dronefreak/uavdt-rfdetr-small

RF-DETR Small Finetuned on UAVDT

Fine-tuned RF-DETR Small 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.

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Usage

Install Dependencies

pip install rfdetr huggingface_hub

Load Model from Hugging Face

from huggingface_hub import hf_hub_download
import rfdetr

weights = hf_hub_download(
    repo_id="dronefreak/uavdt-rfdetr-small",
    filename="checkpoint_best_total.pth"
)

model = rfdetr.RFDETRSmall(pretrain_weights=weights)

Run Inference

detections = model.predict("image.jpg", threshold=0.25)

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Performance

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

Metric	Score (%)
mAP@50	32.62
mAP@50-95	20.21
Precision	73.83
Recall	71.63
F1 Score	72.71
Parameters	32.1M
FLOPs	N/A (not published upstream)

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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
YOLO26m	33.43	19.56	38.14	39.84
RF-DETR Medium	33.28	20.54	73.03	70.03
YOLO26s	32.98	19.61	43.86	40.38
RF-DETR Nano	32.78	20.31	73.6	66.98
YOLO26x	32.65	19.22	41.85	38.19
YOLO26l	32.64	18.75	40.17	36.45
RF-DETR Small	32.62	20.21	73.83	71.63
YOLOv9s	31.82	18.71	39.83	38.12
YOLOv8m	31.42	18.8	40.27	37.79
YOLO11x	31.05	18.31	37.4	36.38
YOLO11m	30.47	17.71	37.7	37.01
YOLOv8x	30.47	17.66	39.61	36.16
YOLOv10m	30.12	17.33	40.13	35.68
YOLOv9m	29.43	16.97	35.92	35.7
YOLOv9t	29.42	17.03	35.75	36.47
YOLOv10x	29.38	17.15	37.29	35.15
YOLOv10l	29.16	16.54	36.9	35.6
YOLOv9c	29.16	16.46	35.38	34.35
YOLO11s	29.1	17.16	34.32	37.31
YOLO26n	28.88	16.79	33.14	35.66
YOLOv8l	28.86	17.27	38.33	32.86
YOLOv10s	28.85	16.48	36.53	33.16
YOLO11l	28.64	17.16	34.75	34.02
YOLO11n	28.56	16.3	38.04	32.26
YOLOv8n	27.8	15.34	35.42	33.61
YOLOv10n	27.17	15.16	33.3	31.21
YOLOv8s	27.12	15.33	34.65	31.87

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Per-Class Performance

Class	mAP@50	mAP@50-95
car	75.28	44.1
truck	5.78	3.86
bus	16.8	12.66

This model was evaluated with Supervision's detection metrics, which report mAP/Precision/Recall directly but don't produce a confusion-matrix plot the way Ultralytics' validator does.

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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

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Training Configuration

Setting	Value
Dataset	UAVDT
Framework	RF-DETR
Training Toolkit	DetectionBench
Epochs (configured max)	20
Epochs (actually trained)	7
Early Stopping Patience	5
Batch Size	12
Resolution	640
Optimizer	adamw
Learning Rate	5e-05
Seed	42

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Repository Contents

checkpoint_best_total.pth
metrics.csv
config.json
uavdt_rfdetr-small_showcase.jpg
assets/demo_banner.mp4
assets/demo_banner_poster.jpg
README.md

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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)

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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.

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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 ~40.7k labelled images come from 50 video sequences, so consecutive frames are near-duplicates. UAVDT's 50 tracking-only sequences have no detection labels and are excluded. 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 22.7 (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.

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Citation

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

@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}
}

@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}
}