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Object Detection
1 posts about Object Detection. Every summary links to the original.
Fine-Tuning SOTA Object Detection Models on Real-World Datasets
This practical comparison examines how YOLO12, YOLO26, and RF-DETR behave when moved from COCO to specialized real-world object-detection datasets, and how fine-tuning changes the outcome. The authors first evaluate six pretrained checkpoints on COCO val2017, then use cable-damage, bone-fracture, and soda-bottles datasets from RF100-VL for zero-shot testing, fine-tuning, and accuracy/latency comparison. Because the model implementations require incompatible dependencies, the workflow uses one PyCharm project with three isolated uv environments and remote GPU execution; the reported latency stays in native frameworks rather than TensorRT. All models perform well on COCO, but direct performance on specialized data falls close to zero, while 10 fine-tuning epochs adapt the detectors well to cable damage and soda bottles. Bone-fracture remains difficult despite fine-tuning, with RF-DETR performing best, indicating that domain shift may require more data, longer training, or domain-specific pretraining.
Arina Belova
