🔍 Transistor Anomaly Detection App

An AI-powered web app for automated quality inspection of transistor components and circuit boards using computer vision.

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🚀 Project Overview

The Transistor Anomaly Detection App is built to help engineers and quality inspectors quickly detect defects or irregularities in transistor circuits. Leveraging deep learning models trained with Google Teachable Machine, this app simplifies manual inspection with a fast, scalable, and accurate AI solution.

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🧠 Model Building

• Built using Google Teachable Machine
• Trained for binary classification:
  • ✅ Good (Normal)
  • ⚠️ Anomaly (Defective/Damaged)
• Exported as .h5 Keras model and integrated into a Streamlit app

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📂 Dataset Source

• Dataset created by collecting transistor and circuit board images from Mvtec Datasets.
• Classes:
  • Good Transistors
  • Defective Transistors (burn marks, bent pins, cracks)
• Used for training and validation via Teachable Machine

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💡 App Features

• 📁 Upload or 📷 capture circuit images
• 🧠 On-device AI model prediction
• 🔍 Predicts image status as Good or Anomaly
• ✅ Real-time, user-friendly interface built with Streamlit

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🛠️ Technologies Used

• Python
• Streamlit
• TensorFlow / Keras
• Google Teachable Machine
• Computer Vision & Deep Learning
• Git & GitHub

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✅ How to Run Locally

1. Clone the repo:

   git clone https://github.com/darshan1654/Transistor-Anomaly-Detection_App.git
   cd Transistor-Anomaly-Detection_App
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the App:

   streamlit run App.py
   

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📌 Conclusion

This app showcases how deep learning and computer vision can automate visual inspection in electronics manufacturing. It serves as a foundation for deploying real-world quality control tools with AI.

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🙌 Acknowledgements

• Google Teachable Machine
• Mvtec Datasets
• Streamlit Community
• Intel AI