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mmsegmentation/configs/ocrnet/ocrnet.yml

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Collections:
- Metadata:
Training Data:
- Cityscapes
- ' HRNet backbone'
- ' ResNet backbone'
- ADE20K
- Pascal VOC 2012 + Aug
Name: ocrnet
Models:
- Config: configs/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 95.69
lr schd: 40000
memory (GB): 3.5
Name: ocrnet_hr18s_512x1024_40k_cityscapes
Results:
Dataset: ' HRNet backbone'
Metrics:
mIoU: 74.3
mIoU(ms+flip): 75.95
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes/ocrnet_hr18s_512x1024_40k_cityscapes_20200601_033304-fa2436c2.pth
- Config: configs/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 133.33
lr schd: 40000
memory (GB): 4.7
Name: ocrnet_hr18_512x1024_40k_cityscapes
Results:
Dataset: ' HRNet backbone'
Metrics:
mIoU: 77.72
mIoU(ms+flip): 79.49
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320-401c5bdd.pth
- Config: configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 236.97
lr schd: 40000
memory (GB): 8.0
Name: ocrnet_hr48_512x1024_40k_cityscapes
Results:
Dataset: ' HRNet backbone'
Metrics:
mIoU: 80.58
mIoU(ms+flip): 81.79
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336-55b32491.pth
- Config: configs/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,1024)
lr schd: 80000
Name: ocrnet_hr18s_512x1024_80k_cityscapes
Results:
Dataset: ' HRNet backbone'
Metrics:
mIoU: 77.16
mIoU(ms+flip): 78.66
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735-55979e63.pth
- Config: configs/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,1024)
lr schd: 80000
Name: ocrnet_hr18_512x1024_80k_cityscapes
Results:
Dataset: ' HRNet backbone'
Metrics:
mIoU: 78.57
mIoU(ms+flip): 80.46
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521-c2e1dd4a.pth
- Config: configs/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,1024)
lr schd: 80000
Name: ocrnet_hr48_512x1024_80k_cityscapes
Results:
Dataset: ' HRNet backbone'
Metrics:
mIoU: 80.7
mIoU(ms+flip): 81.87
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752-9076bcdf.pth
- Config: configs/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,1024)
lr schd: 160000
Name: ocrnet_hr18s_512x1024_160k_cityscapes
Results:
Dataset: ' HRNet backbone'
Metrics:
mIoU: 78.45
mIoU(ms+flip): 79.97
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005-f4a7af28.pth
- Config: configs/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,1024)
lr schd: 160000
Name: ocrnet_hr18_512x1024_160k_cityscapes
Results:
Dataset: ' HRNet backbone'
Metrics:
mIoU: 79.47
mIoU(ms+flip): 80.91
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001-b9172d0c.pth
- Config: configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,1024)
lr schd: 160000
Name: ocrnet_hr48_512x1024_160k_cityscapes
Results:
Dataset: ' HRNet backbone'
Metrics:
mIoU: 81.35
mIoU(ms+flip): 82.7
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037-dfbf1b0c.pth
- Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py
In Collection: ocrnet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 40000
Name: ocrnet_r101-d8_512x1024_40k_b8_cityscapes
Results:
Dataset: ' ResNet backbone'
Metrics:
mIoU: 80.09
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes/ocrnet_r101-d8_512x1024_40k_b8_cityscapes-02ac0f13.pth
- Config: configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes.py
In Collection: ocrnet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 331.13
lr schd: 40000
memory (GB): 8.8
Name: ocrnet_r101-d8_512x1024_40k_b16_cityscapes
Results:
Dataset: ' ResNet backbone'
Metrics:
mIoU: 80.3
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes/ocrnet_r101-d8_512x1024_40k_b16_cityscapes-db500f80.pth
- Config: configs/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes.py
In Collection: ocrnet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 331.13
lr schd: 80000
memory (GB): 8.8
Name: ocrnet_r101-d8_512x1024_80k_b16_cityscapes
Results:
Dataset: ' ResNet backbone'
Metrics:
mIoU: 80.81
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes/ocrnet_r101-d8_512x1024_80k_b16_cityscapes-78688424.pth
- Config: configs/ocrnet/ocrnet_hr18s_512x512_80k_ade20k.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 34.51
lr schd: 80000
memory (GB): 6.7
Name: ocrnet_hr18s_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 35.06
mIoU(ms+flip): 35.8
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600-e80b62af.pth
- Config: configs/ocrnet/ocrnet_hr18_512x512_80k_ade20k.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 52.83
lr schd: 80000
memory (GB): 7.9
Name: ocrnet_hr18_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 37.79
mIoU(ms+flip): 39.16
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157-d173d83b.pth
- Config: configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 58.86
lr schd: 80000
memory (GB): 11.2
Name: ocrnet_hr48_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 43.0
mIoU(ms+flip): 44.3
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518-d168c2d1.pth
- Config: configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
lr schd: 160000
Name: ocrnet_hr18s_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 37.19
mIoU(ms+flip): 38.4
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505-8e913058.pth
- Config: configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
lr schd: 160000
Name: ocrnet_hr18_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 39.32
mIoU(ms+flip): 40.8
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940-d8fcd9d1.pth
- Config: configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
lr schd: 160000
Name: ocrnet_hr48_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 43.25
mIoU(ms+flip): 44.88
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705-a073726d.pth
- Config: configs/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 31.7
lr schd: 20000
memory (GB): 3.5
Name: ocrnet_hr18s_512x512_20k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 71.7
mIoU(ms+flip): 73.84
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913-02b04fcb.pth
- Config: configs/ocrnet/ocrnet_hr18_512x512_20k_voc12aug.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 50.23
lr schd: 20000
memory (GB): 4.7
Name: ocrnet_hr18_512x512_20k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 74.75
mIoU(ms+flip): 77.11
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932-8954cbb7.pth
- Config: configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 56.09
lr schd: 20000
memory (GB): 8.1
Name: ocrnet_hr48_512x512_20k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.72
mIoU(ms+flip): 79.87
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932-9e82080a.pth
- Config: configs/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18-Small
crop size: (512,512)
lr schd: 40000
Name: ocrnet_hr18s_512x512_40k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 72.76
mIoU(ms+flip): 74.6
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025-42b587ac.pth
- Config: configs/ocrnet/ocrnet_hr18_512x512_40k_voc12aug.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W18
crop size: (512,512)
lr schd: 40000
Name: ocrnet_hr18_512x512_40k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 74.98
mIoU(ms+flip): 77.4
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958-714302be.pth
- Config: configs/ocrnet/ocrnet_hr48_512x512_40k_voc12aug.py
In Collection: ocrnet
Metadata:
backbone: HRNetV2p-W48
crop size: (512,512)
lr schd: 40000
Name: ocrnet_hr48_512x512_40k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 77.14
mIoU(ms+flip): 79.71
Task: Semantic Segmentation
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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