Fine-Tuning SOTA Object Detection Models on Real-World Datasets - The JetBrains Blog

Fine-tuning is essential for deploying pretrained object detectors on specialized data because models trained primarily on COCO’s common everyday categories perform near zero-shot accuracy on unfamiliar classes and visual domains such as industrial cable damage, bone fractures in X-rays, and densely packed soda bottles. The study compared YOLOv12, YOLO26, and RF-DETR models in small and medium or base variants, first reproducing broadly comparable COCO accuracy baselines while noting that real deployment latency varies with hardware and framework optimization. Using PyCharm with separate virtual environments for incompatible model dependencies and remote GPU training, the models were fine-tuned for 10 epochs on three RF100 datasets. Fine-tuning substantially improved results, with soda-bottle detection achieving high performance across all models, cable-damage detection proving reliable but difficult to localize precisely, and bone-fracture detection remaining challenging because of the large shift from natural photographs to X-ray imagery. RF-DETR Base was the most consistently strong model, particularly on unfamiliar domains, but the findings emphasize that no single detector is universally best and that model selection should account for target-domain similarity, accuracy, latency, model size, licensing, and the need for additional data or domain-specific pretraining.

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