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Pedestrian Detection on Custom Dataset using an Attention Based Transformer Technique. NB includes visualizations of intermediate layers, sampling points of the best query and fine-tuning apart from regular inference

License: GNU General Public License v3.0

Jupyter Notebook 99.94% Python 0.06%

dino-detr_pedestrian_detection's Introduction

DINO-DETR Pedestrian Detection On Custom Dataset


Pedestrian Detection on Custom Dataset using an Attention Based Transformer Technique. NB includes visualizations of intermediate layers, sampling points of the best query and fine-tuning apart from regular inference.

Annotated Image from the IIT-D Dataset


DINO

This is a standalone notebook of the paper "DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection".

Checkpoints available here:

We have put our model checkpoints here [model zoo in Google Drive][model zoo in 百度网盘](提取码"DINO"), where checkpoint{x}_{y}scale.pth denotes the checkpoint of y-scale model trained for x epochs.

No installation requirements if using NB on cloud (preferred Google Colab)

Bibtex

@misc{zhang2022dino,
      title={DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection}, 
      author={Hao Zhang and Feng Li and Shilong Liu and Lei Zhang and Hang Su and Jun Zhu and Lionel M. Ni and Heung-Yeung Shum},
      year={2022},
      eprint={2203.03605},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@inproceedings{li2022dn,
      title={Dn-detr: Accelerate detr training by introducing query denoising},
      author={Li, Feng and Zhang, Hao and Liu, Shilong and Guo, Jian and Ni, Lionel M and Zhang, Lei},
      booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
      pages={13619--13627},
      year={2022}
}

@inproceedings{
      liu2022dabdetr,
      title={{DAB}-{DETR}: Dynamic Anchor Boxes are Better Queries for {DETR}},
      author={Shilong Liu and Feng Li and Hao Zhang and Xiao Yang and Xianbiao Qi and Hang Su and Jun Zhu and Lei Zhang},
      booktitle={International Conference on Learning Representations},
      year={2022},
      url={https://openreview.net/forum?id=oMI9PjOb9Jl}
}

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