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README.md

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+# Cervical Type Classification - Model Training
+
+## Overview
+
+This project classifies cervical images into 3 transformation zone types:
+- **Type 1**: Fully visible squamocolumnar junction (SCJ)
+- **Type 2**: Partially visible SCJ
+- **Type 3**: SCJ not visible (inside cervical canal)
+
+## Best Model Summary
+
+| Metric | Value |
+|--------|-------|
+| **Validation Accuracy** | **65.52%** |
+| **Macro F1** | **65.61%** |
+| Best Epoch | 34 |
+| Total Parameters | 1,327,235 |
+
+---
+
+## Best Model Configuration
+
+**Run Name:** `L32_64_128_256_Res_SE_lr5e-04_d0.3`
+
+### Architecture
+
+| Component | Value |
+|-----------|-------|
+| Conv Layers | [32, 64, 128, 256] |
+| FC Layers | [256, 128] |
+| Kernel Size | 3x3 |
+| Pooling | MaxPool 2x2 |
+| Batch Normalization | Yes |
+| Activation | ReLU |
+| Residual Connections | **Yes** |
+| SE Attention | **Yes** |
+
+### Training Settings
+
+| Parameter | Value |
+|-----------|-------|
+| Learning Rate | 5e-4 |
+| Weight Decay | 1e-4 |
+| Dropout | 0.3 |
+| Batch Size | 32 |
+| Focal Loss Gamma | 2.0 |
+| Label Smoothing | 0.1 |
+| Data Augmentation | Yes |
+
+---
+
+## Performance Metrics
+
+### Per-Class Metrics
+
+| Class | Precision | Recall | F1-Score | Support |
+|-------|-----------|--------|----------|---------|
+| **Type 1** | 79.26% | 61.49% | 69.26% | 348 |
+| **Type 2** | 58.09% | 75.29% | 65.58% | 348 |
+| **Type 3** | 64.40% | 59.77% | 62.00% | 348 |
+| **Macro Avg** | 67.25% | 65.52% | **65.61%** | 1044 |
+
+### Confusion Matrix
+
+```
+                 Predicted
+              Type 1  Type 2  Type 3
+Actual Type 1   214      84      50
+       Type 2    21     262      65
+       Type 3    35     105     208
+```
+
+### Interpretation
+
+| Finding | Implication |
+|---------|-------------|
+| Type 1 has highest precision (79%) | When model predicts Type 1, it's usually correct |
+| Type 2 has highest recall (75%) | Model catches most Type 2 cases |
+| Type 3 has lowest metrics | Hardest to classify - often confused with Type 2 |
+| Type 2 ↔ Type 3 confusion is common | 105 Type 3 misclassified as Type 2 |
+
+---
+
+## Grid Search Results
+
+A grid search of 32 configurations was performed on January 17, 2026.
+
+### Search Space
+
+| Parameter | Values Tested |
+|-----------|---------------|
+| Conv Layers | [32,64,128,256], [64,128,256] |
+| Learning Rate | 5e-4, 1e-4 |
+| Dropout | 0.3, 0.4 |
+| Residual | Yes, No |
+| SE Attention | Yes, No |
+
+### Top 10 Configurations
+
+| Rank | Configuration | Accuracy | Key Features |
+|------|--------------|----------|--------------|
+| 1 | L32_64_128_256_Res_SE_lr5e-04_d0.3 | **65.52%** | 4-layer, Res+SE |
+| 2 | L64_128_256_Res_SE_lr5e-04_d0.3 | 65.04% | 3-layer, Res+SE |
+| 3 | L32_64_128_256_Res_SE_lr1e-04_d0.3 | 64.94% | 4-layer, lower LR |
+| 4 | L64_128_256_Res_SE_lr1e-04_d0.3 | 64.37% | 3-layer, lower LR |
+| 5 | L32_64_128_256_Res_SE_lr5e-04_d0.4 | 64.18% | Higher dropout |
+| 6 | L32_64_128_256_Res_lr5e-04_d0.4 | 64.08% | No SE |
+| 7 | L32_64_128_256_Res_lr1e-04_d0.3 | 63.60% | No SE, lower LR |
+| 8 | L32_64_128_256_Res_SE_lr1e-04_d0.4 | 63.51% | Lower LR, higher dropout |
+| 9 | L64_128_256_Res_SE_lr5e-04_d0.4 | 63.22% | 3-layer, higher dropout |
+| 10 | L64_128_256_Res_SE_lr1e-04_d0.4 | 63.12% | 3-layer, lower LR |
+
+### Key Findings
+
+| Finding | Evidence |
+|---------|----------|
+| **Residual + SE is critical** | Top 10 models all use residual connections; top 4 use both Res+SE |
+| **4-layer network is better** | [32,64,128,256] outperforms [64,128,256] |
+| **Higher LR (5e-4) preferred** | 5e-4 consistently beats 1e-4 |
+| **Lower dropout (0.3) preferred** | 0.3 dropout outperforms 0.4 |
+| **Plain CNN performs worst** | Models without Res or SE are at the bottom |
+
+### What Worked vs What Didn't
+
+| Worked | Didn't Work |
+|--------|-------------|
