This tutorial is based on the YOLOv5 repository by Ultralytics. This notebook shows training on your own custom objects. Many thanks to Ultralytics for putting this repository together - This algorithm combine with clean data management tools at Roboflow.

I recommend that you follow along in this notebook while reading the blog post on how to train YOLOv5, concurrently.

In this tutorial, we will walk through the steps required to train YOLOv5 on the custom objects. We use our own data gathered from Real World. You can also use this notebook on your own data.

To train our detector we take the following steps:

• Install YOLOv5 dependencies
• Download custom YOLOv5 object detection data
• Write our YOLOv5 Training configuration
• Run YOLOv5 training
• Evaluate YOLOv5 performance
• Visualize YOLOv5 training data
• Run YOLOv5 inference on test images
• Export saved YOLOv5 weights for future inference

If you have a Strong System to run and Train your model, you can use your local machine in <colab.research.google> by Typing below code in the terminal.

Before that enter below command in terminal:

Conda activate (your env)

Then: jupyter notebook --NotebookApp.allow_origin='https://colab.research.google.com' --port=8888 --NotebookApp.port_retries=0

This Training has been inspired by Roboflow.ai Blog