dronefreak/uavdt-yolo11x

YOLOv11x Finetuned on UAVDT

Fine-tuned YOLOv11x object detector on the UAVDT 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.

---

Performance

Metric	Score (%)
mAP@50	11.98
mAP@50-95	6.68
Precision	18.7
Recall	25.93
F1 Score	21.73
Parameters	57.0M
FLOPs	196.0B

---

Evaluation Protocol

Metrics reported in this model card are computed on the UAVDT test split, using DetectionBench's standard evaluation pipeline (detectionbench-evaluate).

---

UAVDT Model Zoo

Every model DetectionBench has trained and evaluated on UAVDT 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
RF-DETR Medium	15.02	8.76	40.28	61.14
RF-DETR Small	14.89	8.92	38.83	61.65
YOLOv26s	14.09	7.91	23.88	27.05
RF-DETR Nano	12.96	7.33	37.12	55.4
YOLOv26m	12.32	6.66	19.44	25.83
YOLOv8m	12.28	7.05	19.73	25.33
YOLOv11x	11.98	6.68	18.7	25.93
YOLOv8s	11.48	6.71	19.34	23.39
YOLOv8n	10.79	6.42	18.61	24.58

---

Per-Class Performance

Class	mAP@50	mAP@50-95
car	31.92	17.18
truck	2.29	1.56
bus	1.74	1.3

---

Evaluation Visualizations

Precision-Recall Curve

F1 Curve

Confusion Matrix

Normalized Confusion Matrix

---

Dataset

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

https://huggingface.co/datasets/dronefreak/UAVDT

Classes

• car
• truck
• bus

---

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/uavdt-yolo11x",
    filename="best.pt"
)

model = YOLO(weights)

Run Inference

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

results[0].show()

---

Training Configuration

Setting	Value
Dataset	UAVDT
Framework	Ultralytics YOLO
Training Toolkit	DetectionBench
Epochs (configured max)	500
Epochs (actually trained)	102
Early Stopping Patience	100
Batch Size	8
Image Size	640
Optimizer	Adam
Initial Learning Rate	0.001
Seed	0

---

Repository Contents

best.pt
results.csv
args.yaml
BoxPR_curve.png
BoxF1_curve.png
confusion_matrix.png
confusion_matrix_normalized.png
val_batch0_pred.jpg
uavdt_yolo11x_showcase.jpg
assets/demo_banner.mp4
assets/demo_banner_poster.jpg
README.md

---

Related Resources

• UAVDT dataset card on Hugging Face
• DetectionBench -- reproducible benchmarks for modern object detectors on real-world datasets
• UAVDT paper preprint (arXiv:1804.00518)
• UAVDT project website (official data source)

---

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 (94.6%) dominates the annotated boxes, while truck (3.1%) and bus (2.3%) are rare -- per-class accuracy on the minority classes is measured on comparatively few examples, and every model here scores far lower on them than on car.
• Very small objects: the median box covers only 0.14% of the image area (mean 0.26%), so this is a hard small-object regime and absolute mAP values are low for every architecture; the numbers are best read as a relative comparison between models, not as a production-quality detector.
• Video-derived, highly correlated frames: the ~78k images come from 100 video sequences, so consecutive frames are near-duplicates. The validation split is carved out of the training sequences by sequence (not by frame) to avoid leakage, but effective diversity is far lower than the image count suggests.
• Different density per split: instances per image are 15.7 (train), 28.0 (valid) and 7.0 (test), because the splits contain different sequences -- validation metrics are not directly predictive of test metrics.
• Research-use-only data: UAVDT is distributed "for research purpose only" with no redistribution grant, so the dataset is not mirrored here -- obtain it from the official source (see the Dataset section above) and check its terms before any use beyond research.

---

Citation

If you use this model in your research, please consider citing:

1. The UAVDT dataset (see below)
2. The original YOLOv11x architecture (see below)
3. The other model architectures shown in the Model Zoo/External Comparison tables above, if you reference their results
4. DetectionBench, the training/evaluation framework used to produce this checkpoint

@InProceedings{du2018unmanned,
  title={The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking},
  author={Du, Dawei and Qi, Yuankai and Yu, Hongyang and Yang, Yifan and Duan, Kaiwen and Li, Guorong and Zhang, Weigang and Huang, Qingming and Tian, Qi},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2018}
}

No official YOLO11 research paper has been published by Ultralytics; the most commonly cited independent architectural analysis is used instead:

@article{khanam2024yolov11,
  title={YOLOv11: An Overview of the Key Architectural Enhancements},
  author={Khanam, Rahima and Hussain, Muhammad},
  journal={arXiv preprint arXiv:2410.17725},
  year={2024}
}

Other architectures compared against on UAVDT in this model card:

RF-DETR

@inproceedings{robinson2026rfdetr,
  title     = {RF-DETR: Real-Time Detection Transformer},
  author    = {Robinson, Isaac and Robicheaux, Peter and Popov, Matvei and Ramanan, Deva and Peri, Neehar},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year      = {2026},
  url       = {https://arxiv.org/abs/2511.09554}
}

@article{oquab2023dinov2,
  title={DINOv2: Learning Robust Visual Features without Supervision},
  author={Oquab, Maxime and Darcet, Timoth{\'e}e and Moutakanni, Theo and Vo, Huy and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and others},
  journal={arXiv preprint arXiv:2304.07193},
  year={2023}
}

YOLOv26

@article{jocher2026yolo26,
  title={Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models},
  author={Jocher, Glenn and Qiu, Jing and Liu, Mengyu and Lyu, Shuai and Akyon, Fatih Cagatay and Kalfaoglu, Muhammet Esat},
  journal={arXiv preprint arXiv:2606.03748},
  year={2026}
}

YOLOv8

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}
}

@software{Saksena_DetectionBench_2026,
  author = {Saksena, Saumya Kumaar},
  title = {DetectionBench: Reproducible Benchmarks for Modern Object Detectors on Real-World Datasets},
  url = {https://github.com/dronefreak/DetectionBench},
  year = {2026}
}