation
- Dataset: Cityscapes
- Metrics:
- mIoU: 77.72
- mIoU(ms+flip): 79.49
- Config: configs/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320-401c5bdd.pth
- - Name: ocrnet_hr48_512x1024_40k_cityscapes
- In Collection: OCRNet
- Metadata:
- backbone: HRNetV2p-W48
- crop size: (512,1024)
- lr schd: 40000
- inference time (ms/im):
- - value: 236.97
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,1024)
- Training Memory (GB): 8.0
- Results:
- - Task: Semantic Segmentation
- Dataset: Cityscapes
- Metrics:
- mIoU: 80.58
- mIoU(ms+flip): 81.79
- Config: configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336-55b32491.pth
- - Name: ocrnet_hr18s_512x1024_80k_cityscapes
- In Collection: OCRNet
- Metadata:
- backbone: HRNetV2p-W18-Small
- crop size: (512,1024)
- lr schd: 80000
- Results:
- - Task: Semantic Segmentation
- Dataset: Cityscapes
- Metrics:
- mIoU: 77.16
- mIoU(ms+flip): 78.66
- Config: configs/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735-55979e63.pth
- - Name: ocrnet_hr18_512x1024_80k_cityscapes
- In Collection: OCRNet
- Metadata:
- backbone: HRNetV2p-W18
- crop size: (512,1024)
- lr schd: 80000
- Results:
- - Task: Semantic Segmentation
- Dataset: Cityscapes
- Metrics:
- mIoU: 78.57
- mIoU(ms+flip): 80.46
- Config: configs/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521-c2e1dd4a.pth
- - Name: ocrnet_hr48_512x1024_80k_cityscapes
- In Collection: OCRNet
- Metadata:
- backbone: HRNetV2p-W48
- crop size: (512,1024)
- lr schd: 80000
- Results:
- - Task: Semantic Segmentation
- Dataset: Cityscapes
- Metrics:
- mIoU: 80.7
- mIoU(ms+flip): 81.87
- Config: configs/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752-9076bcdf.pth
- - Name: ocrnet_hr18s_512x1024_160k_cityscapes
- In Collection: OCRNet
- Metadata:
- backbone: HRNetV2p-W18-Small
- crop size: (512,1024)
- lr schd: 160000
- Results:
- - Task: Semantic Segmentation
- Dataset: Cityscapes
- Metrics:
- mIoU: 78.45
- mIoU(ms+flip): 79.97
- Config: configs/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005-f4a7af28.pth
- - Name: ocrnet_hr18_512x1024_160k_cityscapes
- In Collection: OCRNet
- Metadata:
- backbone: HRNetV2p-W18
- crop size: (512,1024)
- lr schd: 160000
- Results:
- - Task: Semantic Segmentation
- Dataset: Cityscapes
- Metrics:
- mIoU: 79.47
- mIoU(ms+flip): 80.91
- Config: configs/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001-b9172d0c.pth
- - Name: ocrnet_hr48_512x1024_160k_cityscapes
- In Collection: OCRNet
- Metadata:
- backbone: HRNetV2p-W48
- crop size: (512,1024)
- lr schd: 160000
- Results:
- - Task: Semantic Segmentation
- Dataset: Cityscapes
- Metrics:
- mIoU: 81.35
- mIoU(ms+flip): 82.7
- Config: configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037-dfbf1b0c.pth
- - Name: ocrnet_r101-d8_512x1024_40k_b8_cityscapes
- In Collection: OCRNet
- Metadata:
- backbone: R-101-D8
- crop size: (512,1024)
- lr schd: 40000
- Results:
- - Task: Semantic Segmentation
- Dataset: Cityscapes
- Metrics:
- mIoU: 80.09
- Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py
- 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
- - Name: ocrnet_r101-d8_512x1024_40k_b16_cityscapes
- In Collection: OCRNet
- Metadata:
- backbone: R-101-D8
- crop size: (512,1024)
- lr schd: 40000
- inference time (ms/im):
- - value: 331.13
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,1024)
- Training Memory (GB): 8.8
- Results:
- - Task: Semantic Segmentation
- Dataset: Cityscapes
- Metrics:
- mIoU: 80.3
- Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes.py
- 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
- - Name: ocrnet_r101-d8_512x1024_80k_b16_cityscapes
- In Collection: OCRNet
- Metadata:
- backbone: R-101-D8
- crop size: (512,1024)
- lr schd: 80000
- inference time (ms/im):
- - value: 331.13
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,1024)
- Training Memory (GB): 8.8
- Results:
- - Task: Semantic Segmentation
- Dataset: Cityscapes
- Metrics:
- mIoU: 80.81
- Config: configs/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes.py
- 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
- - Name: ocrnet_hr18s_512x512_80k_ade20k
- In Collection: OCRNet
- Metadata:
- backbone: HRNetV2p-W18-Small
- crop size: (512,512)
- lr schd: 80000
- inference time (ms/im):
- - value: 34.51
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,512)
- Training Memory (GB): 6.7
- Results:
- - Task: Semantic Segmentation
- Dataset: ADE20K
- Metrics:
- mIoU: 35.06
- mIoU(ms+flip): 35.8
- Config: configs/ocrnet/ocrnet_hr18s_512x512_80k_ade20k.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600-e80b62af.pth
- - Name: ocrnet_hr18_512x512_80k_ade20k
- In Collection: OCRNet
