Hi guys. Quick question. Maybe one of you can help me with it.
Let’s say I have trained a model that detects a specific object in images like cats and after a few weeks I collected new data that I would like to use to further improve my model. Can I continue the training of the saved model with the new data or do I have to “create” (train) a new model based on the old and new data sets?
Nothing, concerning what I want the object detection model to detect, changes. I simply want to give it more data to improve but I can’t find an answer to the question, if I should train a completely new model or if I can continue the training of the current one?
You’re correct about having two options for improving your cat detection model with new data: continuing training the existing model or training a new one from scratch with both the old and new data. Both approaches have their advantages and disadvantages:
1. Continuing training the existing model:
Advantages:
Faster training time as the model already has some knowledge about cats.
Potentially better performance on initial examples it was trained on due to retaining those learned features.
Avoids training a model from scratch, saving computational resources.
Disadvantages:
Might be prone to overfitting to the new data, especially if the new data has different characteristics or biases.
May not learn new features effectively if the initial model architecture is limited.
Requires careful consideration of learning rate and optimizer settings to avoid destabilizing the existing training progress.
2. Training a new model from scratch with both datasets:
Advantages:
Leverages all available data, potentially leading to better overall performance and adaptability.
Less risk of overfitting to specific characteristics of either dataset.
Allows for utilizing a different model architecture if needed to capture new features in the data.
Disadvantages:
Requires more training time than continuing the existing model.
May potentially lose some performance on the initial examples used in the first training round.
Requires more computational resources, especially for complex model architectures.
Choosing the best approach:
The best approach depends on several factors, including:
Size and diversity of the new data: If the new data is small or doesn’t significantly differ from the old data, continuing training might be sufficient.
Desired model performance: If you need the best possible performance across all types of cat images, training a new model from scratch might be better.
Computational resources: Training a new model from scratch requires more resources, so continuing training might be preferred if resources are limited.
Time constraints: Continuing training might be faster, especially for large datasets.
Recommendation:
It’s generally recommended to try both approaches and compare the resulting models’ performance on a validation set. This will give you the most concrete data-driven decision on which approach is best for your specific case.
There’s no one-size-fits-all answer, and the best approach depends on your specific needs and data.
