Training custom datasets with Ultralytics YOLOv8 in Google Colab

In this post, we look at how to train a custom object detection model with Ultralytics YOLOv8 in Google Colab, walking through the entire process from setup to training and evaluation.

Setting Up Ultralytics 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 the ultralytics package, which includes YOLOv8 and its dependencies, using pip. Then import the YOLO class from ultralytics to start building your custom object detection model.

Labeling 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 in the Ultralytics YOLO format and import it into your Google Colab notebook.

Training your custom Ultralytics 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. GPU acceleration keeps training fast as your model learns from the data.

Evaluating and validating your model#

Once training is complete, evaluate your model's performance using metrics like mean average precision (mAP). Validate your model on unseen data to ensure it generalizes well. Plot confusion matrices and analyze predictions to fine-tune your model further.

Ultralytics YOLOv8 models can be validated with a single CLI command, such as yolo val model=path/to/best.pt, which automatically reuses the settings from training and reports multiple metrics.

Ultralytics also supports CLI and Python arguments that you can use during validation to tailor the output to your needs. For more information, see the validation mode documentation.

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

Taking your model to the next level#

You have now successfully trained your custom Ultralytics YOLOv8 model in Google Colab. In our next video, we'll explore how to export model weights and run live inference using our custom-trained YOLOv8 model.

Wrapping up#

Thank you for joining us as we looked at the world of custom object detection with Ultralytics YOLOv8 in Google Colab. Stay tuned for more tutorials on 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 to follow along step by step.