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+# EEL4759 Final Project: Diabetic Retinopathy Classification
+
+Compares three pretrained architectures (ResNet-50, EfficientNet-B0, ViT-B/16) with and without CLAHE preprocessing on 5-class DR severity grading.
+
+## Task
+
+Classify fundus photographs into five DR severity grades:
+
+| Label | Grade |
+|-------|-------|
+| 0 | Healthy |
+| 1 | Mild NPDR |
+| 2 | Moderate NPDR |
+| 3 | Severe NPDR |
+| 4 | Proliferative DR |
+
+Dataset: [Diabetic Retinopathy Resized Arranged](https://www.kaggle.com/datasets/amanneo/diabetic-retinopathy-resized-arranged) (auto-downloaded via `kagglehub`).
+Split: 70% train / 15% val / 15% test, stratified.
+
+## Experiments
+
+Six experiments (one per model/CLAHE combination):
+
+| Model | CLAHE off | CLAHE on |
+|-------|-----------|----------|
+| ResNet-50 | `resnet50_clahe=False` | `resnet50_clahe=True` |
+| EfficientNet-B0 | `efficientnet_b0_clahe=False` | `efficientnet_b0_clahe=True` |
+| ViT-B/16 | `vit_b_16_clahe=False` | `vit_b_16_clahe=True` |
+
+## Design Notes
+
+- CLAHE: applied to the L channel in LAB space before spatial transforms to enhance retinal lesion contrast.
+- Class imbalance: `WeightedRandomSampler` with sqrt-inverse-frequency weights plus Focal Loss (gamma=2).
+- Discriminative LR: backbone trained at `lr x 0.1`, classification head at `lr`.
+- LR schedule: 3-epoch linear warmup followed by cosine annealing.
+- Mixed precision: `torch.amp.autocast` + `GradScaler` (CUDA only).
+- Early stopping: monitored on validation macro F1, patience=10.
+
+## Files
+
+```
+main.py          # entry point, experiment loop
+dataset.py       # CLAHETransform, DRDataset, data loaders
+models.py        # create_model() for resnet50 / efficientnet_b0 / vit_b_16
+train.py         # train_model() with AMP, early stopping
+evaluate.py      # metrics, plots, summary CSV
+utils.py         # set_seed, get_device, FocalLoss, CLASS_NAMES
+requirements.txt # pip dependencies
+manifest.scm     # Guix environment (AMD ROCm)
+```
+
+Each experiment writes to `results/<experiment_key>/`:
+- `best_model.pth`
+- `classification_report.txt`
+- `confusion_matrix.png`
+- `training_curves.png`
+
+When more than one experiment finishes, `results/summary.csv` and `results/comparison_chart.png` are written.
+
+## Usage
+
+```bash
+# Run all 6 experiments (50 epochs, batch 256, 384px)
+python main.py
+
+# Quick sanity check
+python main.py --models resnet50 --clahe 0 --epochs 2
+
+# Custom run
+python main.py --models resnet50 efficientnet_b0 \
+               --clahe 0 1 \
+               --epochs 30 --lr 5e-5 --batch-size 128 \
+               --img-size 224 --num-workers 4
+```
+
+Key arguments:
+
+| Argument | Default | Description |
+|----------|---------|-------------|
+| `--models` | all three | Models to run |
+| `--clahe` | `0 1` | CLAHE variants (0=off, 1=on) |
+| `--epochs` | 50 | Max training epochs |
+| `--patience` | 10 | Early stopping patience |
+| `--batch-size` | 256 | Training batch size |
+| `--lr` | 1e-4 | Learning rate (head); backbone gets 10x lower |
+| `--img-size` | 384 | Input resolution |
+| `--focal-gamma` | 2.0 | Focal loss gamma (0 = standard cross-entropy) |
+| `--data-root` | auto | Dataset root; downloaded if omitted |
+
+## Dependencies
+
+```
+pip install kagglehub torch torchvision scikit-learn opencv-python-headless matplotlib seaborn
+```
+
+For AMD ROCm (Guix): use `manifest.scm`.
+
+## Metrics
+
+Weighted F1, macro F1, accuracy, and quadratic-weighted Cohen's kappa.