This repository contains the implementation of a fault detection system that detects and eliminates faulty products based on shape and color using a Convolutional Neural Network (CNN). The project is part of the GRIP (Graduate Rotational Internship Project) tasks for an IoT and Computer Vision Internship at The Sparks Foundation.

The dataset used for this project is the Caltech 101 dataset, which contains images of objects belonging to 101 categories. The images were collected from various sources and have varying backgrounds, orientations, and scales.

• Python 3.6 or later
• TensorFlow 2.x
• Keras
• NumPy
• OpenCV
• scikit-learn

1. Clone the repository:

        git clone https://github.com/nadinejackson1/fault-detection-system.git
   
        cd fault-detection-system
   

2. Run the Jupyter Notebook or Python script containing the code for training and evaluating the CNN model on the Caltech 101 dataset.

This project is licensed under the MIT License. See the LICENSE file for details.