dronefreak/visdrone-yolov9e

YOLOv9e Finetuned on VisDrone

Fine-tuned YOLOv9e 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

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Performance

Metric	Score (%)
mAP@50	40.02
mAP@50-95	23.73
Precision	54.78
Recall	42.42
F1 Score	47.82
Parameters	-
FLOPs	-

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Evaluation Protocol

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

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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

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Per-Class Performance

Class	mAP@50	mAP@50-95
pedestrian	36.05	15.0
people	19.34	6.85
bicycle	17.42	7.73
car	78.42	51.15
van	43.86	30.07
truck	52.54	35.55
tricycle	26.32	14.91
awning-tricycle	22.21	13.2
bus	62.55	45.45
motor	41.46	17.4

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Evaluation Visualizations

Precision-Recall Curve

F1 Curve

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

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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/yolov9e-visdrone",
    filename="best.pt"
)

model = YOLO(weights)

Run Inference

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

results[0].show()

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Training Configuration

Setting	Value
Epochs	300
Dataset	VisDrone2019-DET
Framework	Ultralytics YOLO
Training Toolkit	VisDrone Dataset Python Toolkit

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Repository Contents

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

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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

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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.

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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

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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}
}