OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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杨培文 (Yang Peiwen)
e0cb8666a5
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3 years ago | |
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| .. | ||
| README.md | 3 years ago | |
| cornernet_hourglass104_mstest_8x6_210e_coco.py | 4 years ago | |
| cornernet_hourglass104_mstest_10x5_210e_coco.py | 4 years ago | |
| cornernet_hourglass104_mstest_32x3_210e_coco.py | 4 years ago | |
| metafile.yml | 3 years ago | |
README.md
CornerNet
Introduction
@inproceedings{law2018cornernet,
title={Cornernet: Detecting objects as paired keypoints},
author={Law, Hei and Deng, Jia},
booktitle={15th European Conference on Computer Vision, ECCV 2018},
pages={765--781},
year={2018},
organization={Springer Verlag}
}
Results and models
| Backbone | Batch Size | Step/Total Epochs | Mem (GB) | Inf time (fps) | box AP | Config | Download |
|---|---|---|---|---|---|---|---|
| HourglassNet-104 | 10 x 5 | 180/210 | 13.9 | 4.2 | 41.2 | config | model | log |
| HourglassNet-104 | 8 x 6 | 180/210 | 15.9 | 4.2 | 41.2 | config | model | log |
| HourglassNet-104 | 32 x 3 | 180/210 | 9.5 | 3.9 | 40.4 | config | model | log |
Note:
- TTA setting is single-scale and
flip=True. - Experiments with
images_per_gpu=6are conducted on Tesla V100-SXM2-32GB,images_per_gpu=3are conducted on GeForce GTX 1080 Ti. - Here are the descriptions of each experiment setting:
- 10 x 5: 10 GPUs with 5 images per gpu. This is the same setting as that reported in the original paper.
- 8 x 6: 8 GPUs with 6 images per gpu. The total batchsize is similar to paper and only need 1 node to train.
- 32 x 3: 32 GPUs with 3 images per gpu. The default setting for 1080TI and need 4 nodes to train.