Excited to share latest Deep Learning project: Faulty Solar Panel Detection using CNN + VGG19! 🚀

☀️ Problem: Manual solar panel inspection is slow, costly, and error-prone due to environmental degradation.

💡 Solution: An image classification model detecting 6 fault types via VGG19 Transfer Learning (ImageNet pretrained).

📂 Dataset: 885 images across 6 classes:
• 🐦 Bird-drop
• ✅ Clean
• 🌫 Dusty
• ⚡️ Electrical-damage
• 💥 Physical-Damage
• ❄️ Snow-Covered

🏗 Architecture:
• Base: VGG19 (frozen for feature extraction)
• Head: GlobalAveragePooling2D → Dropout(0.3) → Dense(90)
• Training: Phase 1 (Head only, 46K params) → Phase 2 (Fine-tune top layers, lr=0.0001)

📊 Results (2 epochs):
✅ Val Accuracy: 81.36%
📉 Val Loss: 0.589

🔍 Takeaways:
→ Transfer learning works well on small datasets (~885 images).
→ Fine-tuning significantly boosted performance over feature extraction alone.
→ Model effectively distinguishes subtle differences (e.g., dusty vs. bird-drop).

🛠 Stack: Python | TensorFlow/Keras | VGG19 | OpenCV | Scikit-learn | Seaborn | Matplotlib

https://t.me/CodeProgrammer 🔰