Best practices to train model with many similar classes

Question

Hello,
I am currently creating a model for the classification of clothing with Azure Custom Vision. In the process, I have some projects which need to distinguish many similar classes. For example, I have a project for classification of outerwear. This contains very similar classes like V-neck T-shirt, polo T-shirt, pocket T-shirt, etc.
What is a good approach to classify these very similar classes with one model as accurately as possible? Simply uploading more data doesn't help anymore, unfortunately.

I would be very happy to get an answer.
Best regards
Eric

Answer 1

Hi @romungi-MSFT ,
thanks for the recommendations. I've already set the domain to retail.
I have not yet tried multi-label classification. This is unfortunately not possible with my data, because I have no information about sleeve length, fit, pocket, hood, design & print. For other categories like shoes or pants, I also have to distinguish between many similar categories. There I have again other properties than sleeve length, fit, pocket, hood, design & print.

So far it has increased the accuracy if I have as few classes per project as possible. I also add blurred and grayscale images of the items to the projects to train the model with a higher variety of images. Furthermore I, classify the images in a hierarchical arrangement of many projects. For example, a t-shirt is classified as follows: Clothing --> Outerwear --> Shirts ---> T-shirts.

Answer 2

Thanks for sharing. This solution is really helpful for my project Lil peep apparel categories.