+| Residual connections | Plain convolutions |
+| SE attention blocks | No attention |
+| 4 conv layers | 3 conv layers |
+| LR = 5e-4 | LR = 1e-4 (too slow) |
+| Dropout = 0.3 | Dropout = 0.4 (too aggressive) |
+| Focal Loss | - |
+| Label smoothing 0.1 | - |
+
+---
+
+## Data
+
+| Split | Samples | Classes | Distribution |
+|-------|---------|---------|--------------|
+| Train | ~7,000 | 3 | Balanced after augmentation |
+| Test | 1,044 | 3 | [348, 348, 348] |
+
+### Image Specifications
+
+- Size: Variable (resized during training)
+- Channels: 3 (RGB)
+- Source: Colposcopy images
+
+---
+
+## Model Files
+
+### Best Model Location
+
+```
+./best_model.pth  (this folder)
+```
+
+Original training output:
+```
+/data/downloads/cervical_type/_output/grid_search_v2_20260117_212011/run_001_L32_64_128_256_Res_SE_lr5e-04_d0.3/
+```
+
+### Checkpoint Contents
+
+```python
+{
+    "epoch": 34,
+    "model_state_dict": ...,
+    "optimizer_state_dict": ...,
+    "scheduler_state_dict": ...,
+    "metrics": {...},
+    "model_config": {...}
+}
+```
+
+### Files in This Folder
+
+| File | Description |
+|------|-------------|
+| `best_model.pth` | Model checkpoint (weights + optimizer state) |
+| `config.json` | Training configuration used |
+| `training_history.json` | Loss/accuracy per epoch |
+| `grid_search_summary.json` | All 32 grid search results |
+| `README.md` | This file |
+
+### Loading the Model
+
+```python
+import torch
+
+# Load checkpoint (from this folder)
+checkpoint = torch.load('best_model.pth', weights_only=False)
+
+# Create model with same config
+model = BaseCNN(
+    conv_layers=[32, 64, 128, 256],
+    fc_layers=[256, 128],
+    num_classes=3,
+    dropout=0.3,
+    use_residual=True,
+    use_se_attention=True
+)
+
+# Load weights
+model.load_state_dict(checkpoint['model_state_dict'])
+model.eval()
+```
+
+---
+
+## Output Structure
+
+```
+_output/
+└── grid_search_v2_20260117_212011/
+    ├── grid_search_config.json    # Search space definition
+    ├── all_results.json           # All 32 run results
+    ├── summary.json               # Sorted results + best run
+    ├── logs/
+    │   └── grid_search.log
+    └── run_001_.../               # Best run
+        ├── checkpoints/
+        │   ├── best_model.pth     # Best validation accuracy
+        │   ├── latest.pth         # Final epoch
+        │   └── epoch_*.pth        # Periodic saves
+        └── logs/
+            ├── run_config.json
+            └── training_history.json
+```
+
+---
+
+## Comparison with v1 Baseline
+
+| Version | Accuracy | Improvement |
+|---------|----------|-------------|
+| v1 Baseline | 61.69% | - |
+| **v2 Best (Res+SE)** | **65.52%** | **+3.83%** |
+
+The addition of residual connections and SE attention improved accuracy by nearly 4%.
+
+---
+
+## Recommendations for Future Work
+
+1. **Try deeper networks** - Add 5th conv layer [32, 64, 128, 256, 512]
+2. **Transfer learning** - Use pretrained EfficientNet or ResNet backbone
+3. **Address Type 3 confusion** - Type 3 is often misclassified as Type 2
+4. **Ensemble methods** - Combine top 3-5 models
+5. **Test Time Augmentation** - Average predictions over augmented versions
+6. **More training data** - Current ~7k samples may be limiting
+
+---
+
+## Quick Start
+
+```bash
+# Run the best configuration
+python train_grid_v2.py
+
+# Or load and evaluate the best model
+python evaluate.py --model /path/to/best_model.pth
+```
+
+---
+
+*Last updated: January 2026*
+*Grid search: 32 configurations, ~15 hours on single GPU*

best_model.pth

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config.json

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+  "dropout": 0.3,
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+  "pool": true,
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+  "nr_classes": 3,
+  "augmentation": true
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grid_search_summary.json

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