Best practice for training YOLOv8 with very small datasets

[haimat]

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Question

I understand that it is always preferrable to have many hundrets or even better thousands of examples per class in the training dataset of a YOLOv8 model. However, in some projects this is not an option - for example for a very specific problem it might simply not be possible to generate so many samples of the object to detect.

So let's say we have only a very few images of the object we want to detect, something between 10 and 30. I understand that one way to tackle this problem is to create more example images by augmenting the few original images we have. But even with that approach we can only generate so many new images - in the end we still might not end up with thousands of training images.

So generally speaking, what is the "best practice" approach for training a YOLOv8 model when we only have such a small training dataset?

Additional

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[PiyushKamboj1503]

@haimat best practice here would be to use the .pt pretrained models. Also, if your class is in the already trained classes, you can use the weight transfer/transfer learning

[haimat]

@PiyushKamboj1503 Well, we are using pretrained models anyway. But the objects to be detected are sometimes very industrial-specific, not related to the objects pre-trained into the models ...

[glenn-jocher]

Hello @haimat,

Dealing with a very small dataset for training an object detection model is indeed challenging, but there are strategies you can employ to increase the chances of achieving good performance with YOLOv8:

1. Pretrained Models: As you've mentioned, starting with a pre-trained model is important. These models have learned features from a vast and diverse dataset, which can be beneficial even when your target classes are not part of the pretrained dataset.

2. Transfer Learning: Even if the pre-trained classes do not overlap with your industrial-specific objects, the learned features can still be useful. You can fine-tune a pretrained YOLOv8 model on your small dataset, which allows it to adapt the general features it has learned to your specific task.

3. Data Augmentation: This is crucial when dealing with limited data. Techniques like random rotations, flipping, scaling, cropping, and color space transformations can generate more varied training examples. YOLOv8 has built-in data augmentation that you can leverage during training.

4. Overfitting Prevention: Since your dataset is small, your model might overfit quickly. To prevent this, use strong regularization techniques, keep the network's capacity in check (i.e., avoid using a very large model), and closely monitor your validation performance.

5. Transfer from Similar Classes: If the pretrained model contains classes that are visually or contextually similar to your target objects, you may have better transfer learning results, as the model has already learned relevant features.

6. Few-Shot Learning: Look into few-shot learning techniques that are designed to work well with a limited number of training samples. These methods are often more robust to overfitting when dealing with small datasets.

7. Ensemble Methods: If possible, train several models with different initializations or architectures, and then combine their predictions. This can sometimes yield better performance than any single model.

8. Domain-Specific Pretraining: If you have access to a larger, yet still relevant dataset from a similar domain, pretraining on this before fine-tuning on your small dataset can be beneficial.

9. Hyperparameter Tuning: Be prepared to experiment with different hyperparameters, particularly those controlling the model's capacity to learn and generalize.

10. Synthetic Data: If applicable, consider generating synthetic data that closely resembles your actual objects of interest to supplement your training dataset.

11. **

[haimat]

@glenn-jocher Thank you very much for your reply, Glenn.

[glenn-jocher]

@haimat hello,

Training YOLOv8 with a very small dataset is a common challenge, but there are strategies to improve performance:

1. Use Pretrained Weights: Start with the weights of a pretrained YOLOv8 model as the foundation for your training. This leverages the knowledge gained from large datasets and often leads to better performance even when the exact classes do not match your specialized use case.

2. Fine-tuning: Fine-tune the pretrained model on your small dataset. This allows the model to adjust the learned features to better match your specific target objects.

3. Data Augmentation: Make the most of your data through augmentation strategies inbuilt into YOLOv8, such as random cropping, flipping, and color adjustments. Being creative here can substantially increase the effective size of your dataset without collecting new images.

4. Regularization: Use regularization techniques to prevent overfitting, such as dropout or weight decay. Carefully monitor the training to ensure the model is generalizing well and not just memorizing your limited training data.

