mirrors
/
mmsegmentation
mirror of https://github.com/open-mmlab/mmsegmentation.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity
mmsegmentation/configs/ocrnet/ocrnet.yml

439 lines
15 KiB
YAML
Raw Blame History

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_4xb2-40k_cityscapes-512x1024
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_4xb2-40k_cityscapes-512x1024.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_4xb2-40k_cityscapes-512x1024
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_4xb2-40k_cityscapes-512x1024.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_4xb2-40k_cityscapes-512x1024
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_4xb2-40k_cityscapes-512x1024.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_4xb2-80k_cityscapes-512x1024
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_4xb2-80k_cityscapes-512x1024.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_4xb2-80k_cityscapes-512x1024
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_4xb2-80k_cityscapes-512x1024.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_4xb2-80k_cityscapes-512x1024
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_4xb2-80k_cityscapes-512x1024.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_4xb2-160k_cityscapes-512x1024
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_4xb2-160k_cityscapes-512x1024.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_4xb2-160k_cityscapes-512x1024
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_4xb2-160k_cityscapes-512x1024.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_4xb2-160k_cityscapes-512x1024
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_4xb2-160k_cityscapes-512x1024.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_4xb2-40k_cityscapes-512x1024
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_4xb2-40k_cityscapes-512x1024.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_8xb2-40k_cityscapes-512x1024
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_8xb2-40k_cityscapes-512x1024.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_8xb2-80k_cityscapes-512x1024
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_8xb2-80k_cityscapes-512x1024.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_4xb4-80k_ade20k-512x512
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_4xb4-80k_ade20k-512x512.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_4xb4-80k_ade20k-512x512
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_4xb4-80k_ade20k-512x512.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_4xb4-80k_ade20k-512x512
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_4xb4-80k_ade20k-512x512.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_4xb4-80k_ade20k-512x512
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_4xb4-80k_ade20k-512x512.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_4xb4-80k_ade20k-512x512
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_4xb4-80k_ade20k-512x512.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_4xb4-160k_ade20k-512x512
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_4xb4-160k_ade20k-512x512.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_4xb4-20k_voc12aug-512x512
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_4xb4-20k_voc12aug-512x512.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_4xb4-20k_voc12aug-512x512
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_4xb4-20k_voc12aug-512x512.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_4xb4-20k_voc12aug-512x512
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_4xb4-20k_voc12aug-512x512.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_4xb4-40k_voc12aug-512x512
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_4xb4-40k_voc12aug-512x512.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_4xb4-40k_voc12aug-512x512
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_4xb4-40k_voc12aug-512x512.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_4xb4-40k_voc12aug-512x512
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_4xb4-40k_voc12aug-512x512.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958-255bc5ce.pth
Reference in New Issue View Git Blame Copy Permalink