RF-DETR Small Finetuned on GC10-DET

Fine-tuned RF-DETR Small object detector on the GC10-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.

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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/gc10det-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 GC10-DET test split, using DetectionBench's standard evaluation pipeline (detectionbench-evaluate).

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GC10-DET Model Zoo

Every model DetectionBench has trained and evaluated on GC10-DET 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

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 GC10-DET. For the full dataset description, provenance, license, and citation, see the dataset card:

https://huggingface.co/datasets/dronefreak/GC10-DET

Classes

• crease
• crescent_gap
• inclusion
• oil_spot
• punching_hole
• rolled_pit
• silk_spot
• waist_folding
• water_spot
• welding_line

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Training Configuration

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Repository Contents

checkpoint_best_total.pth
metrics.csv
config.json
gc10det_rfdetr-small_showcase.jpg
README.md

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Related Resources

• GC10-DET dataset card on Hugging Face
• DetectionBench -- reproducible benchmarks for modern object detectors on real-world datasets
• GC10-DET paper (Sensors 2020, doi:10.3390/s20061562)
• GC10-DET GitHub (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

• No official split: GC10-DET's paper defines no train/valid/test division, so this adapter creates a deterministic seeded 80/10/10 split over the sorted-then-shuffled image list -- results are not directly comparable to a paper that uses a different split.
• Small dataset: only 1,840 training images (2,300 total) across 10 classes, so absolute scores are more sensitive to the specific split than on the project's larger datasets, and per-class scores on the rarest classes are noisy.
• Severe class imbalance: silk_spot is 24.8% of all boxes, while crease has only 74 instances (2.1%) across the whole dataset -- its per-class score is measured on very few examples and should be read with caution.
• Sparse, mostly single-defect images: 1.55 instances/image on average (median 1, max 11), unlike the crowded-scene datasets (PKLot/VisDrone/UAVDT) -- this is a localization task on large, easy-to-see boxes (median 3.48% of image area) rather than a small-object or dense-detection problem.
• Different visual domain: GC10-DET is grayscale industrial line-scan imagery of rolled steel surfaces, not a natural-scene photo -- the first industrial-inspection dataset in DetectionBench, so these results say nothing about how these checkpoints would perform on outdoor/natural-scene detection or vice versa.

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Citation

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

@article{lv2020deep,
  title = {Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network},
  author = {Lv, Xiaoming and Duan, Fajie and Jiang, Jia-jia and Fu, Xiao and Gan, Lin},
  journal = {Sensors},
  volume = {20},
  number = {6},
  pages = {1562},
  year = {2020},
  publisher = {MDPI},
  doi = {10.3390/s20061562}
}

@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}
}