In the recent past, promising performance has been achieved by supervised deep learning networks in various domains like computer vision, machine translation, speech recognition, natural language processing (NLP), etc. However current deep-learning approaches require a large amount of manual data to achieve promising performance. However, manual annotation of data is time-consuming and needs human expertise, which is always not possible in a domain like healthcare. Recently an alternative method called Self Supervised Learning (SSL), uses unlabeled data and does not require manual annotation. With the help of a pretext task, SSL extracts and learns features from the unlabeled data, enabling models trained for these tasks to acquire latent representations that enhance subsequent tasks like object detection and classification. In this work, the unlabeled data is fed to the convolution neural network (CNN), which learns features and transfers them to the downstream task to generate the labeled data. The experiments are conducted on the fashion MNIST dataset in a self-supervised learning environment and a comparison is made with a supervised learning environment. The experiment concludes that self-supervised learning achieves similar performance on unlabeled data which saves the high cost of data labeling. The self-supervised learning environment is preferable in various domains where human annotation for labeling datasets is very costly or not feasible.