Mirfaisal72/CNN-Image_Classification

This repository contains a CNN Image classification model. It performs CNN on a dataset of dogs and cats using deep learning techniques. Achieved accurate categorization through convolutional layers, pooling, and fully connected layers.

CNN Image Classification: Dog vs Cat

This repository contains a Convolutional Neural Network (CNN) implementation for image classification of dogs and cats using the TensorFlow and Keras libraries. The CNN model is trained to differentiate between images of dogs and cats.

Table of Contents

Introduction Dataset Training the Model Evaluation Making Predictions

Introduction

Convolutional Neural Networks (CNNs) are a class of deep learning models particularly well-suited for image classification tasks. In this project, we use a CNN to classify images of dogs and cats.

Dataset

The dataset for this project consists of images of dogs and cats. The dataset is divided into training and testing sets. Each image is represented as a matrix of pixel values in the Red, Green, Blue (RGB) color channels.

Training the Model

The CNN model architecture for image classification is defined using the Keras Sequential API. The architecture includes Convolutional and MaxPooling layers followed by fully connected Dense layers. The model is compiled with an Adam optimizer and binary cross-entropy loss function.

Evaluation

After training, the model is evaluated on the testing dataset. The test loss and accuracy are calculated using the trained model.

Making Predictions

You can use the trained model to make predictions on new images. A random test image is selected, and its predicted class label (dog or cat) is displayed.