RSK World - Vision Transformer (ViT) Image Classification | Deep Learning | Transformers | PyTorch TensorFlow | RSK World

Vision Transformer (ViT) Image Classification Project - RSK World

Deep Learning Image Classification Python PyTorch TensorFlow Computer Vision

This project implements Vision Transformer (ViT), a transformer-based architecture for image classification that splits images into patches and processes them as sequences using self-attention mechanisms. Unlike CNNs, ViT uses patch-based embeddings, positional encoding, and multi-head self-attention layers to achieve state-of-the-art results. Perfect for image classification tasks with high accuracy, featuring PyTorch and TensorFlow implementations, attention visualization, model comparison, and advanced data augmentation techniques.

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Vision Transformer (ViT) Architecture

Transformer-based architecture that splits images into patches and processes them as sequences using self-attention mechanisms for state-of-the-art image classification.

Patch-based image embedding
Multi-head self-attention mechanism
Positional encoding for spatial information
State-of-the-art classification accuracy

PyTorch & TensorFlow Implementation

Complete implementations in both PyTorch and TensorFlow/Keras frameworks for flexibility and comparison.

PyTorch implementation with advanced features
TensorFlow/Keras implementation
Transfer learning support
Configurable training parameters

Attention Map Visualization

Visualize which parts of images are important for model predictions using attention maps and patch visualization tools.

Attention map visualization
Patch visualization tools
Model attention analysis
Interactive visualization

Model Ensemble & Batch Prediction

Combine multiple models for improved accuracy and predict on multiple images at once.

Model ensemble utilities
Batch image prediction
Directory scanning support
Multiple voting methods

Performance Metrics & Visualization

Detailed evaluation with confusion matrix, training curves, and comprehensive performance metrics.

Confusion matrix visualization
Training history plots
Accuracy and precision metrics
Model performance comparison

Jupyter Notebook

Interactive Jupyter Notebook for Vision Transformer (ViT) training, evaluation, and experimentation.

Interactive training notebook
Step-by-step analysis
Model experimentation
Visualization examples

Advanced Data Augmentation

Enhance training data with advanced augmentation techniques including MixUp, CutMix, Random Erasing, and AutoAugment.

MixUp augmentation
CutMix augmentation
Random Erasing
AutoAugment support

Model Analysis Tools

Comprehensive model analysis including parameter counting, model size analysis, and comparison utilities.

Parameter counting
Model size analysis
Model comparison tools
Performance benchmarking

Configuration Management

YAML-based configuration system for easy customization of training parameters and model settings.

YAML configuration files
Easy parameter customization
Training configuration
Model settings management

Sample Data Utilities

Tools to prepare and organize training data with sample data generation and structure creation.

Sample data generation
Data structure creation
Dataset organization tools
Training data preparation

Data Preprocessing

Robust data preprocessing pipeline for image dataset preparation, normalization, and augmentation.

Image dataset loading
Data normalization
Train/val/test split
Image preprocessing utilities

Multiple Model Sizes

Support for different ViT model configurations including Tiny, Small, Base, and Large variants for various use cases.

ViT-Tiny configuration
ViT-Small configuration
ViT-Base configuration
ViT-Large configuration

Training Visualization

Comprehensive visualization utilities for training history, metrics, and prediction analysis.

Training history plots
Confusion matrix visualization
Prediction visualization
Model comparison charts

Utility Functions

Helper functions for logging, file management, model utilities, and common development tasks.

Logging setup and management
Directory creation utilities
Model utility functions
Common development helpers

Requirements

The following are the technical requirements for this project:

Python 3.8+
PyTorch 2.0+
TensorFlow 2.13+
Keras 2.13+
NumPy, PIL, Matplotlib
Jupyter Notebook 1.0.0+

Credits & Acknowledgments

This project is developed for educational purposes and utilizes the following resources:

Python - PSF License
PyTorch - BSD License
TensorFlow - Apache 2.0 License
Keras - Apache 2.0 License
RSK World - Project Inspiration
GitHub Repository - Source code and documentation

Support & Contact

For paid applications, please contact us for integration help or feedback.

Support Email: help@rskworld.in
Contact Number: +91 9330539277
Website: RSKWORLD.in
GitHub Project
Join Our Discord
Slack Support Channel
Vision Transformer (ViT) Image Classification Documentation

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Technologies

Python 3.8+

Keras

PyTorch

TensorFlow

Transformers

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Vision Transformer Image Classification Deep Learning Open Source

Vision Transformer (ViT) Architecture

PyTorch & TensorFlow Implementation

Attention Map Visualization

Model Ensemble & Batch Prediction

Performance Metrics & Visualization

Jupyter Notebook

Advanced Data Augmentation

Model Analysis Tools

Configuration Management

Sample Data Utilities

Data Preprocessing

Multiple Model Sizes

Training Visualization

Utility Functions

Requirements

Credits & Acknowledgments

Support & Contact

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