nithishbasireddy/el-defect-training

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)

# 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

# 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.