dronefreak/visdrone-yolov26m

YOLOv26m Finetuned on VisDrone

Fine-tuned YOLOv26m object detector for aerial imagery using the VisDrone benchmark dataset.

This model is part of the VisDrone Detection Model Zoo, a collection of YOLO models trained and evaluated under a common pipeline for aerial object detection.

Detection Showcase

<p align="center"> <img src="visdrone_showcase.gif" alt="VisDrone Detection Demo"> </p>

Performance

Evaluation Protocol

Metrics reported in this model card are computed on the VisDrone test set with ground-truth annotations available for evaluation.

VisDrone Model Zoo

Per-Class Performance

Evaluation Visualizations

Precision-Recall Curve

[image]

F1 Curve

[image]

Confusion Matrix

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Dataset

VisDrone is a large-scale benchmark for object detection in aerial imagery captured from unmanned aerial vehicles (UAVs).

The dataset contains diverse scenes including:

• —Urban environments
• —Residential areas
• —Traffic intersections
• —Crowded pedestrian regions

Classes

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

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/yolov26m-visdrone",
    filename="best.pt"
)

model = YOLO(weights)

Run Inference

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

results[0].show()

Training Configuration

Repository Contents

best.pt
results.csv
args.yaml
BoxPR_curve.png
BoxF1_curve.png
confusion_matrix.png
assets/visdrone_showcase.gif
README.md

Related Resources

• —VisDrone Detection Model Zoo (Hugging Face Collection)
• —VisDrone Dataset Python Toolkit: https://github.com/dronefreak/VisDrone-dataset-python-toolkit
• —VisDrone Dataset: https://github.com/VisDrone/VisDrone-Dataset

Training Framework

These models were trained using the VisDrone Dataset Python Toolkit, an open-source framework for aerial object detection research and benchmarking on the VisDrone dataset.

Features include:

• —Dataset preparation and conversion utilities
• —Training and evaluation pipelines
• —Detection benchmarking
• —Visualization tools
• —Support for multiple YOLO model families

Repository:

https://github.com/dronefreak/VisDrone-dataset-python-toolkit

If you find these models useful, please consider starring the repository.

Known Limitations

Performance may degrade in:

• —Extremely dense crowds
• —Heavy occlusions
• —Severe motion blur
• —Very small objects occupying only a few pixels
• —Night-time or low-light aerial imagery

Citation

If you use this model in your research, please consider citing:

1. 1.The VisDrone dataset
2. 2.The original YOLO architecture
3. 3.The VisDrone Detection Toolkit

@article{visdrone2019,
  title={Vision Meets Drones: A Challenge},
  author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Ling, Haibin and Hu, Qinghua},
  journal={International Journal of Computer Vision},
  year={2021}
}

@software{Saksena_VisDrone_Detection_Toolkit_2025,
  author = {Saksena, Saumya Kumaar},
  title = {VisDrone Detection Toolkit: Modern PyTorch Implementation for Aerial Object Detection},
  url = {https://github.com/dronefreak/VisDrone-dataset-python-toolkit},
  version = {2.0.0},
  year = {2025}
}