Industrial Equipment Defect Classifier

This project implements a convolutional neural network (CNN) to classify images of industrial equipment as either defective or non-defective. The project includes code for training, evaluating, and testing the model, along with instructions for using the model with uploaded images.

Features

• Binary classification of industrial equipment images.
• Pre-trained CNN model for high accuracy.
• Evaluation with accuracy, precision, recall, and a confusion matrix.
• Test individual images for defect classification.

Dataset

dataset/
├── defective/
│   ├── image1.jpg
│   ├── image2.jpg
│   └── ...
└── non-defective/
    ├── image1.jpg
    ├── image2.jpg
    └── ...

Model Architecture

The model is built using TensorFlow/Keras and consists of:

• Convolutional Layers: Extract features from images.
• Max Pooling Layers: Reduce dimensionality and computation.
• Fully Connected Layers: Make binary predictions.

Contributing

Feel free to submit issues or pull requests. Contributions are welcome!