Implementation for the paper 'YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs'

Related tags

Deep Learning yoloret
Overview

YOLO-ReT

This is the original implementation of the paper: YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs. Prakhar Ganesh, Yao Chen, Yin Yang, Deming Chen, Marianne Winslett, WACV 2022 [Arxiv] [Camera-Ready coming soon]

Citation

If you find this paper and code useful, please cite our work. The bibtex is listed below:

@inproceedings{ganesh2021yoloret,
  title={{YOLO-ReT}: Towards High Accuracy Real-time Object Detection on Edge {GPU}s},
  author={Ganesh, Prakhar and Chen, Yao and Yang, Yin and Chen, Deming and Winslett, Marianne},
  booktitle={IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  year={2022},
  organization={IEEE}
}

Setup and Reproducibility

Please refer to README inside the folder code for more details.

Acknowledgement

This repository is adapted from https://github.com/fsx950223/mobilenetv2-yolov3

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Comments
  • question about backbone truncation

    question about backbone truncation

    Hi, @prakharg24 I feel confused about backbone truncation your paper. For mobilenetv2 arch: image, you say the last two blocks are not used. Can you tell me which two blocks? You mean the last two bottleneck??(I think you don't mean FC layer, nobody will keep FC layer for detection task) Wishing for your reply!

    opened by Senwang98 13
  • The Question about whether use FLOAT 16 or INT 8 when testing FPS.

    The Question about whether use FLOAT 16 or INT 8 when testing FPS.

    In the tabular data mentioned in this paper, are the FPS of YOLO-RET model all tested by FLOAT 16 quantification?You mentioned in your paper that "We also provide FPS values for TensorRT based INT8 optimizations, to provide a fair comparison against baseline models designed specifically for integer computations", do not understand what it means. Hope you can help me to solve my confusion,thanks!

    opened by cacheee 4
  • Update train.py

    Update train.py

    Fixed erroneuous legacy function call for strategy "experimental_run_v2" by replacing with supported "run" function to be compatable with TF 2.9.x

    opened by b-d-e 0
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