The texture of fabric is considered to be an important factor of the design and prediction of Machine-made fabric and handmade fabric. Traditionally, the recognition of fabric has a lot of challenges due to its manual visual inspection. Moreover, the approaches based on early machine learning algorithms directly depend on handcrafted features, which are time-consuming and error-prone processes. Hence, an AI system is needed for classification of fabric in Machine-made and Handmade. In this Project Report, we propose a deep learning model based on data augmentation and learning approach for the classification and recognition of Difference between Machine-made and Handmade Fabric. The fabric images are enhanced by pre-processing at various levels using conventional image processing techniques and they are used to train the networks. The model uses the residual network (VGG16).We evaluated the results of our model using evaluation metrics such as accuracy, balanced accuracy, and F1-score. With the Fabric dataset, a maximum classification accuracy of 80% is achieved in the conducted simulations. The experimental results show that the proposed model is robust and achieves state-of-the-art accuracy even when the physical properties of the fabric are changed. We compared our results with other baseline approaches and a pertained Resnet50 deep learning model which showed that the proposed method achieved higher accuracy. Also we create a Web Application of this model on Streamlit which is open-source framework to rapidly build web apps. It is a Python-based library specifically designed for machine learning engineers.