Train Yolov8 on Google Colab | Custom Dataset | Object Detection Tutorial

Table of Contents

1. Introduction
2. Setting up the Environment
3. Preparing the Data
   1. Creating the Directory Structure
   2. Downloading the Dataset
4. Training the Object Detector
   1. Mounting Google Drive
   2. Defining the Project Directory
   3. Installing Ultralytics
   4. Executing the Training Cells
5. Evaluating the Results
   1. Accessing the Output
   2. Downloading the Model Weights
6. Conclusion

Introduction

Welcome to this Tutorial on how to train an object detector using YOLO V8 and Google Colab. In this video, I will guide you through the process of training an object detector on your own custom data. We will use the YOLO V8 model and leverage the power of Google Colab for training. By following the steps in this tutorial, you will be able to train your own object detector in just a few simple steps.

Setting up the Environment

Before we begin, we need to set up the environment for training. This includes creating a directory in your Google Drive where we will store all the necessary data and files. By organizing the data in a specific structure, we will ensure a smooth training process.

Preparing the Data

The first step in preparing the data is to create the directory structure required for training. This structure includes separate directories for images and labels, as well as subdirectories for training and validation data. It is crucial to follow this structure exactly as it is shown in order for the training process to work properly.

Next, we need to obtain the data we will use for training. In this tutorial, we will be using a dataset of squirrel images. However, you can use any dataset you prefer for your object detection task. I will also provide you with a resource where you can download a diverse dataset with various categories and annotations.

Training the Object Detector

Now that we have our data prepared, we can proceed with training the object detector. The first step is to mount your Google Drive to Google Colab, allowing us to access the data stored in your drive. We will define the project directory, which is the location of your data in Google Drive. Make sure to update this variable with the correct location.

To train YOLO V8 on your custom dataset, we need to install the Ultralytics Python library. This library provides the necessary tools and functionalities for training the detector. Once the installation is complete, we are ready to execute the training cells in the notebook. This will initiate the training process, which may take some time depending on the number of images and epochs specified.

Evaluating the Results

After the training process is completed, we can evaluate the results and assess the performance of our object detector. The output of the training process, including plots and images for evaluation, will be saved in a directory. We can access this directory and download the necessary files for further analysis.

Conclusion

Congratulations! You have successfully trained an object detector on your own custom data using YOLO V8 and Google Colab. By following the steps outlined in this tutorial, you have learned how to set up the environment, prepare the data, train the detector, and evaluate the results. Keep experimenting and refining your object detection models for various use cases.

Thank you for watching this tutorial, and I hope you found it informative and useful. If you have any further questions or need additional assistance, please refer to my previous videos or reach out to me. Happy object detection training!

Now, let's move on to some highlights of this tutorial.