The training set consisted of 25,000 images out of which 5,000 images were taken out as validation data. Separate test data folder consisted of 12,500 images for which the labels were predicted using trained model. My work includes preprocessing for model, Data augmentation to prevent overfitting, callbacks in keras to reduce learning rate timely, various CNN architecture trials with different layers and hyperparameters for best fit and learning curve wrt epochs. I gained a validation accuracy of 87.15 % without using any pretrained imagenet models . VGG-16 gave around 89 % as validation accuracy.

This project is an image classification project using a deep-learning based on Convolutional Neural Networks (CNNs) with Keras. The Dogs vs. Cats is a classic problem for anyone who wants to dive deeper into deep-learning.

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Cats vs Dogs Classification (with 98.7% Accuracy) using CNN Keras

CNN material using cats vs dogs kaggle data and keras framework

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This Project is based on Neural Network to classify between Dogs and Cats.

Binary image classification of cats vs dogs using CNNs built with Keras/TensorFlow, including training visualization.

This repository showcases a a simple Convolutional Neural Network (CNN) using TensorFlow Keras to perform binary image classification (example: cats vs dogs)

This project is a Cats vs Dogs image classifier built using a Convolutional Neural Network (CNN) in TensorFlow/Keras. The model is trained on the Kaggle Dogs vs Cats dataset, which contains thousands of labeled images of cats and dogs. Images are preprocessed by resizing, normalizing, and applying data augmentation .

This project implements a CNN using TensorFlow and Keras to classify dog and cat images from the Kaggle Dogs vs. Cats dataset, with preprocessing, training, evaluation, and prediction steps.

This project implements a Convolutional Neural Network (CNN) using TensorFlow and Keras to classify images of cats and dogs. The model is trained on the Cats vs Dogs dataset from TensorFlow Datasets (TFDS) and achieves 82% test accuracy.

Cat vs Dog Classification using CNN. This project implements a CNN to classify images of cats and dogs. It was developed as a learning exercise to explore deep learning, image classification, and model evaluation techniques. Features Dataset: www.kaggle.com/c/dogs-vs-cats/data Framework: TensorFlow and Keras Accuracy: 90.63%

This repository contains a Python script for building a Convolutional Neural Network (CNN) using TensorFlow and Keras to classify images of cats and dogs. The model is trained on the Dogs vs. Cats dataset and can predict whether an input image is a cat or a dog.

This project is a simple Convolutional Neural Network (CNN) image classifier built using TensorFlow and Keras. The model classifies images into categories such as cats vs dogs, but it can be adapted to any other dataset.

Dogs vs Cats classifier using CNN (dependency: keras)

This project focuses on classifying cats and dogs from the given dataset using CNN and keras.

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https://www.kaggle.com/c/dogs-vs-cats/

This project demonstrates the use of Convolutional Neural Networks (CNNs) to classify images of dogs and cats. The dataset used is the "Dogs vs. Cats" dataset from Kaggle. The model is built using Keras with TensorFlow as the backend.

The Aim of the Project is to build a Convolutional Neural Network which classifies the Image that contains a dog or a cat, deploy the trained model using Graphical User Interface

Cats vs Dogs Classification (with 98.7% Accuracy) using CNN Keras

A CNN model using tensorflow-keras to predict Cats vs Dogs

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Cats vs Dogs image classifier using CNN (TensorFlow, Keras, Google Colab, GPU)

Classification of Dogs vs Cats dataset using CNN - Keras with TensorFlow backend

“Multi-model CNN image classification for cats vs. dogs using Keras and TensorFlow.”