CNN Cat vs Dog Classification

Overview

Developed a convolutional neural network using PyTorch for binary classification of cat and dog images. Achieved high accuracy through data augmentation, transfer learning, and hyperparameter tuning.

A deep learning project that implements a Convolutional Neural Network (CNN) using PyTorch for binary image classification between cats and dogs. The project utilizes advanced techniques including data augmentation, transfer learning, and comprehensive hyperparameter tuning to achieve high classification accuracy. The model demonstrates proficiency in computer vision and deep learning.

Key Features

• CNN architecture design
• Data augmentation techniques
• Transfer learning implementation
• Hyperparameter optimization
• Model training and evaluation
• Image preprocessing pipeline

Technologies Used