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dronefreak / visdrone-yolov8s
Model's Last Updated: July 06 2026
object-detection
Introduction of visdrone-yolov8s
Model Details of visdrone-yolov8s
YOLOv8s Finetuned on VisDrone
Fine-tuned YOLOv8s 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
| Metric | Score (%) |
|---|---|
| mAP@50 | 31.95 |
| mAP@50-95 | 18.24 |
| Precision | 45.99 |
| Recall | 35.49 |
| F1 Score | 40.06 |
| Parameters | 11.2M |
| FLOPs | 28.6B |
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 | YOLOv10l | 35.95 | 21.09 | 52.13 | 38.48 |
| 10 | YOLOv9m | 36.19 | 20.95 | 51.05 | 39.12 |
| 11 | YOLOv8m | 34.39 | 19.95 | 48.18 | 38.2 |
| 12 | YOLOv8s | 31.95 | 18.24 | 45.99 | 35.49 |
| 13 | YOLOv8n | 28.18 | 15.77 | 40.86 | 31.81 |
| 14 | YOLOv11n | 27.59 | 15.46 | 39.58 | 31.74 |
| 15 | YOLOv10n | 27.65 | 15.32 | 41.02 | 31.68 |
| 16 | YOLOv26n | 26.73 | 14.64 | 38.6 | 31.14 |
Per-Class Performance
| Class | mAP@50 | mAP@50-95 |
|---|---|---|
| pedestrian | 28.71 | 11.31 |
| people | 15.19 | 4.86 |
| bicycle | 9.21 | 3.66 |
| car | 72.42 | 45.63 |
| van | 34.65 | 22.85 |
| truck | 38.48 | 24.68 |
| tricycle | 19.05 | 9.84 |
| awning-tricycle | 16.96 | 9.37 |
| bus | 54.02 | 37.96 |
| motor | 30.77 | 12.19 |
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/yolov8s-visdrone",
filename="best.pt"
)
model = YOLO(weights)
Run Inference
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
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:
- The VisDrone dataset
- The original YOLO architecture
- 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}
}
Runs of dronefreak visdrone-yolov8s on huggingface.co
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More Information About visdrone-yolov8s huggingface.co Model
Url of visdrone-yolov8s
visdrone-yolov8s huggingface.co Url
Provider of visdrone-yolov8s huggingface.co
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Updated:October 06 2026
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