How to Save and Resume Training on a YOLOv8 Model with Custom Datasets

Discover how to easily save your YOLOv8 model after training and continue where you left off, ensuring efficient and effective training sessions.
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How to Save and Resume Training on a YOLOv8 Model with Custom Datasets

When working with machine learning models, particularly in object detection, the ability to save a model after a training session is crucial. This allows you to efficiently utilize your computational resources, save your progress, and continue training later without starting from scratch. In this guide, we’ll explore how to save a YOLOv8 model after training on a custom dataset, so you can come back to it later and continue your work seamlessly.

The Challenge

You may be training a YOLOv8 model in Google Colab using a custom dataset. After completing several epochs, you may wish to save your model. However, the methods you’ve used might not yield the expected results, as they do not seem to work well with the YOLOv8 framework.

Here’s a snippet of the code you might be using to train your model:

[[See Video to Reveal this Text or Code Snippet]]

And the following lines were intended to save the model's weights:

[[See Video to Reveal this Text or Code Snippet]]

Despite this approach, you encounter issues when trying to save or load the model. So, how can you effectively save your progress?

The Solution

Automatic Model Weight Saving

The good news is that YOLOv8 has built-in functionalities that cater to this requirement. After you train your YOLOv8 model, the best weights are automatically saved. Here’s how to handle this correctly:

Default Saving Mechanism:

When you train your model, YOLOv8 automatically saves the best weights in the directory:

[[See Video to Reveal this Text or Code Snippet]]

This file is saved as best.pt, which contains the optimal weights based on your training.

Resuming Training

To continue your training after saving the model, follow these steps:

Load the Best Weights:

When you are ready to resume training, simply load the best.pt file instead of yolov8x.pt. This merges all previous training knowledge with your current session.

Here’s how you can achieve that:

[[See Video to Reveal this Text or Code Snippet]]

Continue Training:

You can specify how many additional epochs you want to train the model further, allowing you to extend your training beyond the starting point.

Summary of Steps

Train your model using:

[[See Video to Reveal this Text or Code Snippet]]

Find your saved weights at:

[[See Video to Reveal this Text or Code Snippet]]

Resume training by loading the best weights:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

Saving your YOLOv8 model after training on custom datasets allows you to effectively manage your training sessions and makes it easy to continue later. By utilizing the built-in mechanism to save the best weights, you can efficiently progress your project without losing earlier efforts. So, take advantage of this feature and enhance your workflow today!

If you have any questions or want to share your experiences with YOLOv8 training, feel free to leave a comment below!

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