| --- |
| license: agpl-3.0 |
|
|
| pipeline_tag: object-detection |
|
|
| library_name: ultralytics |
|
|
| datasets: |
| - Voxel51/VisDrone2019-DET |
|
|
| tags: |
| - object-detection |
| - aerial-imagery |
| - drone |
| - visdrone |
| - ultralytics |
| - pytorch |
| - computer-vision |
|
|
| metrics: |
| - map50 |
| - map50-95 |
| - precision |
| - recall |
| - f1 |
|
|
| base_model: "Ultralytics/YOLO11" |
| --- |
| |
|
|
| # YOLOv11s Finetuned on VisDrone |
|
|
| Fine-tuned YOLOv11s 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 |
|
|
| | Metric | Score (%) | |
| | ---------- | --------------- | |
| | mAP@50 | 32.3 | |
| | mAP@50-95 | 18.47 | |
| | Precision | 45.49 | |
| | Recall | 35.31 | |
| | F1 Score | 39.76 | |
| | Parameters | 9.5M | |
| | FLOPs | 21.7B | |
|
|
| --- |
|
|
| ## 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 |
|
|
| | Rank | Model | mAP@50 | mAP@50-95 | Precision | Recall | |
| | -------------------------- | -------------------- | ------------- | --------------- | ----------------- | -------------- | |
| | | | | | | | |
| | 1 | YOLOv9e | 40.02 | 23.73 | 54.78 | 42.42 | |
| | | | | | | | |
| | 2 | YOLOv11x | 38.44 | 22.6 | 52.41 | 41.43 | |
| | | | | | | | |
| | 3 | YOLOv26x | 38.33 | 22.48 | 52.91 | 41.06 | |
| | | | | | | | |
| | 4 | YOLOv11l | 37.14 | 21.85 | 51.87 | 40.33 | |
| | | | | | | | |
| | 5 | YOLOv10x | 37.24 | 21.81 | 52.59 | 39.84 | |
| | | | | | | | |
| | 6 | YOLOv26l | 37.65 | 21.75 | 51.6 | 40.42 | |
| | | | | | | | |
| | 7 | YOLOv9c | 37.22 | 21.73 | 51.99 | 39.77 | |
| | | | | | | | |
| | 8 | YOLOv8x | 36.81 | 21.52 | 51.91 | 39.78 | |
| | | | | | | | |
| | 9 | YOLOv26m | 36.67 | 21.22 | 51.03 | 39.79 | |
| | | | | | | | |
| | 10 | YOLOv10l | 35.95 | 21.09 | 52.13 | 38.48 | |
| | | | | | | | |
| | 11 | YOLOv11m | 36.35 | 21.02 | 50.24 | 39.46 | |
| | | | | | | | |
| | 12 | YOLOv9m | 36.19 | 20.95 | 51.05 | 39.12 | |
| | | | | | | | |
| | 13 | YOLOv8m | 34.39 | 19.95 | 48.18 | 38.2 | |
| | | | | | | | |
| | 14 | YOLOv9s | 33.52 | 19.26 | 46.16 | 37.43 | |
| | | | | | | | |
| | 15 | YOLOv11s | 32.3 | 18.47 | 45.49 | 35.31 | |
| | | | | | | | |
| | 16 | YOLOv8s | 31.95 | 18.24 | 45.99 | 35.49 | |
| | | | | | | | |
| | 17 | YOLOv26s | 32.1 | 18.06 | 45.75 | 35.05 | |
| | | | | | | | |
| | 18 | YOLOv9t | 29.09 | 16.22 | 42.57 | 32.66 | |
| | | | | | | | |
| | 19 | YOLOv8n | 28.18 | 15.77 | 40.86 | 31.81 | |
| | | | | | | | |
| | 20 | YOLOv11n | 27.59 | 15.46 | 39.58 | 31.74 | |
| | | | | | | | |
| | 21 | YOLOv10n | 27.65 | 15.32 | 41.02 | 31.68 | |
| | | | | | | | |
| | 22 | YOLOv26n | 26.73 | 14.64 | 38.6 | 31.14 | |
| | | | | | | | |
| | 23 | rt_detr_l | 21.68 | 9.34 | 35.76 | 26.3 | |
| | | | | | | | |
|
|
| --- |
|
|
| ## Per-Class Performance |
|
|
| | Class | mAP@50 | mAP@50-95 | |
| | -------------------------- | --------------- | ----------------- | |
| | | | | |
| | pedestrian | 27.96 | 10.85 | |
| | | | | |
| | people | 14.18 | 4.7 | |
| | | | | |
| | bicycle | 9.67 | 3.88 | |
| | | | | |
| | car | 72.41 | 45.27 | |
| | | | | |
| | van | 37.48 | 24.44 | |
| | | | | |
| | truck | 40.54 | 25.64 | |
| | | | | |
| | tricycle | 18.19 | 9.71 | |
| | | | | |
| | awning-tricycle | 17.61 | 10.06 | |
| | | | | |
| | bus | 55.59 | 38.77 | |
| | | | | |
| | motor | 29.33 | 11.39 | |
| | | | | |
|
|
| --- |
|
|
| ## 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 |
|
|
| ```bash |
| pip install ultralytics huggingface_hub |
| ``` |
|
|
| ### Load Model from Hugging Face |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| from ultralytics import YOLO |
| |
| weights = hf_hub_download( |
| repo_id="dronefreak/yolov11s-visdrone", |
| filename="best.pt" |
| ) |
| |
| model = YOLO(weights) |
| ``` |
|
|
| ### Run Inference |
|
|
| ```python |
| results = model.predict( |
| source="image.jpg", |
| conf=0.25 |
| ) |
| |
| results[0].show() |
| ``` |
|
|
| --- |
|
|
| ## Training Configuration |
|
|
| | Setting | Value | |
| | ---------------- | ------------------------------- | |
| | Epochs | 300 | |
| | Dataset | VisDrone2019-DET | |
| | Framework | Ultralytics YOLO | |
| | Training Toolkit | VisDrone Dataset Python Toolkit | |
|
|
| --- |
|
|
| ## Repository Contents |
|
|
| ```text |
| 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 |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
