Content-Based-Image-Retrieval-using-Autoencoders-Unsupervised-deep-learning-algorithms. In this study, Autoencoders can be used for finding similar images in an unlabeled image dataset. Specifically this code work with a small training database of 5 common item classes: tom, jerry, building, human faces and some food items. The performance of the algorithms provided are far from perfect, but provide for a good starting point for interested in deep learning image retrieval. Content based image retrieval (CBIR) systems enable to find similar images to a query image among an image dataset. In this code, we used three methods such as convAE, simpleAE and vgg16. In CBIR, images are indexed by their visual content, such as color, texture, shapes. Basically we first extract features from an image database and store it. Then we compute the features associated with a query image. Finally we retrieve images with the closest features.