YOLOv11m Finetuned on VisDrone

Fine-tuned YOLOv11m 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

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

F1 Curve

Confusion Matrix

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/yolov11m-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. The VisDrone dataset
2. The original YOLO architecture
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}
}