MobileViT/README.md

Custom Vision Transformer for Fine-Grained Classification

Business Context & Use Case

Scenario: Build a state-of-the-art computer vision system for automotive industry that requires fine-grained vehicle classification with high accuracy and robustness. The system needs to distinguish between 196 different car models while maintaining performance under various real-world conditions (lighting variations, blur, compression artifacts).

"Classify these vehicle images with confidence scores, compare performance against pre-trained models, analyze robustness under different noise conditions, and provide detailed performance metrics across different architectural configurations."*

This requires custom Vision Transformer implementation, extensive experimentation, hyperparameter optimization, and comprehensive performance analysis across multiple evaluation scenarios.

Technical Architecture Requirements

Infrastructure Setup (Required)

1. Dataset Integration - Stanford Cars Dataset

Dataset Details:

• Training Set: 8,144 images for model training
• Test Set: 8,041 images for standard evaluation
• Robustness Test Sets: 7 corrupted versions (8,041 images each)
  • Contrast variations
  • Gaussian noise
  • Impulse noise
  • JPEG compression artifacts
  • Motion blur
  • Pixelation effects
  • Spatter corruption
• Classes: 196 fine-grained car categories
• Task: Multi-class classification with high inter-class similarity

from datasets import load_dataset
dataset = load_dataset("tanganke/stanford_cars")

2. Custom Vision Transformer Architecture

Core ViT Components (Must Implement from Scratch)

• Patch Embedding Layer: Configurable patch size (8x8, 16x16, 32x32)
• Multi-Head Self-Attention: Custom attention mechanism with configurable heads
• Transformer Encoder Blocks: Variable depth with residual connections
• Classification Head: Configurable hidden dimensions and dropout rates
• Positional Encoding: Learnable vs fixed positional embeddings

Advanced Features (Required)

• Hierarchical Attention: Multi-scale feature extraction
• Attention Pooling: Alternative to CLS token classification
• Layer Normalization: Pre-norm vs post-norm configurations
• Stochastic Depth: Random layer dropping during training
• Gradient Checkpointing: Memory-efficient training

3. Comprehensive Experiment Tracking System

Configuration Management

@dataclass
class ViTConfig:
    # Architecture parameters
    image_size: int = 224
    patch_size: int = 16
    num_layers: int = 12
    hidden_dim: int = 768
    num_heads: int = 12
    mlp_ratio: float = 4.0
    
    # Regularization parameters
    dropout_rate: float = 0.1
    attention_dropout: float = 0.1
    stochastic_depth_rate: float = 0.1
    
    # Training parameters
    learning_rate: float = 1e-3
    weight_decay: float = 0.05
    batch_size: int = 64
    
    # Optimization parameters
    optimizer: str = "adamw"
    scheduler: str = "cosine"
    warmup_epochs: int = 5

Core Implementation Requirements

Phase 1: Custom ViT Implementation

• Patch Embedding Module: Convert images to patch tokens
• Multi-Head Attention: Custom self-attention implementation
• Transformer Block: Encoder block with layer norm and MLP
• Classification Head: Final classification layer with dropout
• Model Assembly: Complete ViT architecture integration
• Parameter Initialization: Xavier/He initialization strategies

Phase 2: Training Infrastructure (Week 2-3)

• Custom Training Loop: Mixed precision, gradient accumulation
• Data Pipeline: Efficient data loading with augmentations
• Loss Functions: Cross-entropy, label smoothing, focal loss
• Optimization: AdamW, SGD, learning rate scheduling
• Regularization: Dropout, weight decay, stochastic depth
• Checkpointing: Model saving and resuming capabilities

Phase 3: Experiment Framework (Week 3-4)

• Hyperparameter Sweeps: Automated configuration testing
• Metric Tracking: Accuracy, F1, precision, recall, AUC
• Visualization: Training curves, attention maps, confusion matrices
• Robustness Evaluation: Performance on corrupted test sets
• Comparison Framework: Benchmarking against pre-trained models
• Statistical Analysis: Significance testing, confidence intervals

Phase 4: Advanced Features (Week 4-5)

• Architecture Variants: Different ViT configurations
• Knowledge Distillation: Teacher-student training
• Transfer Learning: Fine-tuning from different pre-trained models
• Attention Analysis: Visualization and interpretation
• Model Compression: Pruning and quantization techniques
• Deployment Optimization: ONNX export and inference optimization

Required Python Tech Stack

import plotly.graph_objects as go from torchvision.utils import make_grid import cv2 # For image processing

