Google has recently released new efficient model architectures for edge devices, I thought it would be cool to see what the results of these models are as a backbone for FasterRCNNN. The EfficientNet-Light-head-RCNN has 31MAP on pedestrian detection and run at 12 FPS on my GTX 1060. Only EfficientNet B0 was tested.
the crowdHuman Dataset can be downloaed here CrowdHuman
==>data
===>annotations
===>Images
===>Images_test
====>Images_validation
| Average Precision (AP) | IoU=0.50:0.95 | area= all | maxDets=100 | 0.310 |
|---|---|---|---|---|
| Average Precision (AP) | IoU=0.50 | area= all | maxDets=100 | 0.589 |
| Average Precision (AP) | IoU=0.75 | area= all | maxDets=100 | 0.295 |
if you want to use the pretrained model, please download, create a folder named checkpoint and put it inside Pretrained_model
python3 Train.py
@article{li2017light,
title={Light-Head R-CNN: In Defense of Two-Stage Object Detector},
author={Li, Zeming and Peng, Chao and Yu, Gang and Zhang, Xiangyu and Deng, Yangdong and Sun, Jian},
journal={arXiv preprint arXiv:1711.07264},
year={2017}
}
@article{shao2018crowdhuman,
title={CrowdHuman: A Benchmark for Detecting Human in a Crowd},
author={Shao, Shuai and Zhao, Zijian and Li, Boxun and Xiao, Tete and Yu, Gang and Zhang, Xiangyu and Sun, Jian},
journal={arXiv preprint arXiv:1805.00123},
year={2018}
}
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