visdrone-yolov10n

YOLOv10n Finetuned on VisDrone-DET

Fine-tuned YOLOv10n object detector on the VisDrone-DET benchmark dataset, trained and evaluated as part of DetectionBench -- a framework for reproducibly benchmarking modern object detectors with identical training recipes and evaluation metrics across multiple real-world datasets.

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Usage

Install Dependencies

pip install ultralytics huggingface_hub

Load Model from Hugging Face

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

weights = hf_hub_download(
    repo_id="dronefreak/visdrone-yolov10n",
    filename="best.pt"
)

model = YOLO(weights)

Run Inference

results = model.predict(
    source="image.jpg",
    conf=0.25
)

results[0].show()

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Performance

Evaluated on the VisDrone-DET test split, using DetectionBench's standard evaluation pipeline (detectionbench-evaluate).

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VisDrone-DET Model Zoo

Every model DetectionBench has trained and evaluated on VisDrone-DET so far, for full transparency -- see DetectionBench for the smaller, curated comparison set used on the project README.

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Per-Class Performance

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Dataset

This model was trained on VisDrone-DET. For the full dataset description, provenance, license, and citation, see the dataset card:

https://huggingface.co/datasets/Voxel51/VisDrone2019-DET

Classes

• pedestrian
• people
• bicycle
• car
• van
• truck
• tricycle
• awning-tricycle
• bus
• motor
• others

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Training Configuration

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Repository Contents

best.pt
results.csv
args.yaml
BoxPR_curve.png
BoxF1_curve.png
BoxP_curve.png
BoxR_curve.png
confusion_matrix.png
confusion_matrix_normalized.png
val_batch0_pred.jpg
visdrone_yolov10n_showcase.jpg
assets/demo_banner.mp4
assets/demo_banner_poster.jpg
README.md

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Related Resources

• VisDrone-DET dataset card on Hugging Face
• DetectionBench -- reproducible benchmarks for modern object detectors on real-world datasets

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Training Framework

This model was trained using DetectionBench, an open-source framework for benchmarking object detectors across multiple real-world datasets with a common pipeline.

Features include:

• A dataset-adapter registry for converting real-world datasets into a canonical format
• Identical training/evaluation recipes across model families (Ultralytics YOLO/RT-DETR, RF-DETR)
• Hardware profiling (latency, FPS, VRAM, parameters, FLOPs)
• One-command reproducibility via versioned Hydra configs

If you find this model useful, please consider starring the repository.

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Known Limitations

• Severe class imbalance: car (42.21%) and pedestrian (23.12%) account for two-thirds of all annotated boxes in the training set, while awning-tricycle (0.95%) and tricycle (1.40%) are rare -- the others class has zero annotated instances in the training set entirely and is effectively unusable (always 0 AP).
• Extreme small-object density: ~53 annotated boxes per image on average, with roughly 69% of boxes covering under 0.1% of the image area -- consistent with VisDrone's aerial small-object detection challenge (objects captured from significant altitude).
• The original authors license VisDrone under CC BY-NC-SA 3.0 -- non-commercial research use only (see the dataset's homepage); this applies to any model trained on it, not only the raw images.
• These RF-DETR checkpoints were trained/evaluated directly through DetectionBench. The YOLO/RT-DETR rows in the External VisDrone Model Zoo comparison below were trained via a separate companion codebase, not reproduced inside DetectionBench -- see that collection for their own training details and caveats.

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Citation

If you use this model in your research, please consider citing the dataset and the model architecture:

@article{zhu2018vision,
  title={Vision meets drones: A challenge},
  author={Zhu, Pengfei and Wen, Longyin and Bian, Xiao and Ling, Haibin and Hu, Qinghua},
  journal={arXiv preprint arXiv:1804.07437},
  year={2018}
}

@article{wang2024yolov10,
  title={YOLOv10: Real-Time End-to-End Object Detection},
  author={Wang, Ao and Chen, Hui and Liu, Lihao and Chen, Kai and Lin, Zijia and Han, Jungong and Ding, Guiguang},
  journal={arXiv preprint arXiv:2405.14458},
  year={2024}
}