## Detailed Deliverables

### 1. Code Structure (Must Be Modular)

custom_vit/ ├── README.md # Comprehensive project documentation ├── ARCHITECTURE.md # Technical architecture details
├── SETUP.md # Installation and setup guide ├── requirements.txt # Python dependencies ├── configs/ # Configuration files │ ├── base_config.yaml │ ├── small_vit.yaml │ ├── large_vit.yaml │ └── experiment_configs/ ├── src/ │ ├── models/ # Custom ViT implementations │ │ ├── vit.py │ │ ├── attention.py │ │ ├── embeddings.py │ │ └── layers.py │ ├── data/ # Data loading and preprocessing │ │ ├── dataset.py │ │ ├── transforms.py │ │ └── utils.py │ ├── training/ # Training infrastructure │ │ ├── trainer.py │ │ ├── losses.py │ │ ├── optimizers.py │ │ └── schedulers.py │ ├── evaluation/ # Evaluation and metrics │ │ ├── metrics.py │ │ ├── robustness.py │ │ └── visualization.py │ ├── experiments/ # Experiment runners │ │ ├── hyperparameter_sweep.py │ │ ├── ablation_study.py │ │ └── comparison_study.py │ └── utils/ # Utility functions │ ├── logging.py │ ├── checkpointing.py │ └── config.py ├── notebooks/ # Analysis and visualization │ ├── data_exploration.ipynb │ ├── model_analysis.ipynb │ ├── attention_visualization.ipynb │ └── results_analysis.ipynb ├── experiments/ # Experiment results and configs ├── checkpoints/ # Model checkpoints ├── logs/ # Training logs └── docs/ # Additional documentation ├── model_architecture.md ├── experiment_results.md └── performance_analysis.md

### 2. Documentation Requirements

#### README.md (Must Include)
- Project overview and technical objectives
- Quick start guide (< 5 minutes to run first experiment)
- Environment setup and GPU requirements
- Dataset download and preparation instructions
- Example commands for training and evaluation
- Results summary with performance comparisons
- Architecture overview with diagrams
- Hyperparameter configuration guide

#### ARCHITECTURE.md (Must Include)
- Custom ViT implementation details
- Mathematical formulations for attention mechanisms
- Design decisions and architectural choices
- Comparison with standard ViT implementations
- Performance optimization techniques
- Memory and computational complexity analysis
- Extension possibilities and future work

#### SETUP.md (Must Include)
- Step-by-step installation for different environments
- CUDA and PyTorch setup instructions
- Dataset preparation and verification
- Configuration file setup
- Troubleshooting common installation issues
- Development environment setup
- Production deployment considerations

### 3. Visual Documentation (Required)

#### Model Architecture Diagram
- ViT architecture with detailed layer information
- Attention mechanism visualization
- Data flow through the network
- Parameter sharing and connections
- Tools: Draw.io, TikZ, or programmatic visualization

#### Experiment Results Dashboard
- Training and validation curves
- Hyperparameter sensitivity analysis
- Robustness evaluation across corruption types
- Attention map visualizations
- Confusion matrices and classification reports

#### Performance Comparison Charts
- Accuracy vs model size trade-offs
- Training time vs performance analysis
- Custom ViT vs pre-trained model comparison
- Robustness performance across different corruptions

## Test Scenarios & Success Criteria

### Primary Experiments

**Experiment 1: Baseline Custom ViT**
- Train custom ViT-Base equivalent from scratch
- Compare against timm/transformers pre-trained models
- Target: >85% accuracy on clean test set

**Experiment 2: Architecture Ablation Study**
- Test different patch sizes (8, 16, 32)
- Vary number of layers (6, 12, 24)
- Compare attention head configurations (4, 8, 12, 16)
- Analyze dropout and regularization effects

**Experiment 3: Robustness Evaluation**
- Evaluate on all 7 corruption types
- Compare robustness vs accuracy trade-offs
- Implement and test data augmentation strategies

**Experiment 4: Optimization Study**
- Compare optimizers (SGD, Adam, AdamW)
- Test learning rate schedules (cosine, linear, exponential)
- Analyze batch size effects on performance


## Evaluation Criteria

### Technical Implementation
- **Custom ViT Quality**: Clean, efficient implementation from scratch
- **Training Infrastructure**: Robust training loop with proper error handling
- **Configuration System**: Flexible hyperparameter management
- **Code Organization**: Modular, well-documented, and maintainable code

### Experimental Rigor 
- **Comprehensive Evaluation**: Multiple metrics, statistical significance
- **Ablation Studies**: Systematic analysis of architectural components
- **Hyperparameter Analysis**: Thorough exploration of parameter space
- **Robustness Testing**: Evaluation under adversarial conditions

### Performance & Innovation
- **Model Performance**: Competitive accuracy on standard benchmarks
- **Training Efficiency**: Optimized training pipeline and convergence
- **Novel Insights**: Original findings about ViT behavior and optimization
- **Comparison Quality**: Fair and comprehensive baseline comparisons

### Documentation & Reproducibility
- **Code Documentation**: Clear docstrings, comments, and type hints
- **Experiment Documentation**: Detailed methodology and results
- **Reproducibility**: Easy setup and consistent results
- **Visual Presentation**: Clear plots, diagrams, and result summaries

## Expected Outcomes & Deliverables

### Model Checkpoints
- Custom ViT models trained with different configurations
- Pre-trained baseline models for comparison
- Compressed/optimized models for deployment
- Attention map visualizations and analysis

### Experiment Reports
- Comprehensive performance analysis across all test conditions
- Hyperparameter sensitivity analysis with statistical significance
- Robustness evaluation with detailed corruption analysis  
- Comparison study with pre-trained models and architectural variants

### Technical Contributions
- Custom ViT implementation with detailed mathematical documentation
- Training infrastructure that can be extended to other vision tasks
- Comprehensive evaluation framework for fine-grained classification
- Insights into ViT behavior on automotive image classification

### Success Metrics
- **Accuracy**: >85% top-1 accuracy on Stanford Cars test set
- **Robustness**: <10% accuracy drop under moderate corruptions
- **Efficiency**: Competitive training time vs pre-trained alternatives
- **Reproducibility**: All experiments reproducible with provided configurations