Car Model Prediction Using Deep Learning

In today’s globe, there are numerous car models to choose from. We are utilising deep learning algorithms to anticipate the various car models. Deep learning has been demonstrated to be a successful method for increasing the precision of model prediction in a certain set of data. For the prediction of a picture, we use a deep learning technique with various parameters including image size and the quantity of images in data sets.The goal of this research is to create a reliable and effective method for predicting car models. For each new task at hand, we create a model in classical (machine) learning and train it on fresh data.Transfer learning is being used in this project, which is different from that transferring information from one work to another is the approach. With the aid of transfer learning, we can complete a task utilising all or a portion of a model that has already been trained for a separate job. ResNet is a deep learning model that is employed in computer vision applications. It is a convolutional neural network (CNN), which is mostly used for image processing, pattern recognition, and other related tasks. This study seeks to resolve this problem by enhancing the projected model’s accuracy through the use of deep learning techniques. The project uses oversampling, undersampling, and hybrid sampling approaches, among other strategies, to deal with unbalanced datasets.The project’s findings demonstrate that the deep learning-based method that has been suggested is quite good at predicting car models.