Vision Transfer Learning

📋 Project Overview

This project explores the application of Data-efficient Image Transformers (DeiT) for image classification tasks using transfer learning. DeiT represents a breakthrough in vision transformers, achieving competitive performance with CNNs while requiring less data and computational resources. The project demonstrates how to effectively fine-tune pre-trained transformer models for custom classification tasks.

By leveraging transfer learning with DeiT, we can achieve state-of-the-art results on image classification tasks with limited training data, making it particularly valuable for domain-specific applications where large datasets are not available.

💡 Problem Statement

Traditional deep learning approaches face several challenges:

• Data Requirements: Training vision models from scratch requires massive datasets
• Computational Cost: Training large models is resource-intensive and time-consuming
• Domain Adaptation: Models trained on general datasets may not perform well on specific domains
• Limited Data Scenarios: Many real-world applications have limited labeled data
• Model Efficiency: Balancing accuracy with model size and inference speed

⚡ Solution Approach

The project implements DeiT-based transfer learning:

• Pre-trained DeiT Models: Leverage models trained on ImageNet with knowledge distillation
• Fine-tuning Strategy: Progressive unfreezing and differential learning rates
• Data Augmentation: Advanced augmentation techniques including MixUp, CutMix, and RandAugment
• Knowledge Distillation: Utilize teacher-student training paradigm for better performance
• Adaptive Fine-tuning: Layer-wise learning rate scheduling for optimal adaptation
• Ensemble Methods: Combine multiple models for improved robustness

🛠️ Technical Implementation

DeiT Architecture

• Vision Transformer: Patch-based image processing with self-attention mechanisms
• Distillation Token: Learnable token that distills knowledge from teacher CNN
• Multi-head Attention: Captures long-range dependencies in images
• Position Embeddings: Encodes spatial information for patch sequences
• Classification Head: MLP for final class predictions

Training Pipeline

• Data Preparation: Image preprocessing, normalization, and dataset splitting
• Augmentation: RandAugment, MixUp, CutMix, and AutoAugment strategies
• Transfer Learning: Load pre-trained weights and adapt to target domain
• Fine-tuning: Two-stage training with frozen and unfrozen layers
• Optimization: AdamW optimizer with cosine annealing schedule
• Regularization: Dropout, weight decay, and label smoothing
• Evaluation: Top-1 and Top-5 accuracy, confusion matrix, and per-class metrics

📚 Key Learnings

• Vision Transformers: Understanding how transformers work for image classification
• Transfer Learning: Best practices for adapting pre-trained models to new tasks
• Data Augmentation: Advanced techniques for improving model generalization
• Knowledge Distillation: Using teacher-student training for model compression
• Fine-tuning Strategies: Layer-wise learning rates and progressive unfreezing
• Model Comparison: Evaluating transformer vs CNN architectures

🚀 Future Enhancements

• Exploring newer vision transformer architectures (Swin, PVT, etc.)
• Multi-task learning for simultaneous classification and localization
• Few-shot learning capabilities for rapid domain adaptation
• Model compression techniques for edge deployment
• Attention visualization for model interpretability
• Active learning strategies for efficient data collection
• Federated learning for privacy-preserving training

Skills Demonstrated