Self-Training for Image Classification

This project explores self-training techniques for improving image classification performance. Self-training allows a model to generate pseudo-labels for unlabeled data and then retrain the model with these labels to improve its accuracy. The repository includes the implementation of self-training algorithms in the context of image classification and demonstrates how to use self-training in scenarios where labeled data is limited or unavailable.