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YOLOv10 is a real-time object detection model known for its high performance and low computational cost. The latest version, YOLOv10, offers significant improvements in efficiency and accuracy, trained on the COCO dataset to detect 80 different object classes. The blog explores how YOLOv10 can be fine-tuned for custom object detection tasks, such as kidney stone detection in medical CT scan images.
The YOLOv10 architecture introduces novel features like consistent dual assignments, NMS-free training, and improvements in efficiency, such as a lightweight classification head and spatial-channel decoupled downsampling. YOLOv10 outperforms previous versions and other state-of-the-art models in terms of accuracy and efficiency.
The blog also details the process of fine-tuning YOLOv10 on a kidney stone dataset from Roboflow, showcasing the potential of the model for custom object detection tasks. The training results show improvements in mAP50 scores, with the model stabilizing around epoch 50 and achieving a mAP50 score of ~0.72. The blog concludes by comparing ground truth with prediction bounding boxes, demonstrating the effectiveness of YOLOv10 in detecting kidney stones and the potential for further enhancements with specialized data augmentation techniques.
The code snippets provided in the blog demonstrate how to run YOLOv10 on sample images, fine-tune the model for custom object detection, and evaluate the model’s performance on test images. The blog also emphasizes the importance of metric monitoring during training, specifically using mAP50 to track the model’s performance.
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Source link: https://medium.com/@girishajmera/fine-tuning-yolov10-for-custom-object-detection-7b12093691c8?source=rss——ai-5
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