This GitHub repository contains the Jupyter Notebook and the trained YOLOv10 model (best.pt) that I used in my YouTube tutorial on training a YOLOv10 model on a custom dataset. The project aims to guide users through the entire process of setting up and training a YOLOv10 model to detect objects specific to their own datasets.
About the Project
The primary objective of this project is to teach you how to train the YOLOv10 model using a custom dataset, allowing you to fine-tune the model to detect objects unique to your data. The repository provides the exact code and model weights used in the tutorial, enabling you to replicate the results or adapt the code to fit your specific needs.
What’s Included
- Jupyter Notebook: Contains step-by-step instructions and code for setting up, training, and evaluating the YOLOv10 model on your custom dataset.
- best.pt Model File: The trained YOLOv10 model that you can use for inference on new images or further training.
How to Use Follow the instructions in the Youtube Video to prepare your data, and start training your own YOLOv10 model. The trained model (best.pt) is provided for immediate use or further fine-tuning.
About the YouTube Tutorial
This GitHub repository is associated with my YouTube video, where I walk you through each step of training YOLOv10 on a custom dataset. If you’re new to YOLO or custom object detection, make sure to check out the video for a detailed, hands-on learning experience.