--- license: apache-2.0 pipeline_tag: object-detection library_name: rfdetr datasets: - dronefreak/SeaDronesSee tags: - object-detection - detectionbench - rfdetr - pytorch - computer-vision - maritime - uav - drone - search-and-rescue metrics: - map50 - map50-95 - precision - recall - f1 base_model: "Roboflow/rf-detr-medium" model-index: - name: RF-DETR Medium Finetuned on SeaDronesSee results: - task: type: object-detection name: Object Detection dataset: name: SeaDronesSee type: seadronessee metrics: - type: mAP50 value: 83.47 name: mAP@50 (val split) - type: mAP50-95 value: 47.49 name: mAP@50-95 (val split) - type: precision value: 87.01 name: Precision (val split) - type: recall value: 83.33 name: Recall (val split) source: url: https://github.com/dronefreak/DetectionBench name: DetectionBench --- # RF-DETR Medium Finetuned on SeaDronesSee Fine-tuned RF-DETR Medium object detector on the **SeaDronesSee** benchmark dataset, trained and evaluated as part of [DetectionBench](https://github.com/dronefreak/DetectionBench) -- a framework for reproducibly benchmarking modern object detectors with identical training recipes and evaluation metrics across multiple real-world datasets.
Task Framework Base Model
mAP@50 mAP@50:95 Params
License Source
--- ## Detection Showcase

SeaDronesSee Detection Demo

--- ## Performance | Metric | Score (%) | | ---------- | --------------- | | mAP@50 | 83.47 | | mAP@50-95 | 47.49 | | Precision | 87.01 | | Recall | 83.33 | | F1 Score | 85.13 | | Parameters | 33.7M | | FLOPs | N/A (not published upstream) | --- ## Evaluation Protocol Metrics reported in this model card are computed on the SeaDronesSee **val** split, using DetectionBench's standard evaluation pipeline (`detectionbench-evaluate`). --- ## SeaDronesSee Model Zoo Every model DetectionBench has trained and evaluated on SeaDronesSee so far, for full transparency -- see [DetectionBench](https://github.com/dronefreak/DetectionBench) for the smaller, curated comparison set used on the project README. | Model | mAP@50 | mAP@50-95 | Precision | Recall | | --------------------- | ------------- | --------------- | ----------------- | -------------- | | RF-DETR Medium | 83.47 | 47.49 | 87.01 | 83.33 | | YOLOv26m | 82.38 | 49.57 | 90.01 | 81.18 | | RF-DETR Small | 80.97 | 45.31 | 85.68 | 80.16 | | YOLOv26s | 80.14 | 47.35 | 88.5 | 77.51 | | YOLOv11x | 74.82 | 45.56 | 87.37 | 72.46 | | YOLOv8s | 72.94 | 43.05 | 84.52 | 71.25 | | RF-DETR Nano | 72.38 | 39.83 | 81.37 | 74.08 | | YOLOv11n | 69.93 | 40.41 | 82.87 | 69.04 | | YOLOv8n | 69.22 | 40.35 | 82.46 | 68.36 | | YOLOv8m | 62.08 | 34.41 | 77.3 | 61.01 | --- ## Per-Class Performance | Class | mAP@50 | mAP@50-95 | | -------------------------- | --------------- | ----------------- | | swimmer | 76.06 | 30.44 | | boat | 95.71 | 69.35 | | jetski | 93.55 | 64.16 | | life_saving_appliances | 70.55 | 25.24 | | buoy | 81.49 | 48.26 | --- ## Evaluation Visualizations This model was evaluated with [Supervision](https://github.com/roboflow/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. --- ## Dataset This model was trained on **SeaDronesSee**. For the full dataset description, provenance, license, and citation, see the dataset card: https://huggingface.co/datasets/dronefreak/SeaDronesSee ### Classes * swimmer * boat * jetski * life_saving_appliances * buoy --- ## Usage ### Install Dependencies ```bash pip install rfdetr huggingface_hub ``` ### Load Model from Hugging Face ```python from huggingface_hub import hf_hub_download import rfdetr weights = hf_hub_download( repo_id="dronefreak/seadronessee-rfdetr-medium", filename="checkpoint_best_total.pth" ) model = rfdetr.RFDETRMedium(pretrain_weights=weights) ``` ### Run Inference ```python detections = model.predict("image.jpg", threshold=0.25) ``` --- ## Training Configuration | Setting | Value | | ---------------- | -------------------------------- | | Dataset | SeaDronesSee | | Framework | RF-DETR | | Training Toolkit | DetectionBench | | Epochs (configured max) | 500 | | Epochs (actually trained) | 123 | | Early Stopping Patience | 100 | | Batch Size | 5 | | Resolution | 576 | | Optimizer | adamw | | Learning Rate | 0.0001 | | Seed | 42 | --- ## Repository Contents ```text checkpoint_best_total.pth metrics.csv config.json seadronessee_rfdetr-medium_showcase.jpg README.md ``` --- ## Related Resources * [SeaDronesSee dataset card](https://huggingface.co/datasets/dronefreak/SeaDronesSee) on Hugging Face * [DetectionBench](https://github.com/dronefreak/DetectionBench) -- reproducible benchmarks for modern object detectors on real-world datasets --- ## Training Framework This model was trained using [DetectionBench](https://github.com/dronefreak/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: `swimmer` (64.22%) and `boat` (22.55%) account for roughly 87% of all annotated boxes in the training set, while `life_saving_appliances` (1.60%) is rare -- per-class accuracy on the minority classes is measured on comparatively few examples. * Small-object heavy: objects are captured from altitude over open water, so roughly 79% of boxes cover under 0.1% of the image area -- swimmers and buoys in particular are small, low-contrast targets against water. * No public test-split labels: the official `images/test/` split is a held-out competition set with no released ground truth, so these models are evaluated on the `valid` split instead of `test` -- the number reported here is not directly comparable to official SeaDronesSee leaderboard submissions, which score against the held-out test set via the benchmark's own server. * A maritime search-and-rescue benchmark specifically: generalization to non-maritime aerial scenes, different water/lighting conditions, or altitudes outside this dataset's capture range is untested. --- ## Citation If you use this model in your research, please consider citing: 1. The SeaDronesSee dataset (see below) 2. The original RF-DETR Medium 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{varga2022seadronessee, title={SeaDronesSee: A maritime benchmark for detecting humans in open water}, author={Varga, Leon Amadeus and Kiefer, Benjamin and Messmer, Martin and Zell, Andreas}, booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision}, pages={2260--2270}, year={2022} } @misc{varga2021seadronesseemaritimebenchmarkdetecting, title={SeaDronesSee: A Maritime Benchmark for Detecting Humans in Open Water}, author={Leon Amadeus Varga and Benjamin Kiefer and Martin Messmer and Andreas Zell}, year={2021}, eprint={2105.01922}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2105.01922} } ``` ```bibtex @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} } ``` Other architectures compared against on SeaDronesSee in this model card: ### YOLOv11 ```bibtex 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} } ``` ### YOLOv26 ```bibtex @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 ```bibtex 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} } ``` ```bibtex @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} } ```