Hi there, I am planning to train a dataset containing 1316 images, and I am wondering whether there is a way of determining the number of classes depending on the size of the dataset to avoid class imbalance.
Thank you!
Hi Anusha_Gamlath,
Thanks for reaching out! The number of classes for your project should be determined by your specific goals – what distinct objects or categories do you need the model to recognize? It’s not directly dependent on the total dataset size (1316 images in your case).
Class imbalance occurs when you have significantly fewer images for some classes compared to others after you’ve defined them. First, decide on the classes based on your project requirements. Then, you can analyze the distribution of your 1316 images across those chosen classes to check for potential imbalance.
You can find more guidance on defining classes in our Data Collection and Annotation Strategies guide.
Hope this helps clarify things!