---
title: "Object Detection"
description: "1 posts about Object Detection, summarised, each linking to the original."
---

# Object Detection
> 1 posts about Object Detection, summarised, each linking to the original.

## Articles

### [Fine-Tuning SOTA Object Detection Models on Real-World Datasets](https://yomu.fyi/post/fine-tuning-sota-object-detection-models-on-real-world-datasets.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Arina Belova
- Published: Aug 31, 2026

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.