5. Few-shot Learning: Consider methods specially designed for training on limited data, which might help the model to learn from a small number of examples more effectively.

6. Hyperparameter Optimization: Optimize the hyperparameters thoughtfully. This may include adjusting the learning rate, batch size, or number of epochs to find the best settings for your dataset.

7. Model Size Selection: With a small dataset, a smaller model can sometimes generalize better. Avoid models that are too large as they may be more prone to overfitting.

8. Gradual Unfreezing: Gradually unfreeze your model layers—from top to bottom—allowing for selective fine-tuning.

Lastly, while strategies like synthetic data generation and ensembling may be beyond the scope of YOLOv8's built-in tools and are not covered in this response, they can also be considered in larger pipeline development if resources allow for it.

Let's keep the dialogue open, and feel free to reach out if you face specific issues during your training process.

Best, Glenn Jocher

[haimat]

@glenn-jocher Thanks for your feedback. Two questions:

• Do you have more info on few-shot learning (in the context of YOLOv8)?
• Is the more documentation or howtos on how to (un-)freeze layers in YOLOv8?

[glenn-jocher]

@haimat,

Few-shot learning aims to train a model using a minimal amount of labeled data per class. In the context of YOLOv8, while there isn’t a native few-shot learning setup, you can adapt your training pipeline to support this approach.

Here are a few common methods for few-shot learning in object detection:

1. Meta-Learning: Meta-learning algorithms, like MAML (Model-Agnostic Meta-Learning), aim to train a network in a way that allows it to adapt quickly to new tasks with minimal training. This can be applied to object detection tasks although the integration with YOLOv8 would require custom implementation.

2. Data Augmentation: Use aggressive data augmentation strategies, especially when training on a small number of samples per class. Techniques like mixup, cutmix, or semantic augmentation can help the model generalize better from few examples.

3. Pretraining on Similar Classes: If available, pretrained models or features on related classes can provide a good initialization when fine-tuning your YOLOv8 model on the few-shot dataset.

Regarding (un-)freezing layers in YOLOv8, here's an outline of the typical process:

1. Freezing Layers: When performing transfer learning or initial training, you might choose to freeze layers by setting their requires_grad attribute to False. In YOLOv8, the lower convolutional layers are good candidates for freezing during initial training.

2. Unfreezing for Fine-Tuning: During fine-tuning, you can gradually unfreeze layers starting from the top and moving downwards. This allows the model to fine-tune its parameters on the specific dataset while still retaining the general features learned from the pretrained weights.

For more specific documentation on implementation, I recommend referring to the official YOLOv8 repository and the associated documentation in its README. Additionally, exploring community-contributed tutorials or blog posts might provide insights into practical implementation details for few-shot learning and layer freezing in YOLOv8.

I hope this guidance provides a good starting point for your work. Don’t hesitate to reach out if you have further inquiries or need more detailed information on these topics.

Best regards, Glenn Jocher

[haimat]

@glenn-jocher Thanks Glenn, that helps a lot :+1:

[glenn-jocher]

@haimat, you're welcome! I'm glad to hear the information was helpful. Good luck with your YOLOv8 training efforts, and should you have any more questions down the line, feel free to reach out again. Your successes and challenges contribute to the growth of the YOLO community and Ultralytics. Happy detecting!

[haimat]

@glenn-jocher Many thanks back to you, Glenn. It's really great that you share this awesome piece of software with the community. Keep up the good work :+1:

[glenn-jocher]

@haimat, it's truly a pleasure, and the positivity and contributions from users like you enhance the project for everyone involved. Remember, this is a collaborative effort and your feedback and experiences are invaluable. Enjoy working with YOLOv8!

[H0128Z]

@haimat Hello, I'm also trying to apply MAML to YOLOv8 for few-shot learning. I was wondering if I could ask how you managed to resolve the issue mentioned above?

Thank you!

[haimat]

@H0128Z Hi, wie ended up augmenting the existing images we had ...

[H0128Z]

@haimat oh, thanks for your reply.