Text and image classification models (Multimodal) are a type of machine learning model that can classify images based on their content and text descriptions. These models are useful in a wide range of applications such as image search, content recommendation, and more. In this post, we will explore how to build a text and image classification model using PyTorch and TensorFlow. We will cover the steps involved in data preparation, text and image embedding, model architecture, training, and evaluation. We will also provide code examples in PyTorch and TensorFlow to help you get started. By the end of this post, you will have a good understanding of how to build a text and image classification model and how to apply it to your own projects. How to Implement a Text and Image  Classification Model 1. Data Preparation: Collect a dataset of images and their corresponding text descriptions. Preprocess the images and text to ensure they are in a format that can be used by the model. 2. Text Em