YOLOX-Nano-P2-UAV-Small-Detection

YOLOX-Nano+-P2 Checkpoints

This repository contains YOLOX-Nano+-P2 checkpoints from the UAV-QIEA Edge Detection project.

Paper: Edge-Constrained UAV Small-Object Detection with P2 Enhancement and Quantum-Inspired Lightweight Structure Search
Code: Ming23233/UAV-QIEA-Edge-Detection

Model Description

YOLOX-Nano+-P2 is a YOLOX-Nano detector variant with an added high-resolution P2 detection branch for dense UAV small-object scenes. The P2 branch adds a stride-4 detection path, complementing the original stride-8/16/32 detection heads and improving small-object detection on VisDrone-style aerial imagery.

These checkpoints are provided to make the paper easier to reproduce and inspect. The full research code, dataset conversion utilities, and evaluation scripts are maintained in the GitHub repository linked above.

Files

Checkpoint Summary

The three released checkpoints correspond to 100-epoch VisDrone-DET training runs with input size 640 x 640.

The manuscript-level three-seed summary reports:

Usage

Clone and install the code repository first:

git clone https://github.com/Ming23233/UAV-QIEA-Edge-Detection.git
cd UAV-QIEA-Edge-Detection
pip install -r requirements.txt
pip install -r third_party/ByteTrack/requirements.txt
pip install -e third_party/ByteTrack

Download a checkpoint from this Hugging Face repository:

pip install -U "huggingface_hub[cli]"
hf download Ming233/YOLOX-Nano-P2-UAV-Small-Detection checkpoints/yolox_nano_p2_seed43_best_ckpt.pth --local-dir ./hf_checkpoints

Then evaluate or run inference with the corresponding experiment file from the GitHub repository:

python third_party/ByteTrack/tools/eval.py \
  -f third_party/ByteTrack/exps/example/uav/yolox_nano_visdrone_det_640_p2.py \
  -c ./hf_checkpoints/checkpoints/yolox_nano_p2_seed43_best_ckpt.pth \
  -b 1 \
  -d 1

Adjust dataset paths according to your local VisDrone-DET COCO-format layout. See the GitHub repository documentation for dataset conversion and reproducibility details.

Training Data

The released checkpoints were trained on a COCO-format conversion of VisDrone-DET. This repository does not redistribute VisDrone images or annotations. Please obtain datasets from their official sources and follow their licenses.

Limitations

These checkpoints are research artifacts for UAV small-object detection. They are not a general-purpose object detector and may not generalize reliably to non-UAV imagery, different camera altitudes, unusual sensor types, or deployment settings outside the evaluation protocol.

Citation

If you use these checkpoints or the associated code, please cite the manuscript and the original YOLOX/ByteTrack work.

@misc{lei2026edgeconstraineduavsmallobject,
  title={Edge-Constrained UAV Small-Object Detection with P2 Enhancement and Quantum-Inspired Lightweight Structure Search},
  author={Wuming Lei and Yanbin Gao and Mingyan Sun and Xiaobin Li and Xuechen Liang},
  year={2026},
  eprint={2606.09081},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2606.09081}
}

License

The newly added research code is released under the MIT license. Third-party components retain their original licenses. Dataset usage remains governed by the dataset owners.