updated readme file

README.md

@@ -88,7 +88,7 @@ class ViTConfig:
 - [ ] **Model Assembly**: Complete ViT architecture integration
 - [ ] **Parameter Initialization**: Xavier/He initialization strategies
 
-### Phase 2: Training Infrastructure (Week 2-3)
+### Phase 2: Training Infrastructure
 - [ ] **Custom Training Loop**: Mixed precision, gradient accumulation
 - [ ] **Data Pipeline**: Efficient data loading with augmentations
 - [ ] **Loss Functions**: Cross-entropy, label smoothing, focal loss
@@ -96,7 +96,7 @@ class ViTConfig:
 - [ ] **Regularization**: Dropout, weight decay, stochastic depth
 - [ ] **Checkpointing**: Model saving and resuming capabilities
 
-### Phase 3: Experiment Framework (Week 3-4)
+### Phase 3: Experiment Framework
 - [ ] **Hyperparameter Sweeps**: Automated configuration testing
 - [ ] **Metric Tracking**: Accuracy, F1, precision, recall, AUC
 - [ ] **Visualization**: Training curves, attention maps, confusion matrices
@@ -104,7 +104,7 @@ class ViTConfig:
 - [ ] **Comparison Framework**: Benchmarking against pre-trained models
 - [ ] **Statistical Analysis**: Significance testing, confidence intervals
 
-### Phase 4: Advanced Features (Week 4-5)
+### Phase 4: Advanced Features
 - [ ] **Architecture Variants**: Different ViT configurations
 - [ ] **Knowledge Distillation**: Teacher-student training
 - [ ] **Transfer Learning**: Fine-tuning from different pre-trained models
@@ -114,6 +114,7 @@ class ViTConfig:
 
 ## Required Python Tech Stack
 
+```python
 import plotly.graph_objects as go
 from torchvision.utils import make_grid
 import cv2  # For image processing
@@ -125,57 +126,6 @@ import cv2  # For image processing
 
 ### 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