Building Vision Transformer Model from scratch using Pytorch

🔍 Building Vision Transformer (ViT) Model from Scratch using PyTorch – Full End-to-End Project In this deep dive tutorial, we walk you through building a Vision Transformer (ViT) model from scratch using PyTorch—no transfer learning, no shortcuts. You'll learn the core architecture of ViT, how to preprocess image data, implement the transformer encoder, and make accurate predictions on the CIFAR-10 dataset. 🚀 What You’ll Learn: How Vision Transformers work vs CNNs Image patching and positional encoding explained Building a full ViT architecture step by step Training and evaluating the model from scratch Making predictions and visualizing the results Practical tips for improving ViT performance 🧠 Whether you're an AI enthusiast, a deep learning student, or a researcher looking to understand the internals of Vision Transformers, this project is for you! 💻 Technologies Used: Python PyTorch NumPy, Matplotlib CIFAR-10 dataset 00:00 Intro 01:23 Theoretical Explanation of Vision Transformers 20:40 Environment Setup and Library Imports 28:14 Configurations and Hyperparameter Setup 31:28 Image Transformation Operations 33:28 Downloading the CIFAR-10 Dataset 37:22 Creating DataLoaders 44:32 Building the Vision Transformer (ViT) Model 1:16:41 Defining Loss Function and Optimizer 1:18:37 Training Loop and Model Training 1:36:18 Visualizing Accuracy (Training vs Testing) 1:39:08 Making and Visualizing Predictions 1:51:48 Fine-Tuning with Data Augmentation 1:58:08 Training the Fine-Tuned Model 2:00:08 Visualizing Fine-Tuned Accuracy 2:01:38 Predictions After Fine-Tuning ✅ Code Available on GitHub 📌 Don't forget to like, subscribe, and turn on notifications for more deep learning tutorials! #VisionTransformer #PyTorch #DeepLearning #ComputerVision #ViT #AIProjects