This project focuses on fine-tuning the YOLOv10 model for the custom object detection of kidney stones. Leveraging a dataset sourced from Roboflow, this repository provides a setup for training YOLOv10 model for custom dataset (Ex : Kideny stone detection for medical imaging applications) .

• Introduction
• Features
• Dataset
• Installation
• Usage
  • Training
  • Inference
• Results
• Contributing
• License
• Acknowledgements

Object detection in medical imaging, particularly for identifying kidney stones, is a critical task that can aid in faster and more accurate diagnosis. This project utilizes the YOLOv10 model, a state-of-the-art object detection algorithm, fine-tuned with a specific dataset to detect kidney stones.

• Fine-tuned YOLOv10 model for kidney stone detection
• Training pipeline
• Easy-to-use inference script

The dataset used for this project is sourced from Roboflow, which contains annotated images of kidney stones. The dataset is split into training, validation, and test sets.

1. Clone the repository:

   git clone https://github.com/suhanisuha/yolov10_finetune.git
   cd yolov10_kidneystone