YOLOv8n Finetuned on VisDrone-DET

Fine-tuned YOLOv8n object detector on the VisDrone-DET benchmark dataset, trained and evaluated as part of DetectionBench -- a framework for reproducibly benchmarking modern object detectors with identical training recipes and evaluation metrics across multiple real-world datasets.

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/visdrone-yolov8n",
    filename="best.pt"
)

model = YOLO(weights)

Run Inference

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

results[0].show()

Performance

Evaluated on the VisDrone-DET test split, using DetectionBench's standard evaluation pipeline (detectionbench-evaluate).

Metric	Score (%)
mAP@50	39.69
mAP@50-95	23.03
Precision	52.02
Recall	41.36
F1 Score	46.08
Parameters	3.2M
FLOPs	8.7B (at 640 px)

VisDrone-DET Model Zoo

Every model DetectionBench has trained and evaluated on VisDrone-DET so far, for full transparency -- see DetectionBench for the smaller, curated comparison set used on the project README.

Model	mAP@50	mAP@50-95	Precision	Recall
YOLO26m	49.11	29.24	60.21	49.76
YOLO11m	48.03	28.56	59.34	48.3
YOLOv9m	47.62	28.52	59.26	48.4
YOLOv10m	46.73	27.72	59.23	47.35
YOLOv8m	45.47	26.94	57.86	46.39
YOLOv9s	45.38	26.95	56.95	46.19
YOLO26s	44.87	26.43	56.44	45.41
YOLOv10s	44.51	26.28	55.97	45.61
YOLO11s	43.64	25.88	54.48	45.01
YOLOv8s	43.47	25.77	56.09	44.51
YOLOv9t	40.67	23.73	52.84	41.78
YOLO26n	39.9	22.95	51.14	42.08
YOLOv10n	39.8	23.08	51.22	41.5
YOLOv8n	39.69	23.03	52.02	41.36
RF-DETR Medium	39.62	21.99	70.62	46.76
YOLO11n	39.52	23.0	51.49	41.02
RF-DETR Small	39.13	21.68	64.38	49.47
RF-DETR Nano	37.92	20.88	69.02	46.21

Per-Class Performance

Class	mAP@50	mAP@50-95
pedestrian	38.26	15.89
people	23.61	8.37
bicycle	17.56	7.9
car	79.03	50.92
van	44.74	30.65
truck	45.58	29.68
tricycle	25.4	13.75
awning-tricycle	22.27	13.34
bus	60.1	42.9
motor	40.4	16.89
others	0.0	0.0

Dataset

This model was trained on VisDrone-DET. For the full dataset description, provenance, license, and citation, see the dataset card:

https://huggingface.co/datasets/Voxel51/VisDrone2019-DET

Classes

pedestrian
people
bicycle
car
van
truck
tricycle
awning-tricycle
bus
motor
others

Training Configuration

Setting	Value
Dataset	VisDrone-DET
Framework	Ultralytics YOLO
Training Toolkit	DetectionBench
Epochs (configured max)	100
Epochs (actually trained)	100
Early Stopping Patience	25
Batch Size	auto (Ultralytics AutoBatch)
Image Size	1280
Optimizer	SGD
Initial Learning Rate	0.01
Seed	0

Repository Contents

best.pt
results.csv
args.yaml
BoxPR_curve.png
BoxF1_curve.png
BoxP_curve.png
BoxR_curve.png
confusion_matrix.png
confusion_matrix_normalized.png
val_batch0_pred.jpg
visdrone_yolov8n_showcase.jpg
assets/demo_banner.mp4
assets/demo_banner_poster.jpg
README.md

Related Resources

VisDrone-DET dataset card on Hugging Face
DetectionBench -- reproducible benchmarks for modern object detectors on real-world datasets

Training Framework

This model was trained using DetectionBench, an open-source framework for benchmarking object detectors across multiple real-world datasets with a common pipeline.

Features include:

A dataset-adapter registry for converting real-world datasets into a canonical format
Identical training/evaluation recipes across model families (Ultralytics YOLO/RT-DETR, RF-DETR)
Hardware profiling (latency, FPS, VRAM, parameters, FLOPs)
One-command reproducibility via versioned Hydra configs

If you find this model useful, please consider starring the repository.

Known Limitations

Severe class imbalance: car (42.21%) and pedestrian (23.12%) account for two-thirds of all annotated boxes in the training set, while awning-tricycle (0.95%) and tricycle (1.40%) are rare -- the others class has zero annotated instances in the training set entirely and is effectively unusable (always 0 AP).
Extreme small-object density: ~53 annotated boxes per image on average, with roughly 69% of boxes covering under 0.1% of the image area -- consistent with VisDrone's aerial small-object detection challenge (objects captured from significant altitude).
The original authors license VisDrone under CC BY-NC-SA 3.0 -- non-commercial research use only (see the dataset's homepage); this applies to any model trained on it, not only the raw images.
These RF-DETR checkpoints were trained/evaluated directly through DetectionBench. The YOLO/RT-DETR rows in the External VisDrone Model Zoo comparison below were trained via a separate companion codebase, not reproduced inside DetectionBench -- see that collection for their own training details and caveats.

Citation

If you use this model in your research, please consider citing the dataset and the model architecture:

@article{zhu2018vision,
  title={Vision meets drones: A challenge},
  author={Zhu, Pengfei and Wen, Longyin and Bian, Xiao and Ling, Haibin and Hu, Qinghua},
  journal={arXiv preprint arXiv:1804.07437},
  year={2018}
}

No official YOLOv8 research paper has been published by Ultralytics; this is their own recommended software citation instead:

@software{jocher2023yolov8,
  author = {Glenn Jocher and Ayush Chaurasia and Jing Qiu},
  title = {Ultralytics YOLOv8},
  version = {8.0.0},
  year = {2023},
  url = {https://github.com/ultralytics/ultralytics},
  license = {AGPL-3.0}
}