Training custom datasets with Ultralytics YOLOv8 in Google Colab

In this blog we'll look at how to master custom object detection using Ultralytics YOLOv8 in Google Colab. Get ready to unleash the power of YOLOv8 as we guide you through the entire process, from setup to training and evaluation.

Link to this sectionSetting Up YOLOv8 model in Google Colab#

Let's kick things off by setting up our environment in Google Colab. So what is Google Colab? Short for Google Colaboratory, Google Colab is a free cloud platform by Google for writing and running Python code.

The first step to set this up is to ensure you have access to a GPU by selecting the appropriate runtime type. Check that everything's running smoothly by using the nvidia-smi command to verify your GPU setup.

Next, install Ultralytics and YOLOv8 dependencies using pip. Import the YOLO model from Ultralytics to get started on our custom object detection journey.

Link to this sectionLabeling and preparing your dataset#

Now, let's prepare our dataset. Label your data with bounding boxes, specifying the classes for each object. Export your dataset to the YOLOv8 format from Ultralytics and import it into your Google Colab notebook.

Link to this sectionTraining your custom YOLOv8 model#

Set the task to detect for object detection and choose the YOLOv8 model size that suits your needs. Specify the location of your dataset, the number of epochs, and image size for training. Watch as your model learns and adapts, thanks to the power of YOLOv8 and GPU acceleration.

Link to this sectionEvaluating and validating your model#

Once training is complete, evaluate your model's performance using metrics like mean error position. Validate your model on unseen data to ensure its generalization capabilities. Plot confusion matrices and analyze predictions to fine-tune your model further.

Ultralytics YOLOv8 models can be validated easily with a single CLI command, that has multiple key features i.e. auto hyperparameters setting, multi metrics support, and so on.

Ultralytics also supports some CLI and Python arguments that users can use during validation for better output results based on their needs. For more information, you can explore our docs.

Fig 1. Nicolai Nielsen outlining how to train custom datasets with Ultralytics YOLOv8 in Google Colab.

Link to this sectionTaking your model to the next level#

You have now successfully trained your custom YOLOv8 model in Google Colab. But our journey doesn't end here. In our next video, we'll explore how to export model weights and run live inference using our custom-trained YOLOv8 model. Get ready for an exhilarating experience as we push the boundaries of object detection. Stay Tuned!

Link to this sectionWrapping up#

Thank you for joining us as we looked at the world of custom object detection with YOLOv8 in Google Colab. Stay tuned for more exciting updates and tutorials as we continue to explore the limitless possibilities of AI and machine learning.

With this comprehensive guide, you're now equipped to train your own custom object detection models using Ultralytics YOLOv8 in Google Colab. Watch the full tutorial Watch the full tutorial!