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point_rend.yml
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point_rend.yml
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Collections:
- Name: PointRend
Metadata:
Training Data:
- Cityscapes
- ADE20K
Paper:
URL: https://arxiv.org/abs/1912.08193
Title: 'PointRend: Image Segmentation as Rendering'
README: configs/point_rend/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/point_head.py#L36
Version: v0.17.0
Converted From:
Code: https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend
Models:
- Name: pointrend_r50_512x1024_80k_cityscapes
In Collection: PointRend
Metadata:
backbone: R-50
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 117.92
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 3.1
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.47
mIoU(ms+flip): 78.13
Config: configs/point_rend/pointrend_r50_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes_20200711_015821-bb1ff523.pth
- Name: pointrend_r101_512x1024_80k_cityscapes
In Collection: PointRend
Metadata:
backbone: R-101
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 142.86
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 4.2
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.3
mIoU(ms+flip): 79.97
Config: configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes_20200711_170850-d0ca84be.pth
- Name: pointrend_r50_512x512_160k_ade20k
In Collection: PointRend
Metadata:
backbone: R-50
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 57.77
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 5.1
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 37.64
mIoU(ms+flip): 39.17
Config: configs/point_rend/pointrend_r50_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k_20200807_232644-ac3febf2.pth
- Name: pointrend_r101_512x512_160k_ade20k
In Collection: PointRend
Metadata:
backbone: R-101
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 64.52
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.1
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 40.02
mIoU(ms+flip): 41.6
Config: configs/point_rend/pointrend_r101_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k_20200808_030852-8834902a.pth