Image Classification with PyTorch

This repository contains the code for an image classification model implemented in PyTorch. The model is trained and evaluated on a custom dataset. The dataset consists of 5000 images across 10 classes, with images split into training, validation, and test sets.

Dataset

The dataset is structured as follows:

• Number of images: 5000
• Number of classes: 10
• Resolution of each image: 64 x 64

Splits

• Train: 3000 images
• Validation: 500 images
• Test: 1500 images

Folder Structure

After extracting the dataset, the folder structure should be:

CSV Files

Each CSV file contains the following columns:

• image_name: Name of the image
• image_path: Relative path to the image
• class_id: ID of the class the image belongs to (not available in test.csv)
• class_name: Name of the class the image belongs to (not available in test.csv)

Requirements

• Python 3.x
• PyTorch
• torchvision
• pandas
• numpy
• matplotlib
• tqdm

You can install the required libraries using pip:

pip install torch torchvision pandas numpy matplotlib tqdm