dronefreak / exdark-rfdetr-small

RF-DETR Small Finetuned on ExDark

Fine-tuned RF-DETR Small object detector on the ExDark 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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Detection Showcase

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Performance

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Evaluation Protocol

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

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ExDark Model Zoo

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

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Per-Class Performance

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Evaluation Visualizations

This model was evaluated with Supervision 's detection metrics, which report mAP/Precision/Recall directly but don't produce PR-curve, F1-curve, or confusion-matrix plot images the way Ultralytics' validator does. See the Performance table above for Precision/Recall/F1 and the per-class table above for the full per-class mAP breakdown.

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Dataset

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

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

Classes

• Bicycle
• Boat
• Bottle
• Bus
• Car
• Cat
• Chair
• Cup
• Dog
• Motorbike
• People
• Table

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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/exdark-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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Training Configuration

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Repository Contents

checkpoint_best_total.pth
metrics.csv
config.json
exdark_rfdetr-small_showcase.jpg
README.md

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Related Resources

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

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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: People accounts for roughly 46% of all annotated boxes while Bus is the rarest class, so per-class accuracy on rare classes is measured on very few test examples and should be read with wide uncertainty.
• Small dataset overall (7,344 images, 734 in the test split, across 12 classes) -- limited training signal for several classes independent of the imbalance above.
• Two-hop provenance: this dataset was converted to YOLO format by a third-party Roboflow export before reaching DetectionBench, not sourced directly from the original per-class-folder release; images are pre-resized to 640x640 by that export.
• The original authors separately request non-commercial use of this dataset (beyond the BSD-3-Clause license text itself) -- this applies to any model trained on it, not only the raw images.

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Citation

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

1. The ExDark dataset (see below)
2. The original RF-DETR Small architecture (see below)
3. DetectionBench, the training/evaluation framework used to produce this checkpoint

@article{Exdark,
  title = {Getting to Know Low-light Images with The Exclusively Dark Dataset},
  author = {Loh, Yuen Peng and Chan, Chee Seng},
  journal = {Computer Vision and Image Understanding},
  volume = {178},
  pages = {30-42},
  year = {2019},
  doi = {https://doi.org/10.1016/j.cviu.2018.10.010}
}

@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}
}

@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}
}