Image Classification with PyTorch

This project demonstrates image classification using a Convolutional Neural Network (CNN) implemented with PyTorch. The CNN is trained to classify images of fruits into different classes. It utilizes the torchvision library to preprocess the data and create a dataset for training.

Project Overview

The project includes the following components:

• Data preprocessing and loading using torchvision.datasets.ImageFolder.
• Definition of a CNN model with PyTorch's nn.Module.
• Training the model using a custom training loop.
• Visualization of predicted class labels on test images.

Example Output

Below is an example output showing predicted class labels for test images:

Getting Started

To run the project:

1. Install the required libraries listed in requirements.txt.
2. Organize your dataset in the fruits directory.
3. Run the Python script train_model.py to train the CNN model.
4. Use the trained model to predict class labels using predict_label.py.

Dependencies

• Python 3.x
• PyTorch
• torchvision
• matplotlib

License

This project is licensed under the Apache 2.0 License. See LICENSE for details.