#computervision #objectdetection #yolo #yolov10

Welcome to our step-by-step guide on training the YOLOv10 model on your own custom dataset! In this tutorial, we’ll show you everything you need to know to get started with YOLOv10, from setting up your environment to fine-tuning the model for optimal performance.

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In this video, you will learn:

Introduction to YOLOv10: Overview of the latest YOLOv10 model and its features.
Environment Setup: How to set up your development environment, including necessary libraries and tools.
Dataset Preparation: Methods for collecting, annotating, and organizing your custom dataset.
Training the Model: Step-by-step process to train YOLOv10 on your custom dataset.
Evaluating Performance: Techniques for evaluating the model’s accuracy and performance.
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Resources:

code: https://github.com/Gagan824/Computer-Vision-Projects/tree/main/Yolo_V10_object_detection
Annotation Tool: https://app.roboflow.com/
yolov10: https://docs.ultralytics.com/models/yolov10/
research paper: https://arxiv.org/pdf/2405.14458
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Tools & Libraries Used:
Python
PyTorch
Supervision
Ultralytics
OpenCV
Roboflow
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