Upload README.md

README.md

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+# EL Defect Detection — Training Package
+
+Train a **U-Net++ with EfficientNet-B4 encoder** for solar cell EL defect segmentation.
+
+## Quick Start (RTX 4060 / any CUDA GPU)
+
+```bash
+# 1. Clone this repo
+git clone https://huggingface.co/nithishbasireddy/el-defect-training
+cd el-defect-training
+
+# 2. Install dependencies
+pip install -r requirements.txt
+
+# 3. Verify setup
+python test_setup.py
+
+# 4. Train (auto-downloads E-SCDD dataset)
+python train.py
+```
+
+Training takes ~2-3 hours on RTX 4060. Model saves to `output/best_model.pth`.
+
+## After Training
+
+```bash
+# Run the Streamlit app with your trained model
+streamlit run app.py
+# → Set model path to "output/best_model.pth" in sidebar
+```
+
+## Architecture
+
+- **Model**: U-Net++ + EfficientNet-B4 + scSE attention (20.9M params)
+- **Dataset**: E-SCDD (903 images, 512×512, 30→5 class remap)
+- **Loss**: 0.5 × Dice + 0.5 × Focal (γ=2.0) — handles severe class imbalance
+- **Optimizer**: AdamW with differential LR (encoder 1e-4, decoder 5e-4)
+- **AMP**: Mixed precision for 8GB VRAM GPUs
+
+## Classes (5)
+
+| ID | Name | E-SCDD Labels | Color |
+|----|------|---------------|-------|
+| 0 | background | 0-8, 21-24, 29 | Black |
+| 1 | busbar | 9 | Green |
+| 2 | crack | 10, 14 | Blue |
+| 3 | dark/inactive | 11, 17, 20 | Red |
+| 4 | other_defect | 12,13,15,16,18,19,25-28 | Yellow |
+
+## Why U-Net++ over U-Net?
+
+U-Net++ has **dense skip connections** between encoder and decoder. For thin structures like cracks (often <10px wide), these nested connections preserve fine details that plain U-Net's single skip connections lose. Validated in medical imaging (thin vessels) and industrial inspection.