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dronefreak/
visdrone-yolov9c

Name: visdrone-yolov9c
Availability: InStock

Voxel51/VisDrone2019-DET

agpl-3.0

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YOLOv9c Finetuned on VisDrone

Fine-tuned YOLOv9c 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	37.22
mAP@50-95	21.73
Precision	51.99
Recall	39.77
F1 Score	45.07
Parameters	25.6M
FLOPs	104.0B

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	32.83	13.19

people	17.86	6.15

bicycle	14.65	6.23

car	75.59	48.51

van	40.93	27.65

truck	48.38	32.16

tricycle	25.69	13.77

awning-tricycle	20.42	12.05

bus	58.92	42.3

motor	36.94	15.34

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

python

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

weights = hf_hub_download(
    repo_id="dronefreak/yolov9c-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:

The VisDrone dataset
The original YOLO architecture
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}
}

dronefreak/visdrone-yolov9c