Introduction to Vision Transformers: Image Classification with ViT and DeiT

Traditional convolutional neural networks are no longer the only option for computer vision. Transformers, originally designed for natural language processing, have revolutionized how we analyze and classify images. This written course guides you through the core concepts of Vision Transformers (ViT) and Data-efficient Image Transformers (DeiT). You will transition from understanding basic self-attention mechanisms to implementing and fine-tuning these powerful architectures for practical image classification tasks. What you'll learn: - Understand the foundational mechanics of self-attention and how it applies to visual data instead of text. - Analyze the core architecture of Vision Transformers (ViT), including patch projection and position embeddings. - Explore Data-efficient Image Transformers (DeiT) and the role of knowledge distillation in training smaller models. - Implement image classification workflows using modern deep learning libraries and pre-trained transformer models. - Evaluate model performance using standard classification metrics and understand the trade-offs between CNNs and Transformers. You will start with essential terminology and the conceptual shift from convolutional layers to self-attention. From there, you will read through step-by-step code walkthroughs that demonstrate how to prepare image patches, configure ViT and DeiT architectures, and fine-tune them on custom datasets. This course is designed for aspiring data scientists, machine learning beginners, and computer vision enthusiasts. A basic familiarity with Python and neural network concepts is helpful, but no prior experience with transformers is required. Start reading today to unlock the power of attention-based computer vision.