- Metadata:
- backbone: HRNetV2p-W18
- crop size: (512,512)
- lr schd: 80000
- inference time (ms/im):
- - value: 52.83
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,512)
- Training Memory (GB): 7.9
- Results:
- - Task: Semantic Segmentation
- Dataset: ADE20K
- Metrics:
- mIoU: 37.79
- mIoU(ms+flip): 39.16
- Config: configs/ocrnet/ocrnet_hr18_512x512_80k_ade20k.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157-d173d83b.pth
- - Name: ocrnet_hr48_512x512_80k_ade20k
- In Collection: OCRNet
- Metadata:
- backbone: HRNetV2p-W48
- crop size: (512,512)
- lr schd: 80000
- inference time (ms/im):
- - value: 58.86
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,512)
- Training Memory (GB): 11.2
- Results:
- - Task: Semantic Segmentation
- Dataset: ADE20K
- Metrics:
- mIoU: 43.0
- mIoU(ms+flip): 44.3
- Config: configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518-d168c2d1.pth
- - Name: ocrnet_hr18s_512x512_160k_ade20k
- In Collection: OCRNet
- Metadata:
- backbone: HRNetV2p-W18-Small
- crop size: (512,512)
- lr schd: 160000
- Results:
- - Task: Semantic Segmentation
- Dataset: ADE20K
- Metrics:
- mIoU: 37.19
- mIoU(ms+flip): 38.4
- Config: configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505-8e913058.pth
- - Name: ocrnet_hr18_512x512_160k_ade20k
- In Collection: OCRNet
- Metadata:
- backbone: HRNetV2p-W18
- crop size: (512,512)
- lr schd: 160000
- Results:
- - Task: Semantic Segmentation
- Dataset: ADE20K
- Metrics:
- mIoU: 39.32
- mIoU(ms+flip): 40.8
- Config: configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940-d8fcd9d1.pth
- - Name: ocrnet_hr48_512x512_160k_ade20k
- In Collection: OCRNet
- Metadata:
- backbone: HRNetV2p-W48
- crop size: (512,512)
- lr schd: 160000
- Results:
- - Task: Semantic Segmentation
- Dataset: ADE20K
- Metrics:
- mIoU: 43.25
- mIoU(ms+flip): 44.88
- Config: configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705-a073726d.pth
- - Name: ocrnet_hr18s_512x512_20k_voc12aug
- In Collection: OCRNet
- Metadata:
- backbone: HRNetV2p-W18-Small
- crop size: (512,512)
- lr schd: 20000
- inference time (ms/im):
- - value: 31.7
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,512)
- Training Memory (GB): 3.5
- Results:
- - Task: Semantic Segmentation
- Dataset: Pascal VOC 2012 + Aug
- Metrics:
- mIoU: 71.7
- mIoU(ms+flip): 73.84
- Config: configs/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913-02b04fcb.pth
- - Name: ocrnet_hr18_512x512_20k_voc12aug
- In Collection: OCRNet
- Metadata:
- backbone: HRNetV2p-W18
- crop size: (512,512)
- lr schd: 20000
- inference time (ms/im):
- - value: 50.23
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,512)
- Training Memory (GB): 4.7
- Results:
- - Task: Semantic Segmentation
- Dataset: Pascal VOC 2012 + Aug
- Metrics:
- mIoU: 74.75
- mIoU(ms+flip): 77.11
- Config: configs/ocrnet/ocrnet_hr18_512x512_20k_voc12aug.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932-8954cbb7.pth
- - Name: ocrnet_hr48_512x512_20k_voc12aug
- In Collection: OCRNet
- Metadata:
- backbone: HRNetV2p-W48
- crop size: (512,512)
- lr schd: 20000
- inference time (ms/im):
- - value: 56.09
- hardware: V100
- backend: PyTorch
- batch size: 1
- mode: FP32
- resolution: (512,512)
- Training Memory (GB): 8.1
- Results:
- - Task: Semantic Segmentation
- Dataset: Pascal VOC 2012 + Aug
- Metrics:
- mIoU: 77.72
- mIoU(ms+flip): 79.87
- Config: configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932-9e82080a.pth
- - Name: ocrnet_hr18s_512x512_40k_voc12aug
- In Collection: OCRNet
- Metadata:
- backbone: HRNetV2p-W18-Small
- crop size: (512,512)
- lr schd: 40000
- Results:
- - Task: Semantic Segmentation
- Dataset: Pascal VOC 2012 + Aug
- Metrics:
- mIoU: 72.76
- mIoU(ms+flip): 74.6
- Config: configs/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025-42b587ac.pth
- - Name: ocrnet_hr18_512x512_40k_voc12aug
- In Collection: OCRNet
- Metadata:
- backbone: HRNetV2p-W18
- crop size: (512,512)
- lr schd: 40000
- Results:
- - Task: Semantic Segmentation
- Dataset: Pascal VOC 2012 + Aug
- Metrics:
- mIoU: 74.98
- mIoU(ms+flip): 77.4
- Config: configs/ocrnet/ocrnet_hr18_512x512_40k_voc12aug.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958-714302be.pth
- - Name: ocrnet_hr48_512x512_40k_voc12aug
- In Collection: OCRNet
- Metadata:
- backbone: HRNetV2p-W48
- crop size: (512,512)
- lr schd: 40000
- Results:
- - Task: Semantic Segmentation
- Dataset: Pascal VOC 2012 + Aug
- Metrics:
- mIoU: 77.14
- mIoU(ms+flip): 79.71
- Config: configs/ocrnet/ocrnet_hr48_512x512_40k_voc12aug.py
- Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958-255bc5ce.pth
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