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.

The famous Cats-vs-Dogs dataset. I have used a self laid ConvNet to classify the image into 2 classes either a Dog or a Cat. The images used are of 100*100 pixels each. The images are first converted to the numpy array of pixel values using the python ZipFile module. The images are then divided into the training ,cross-validation,testing set containing 20000 , 5000 , 12500 images respectively. Also I have used data augmentation technique to avoid chances of overfitting the model. Finally I achieved a decent accuracy of about 88 % on the validation set.

A convolutional neural networks predict wether the imported image is cat or dog, using keras library.

No description available

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

No description available

Classic binary image classification using Convolutional Neural Network Model built using Keras with TensorFlow backend.

DL-Dogs-vs-Cat-Classification-with-CNN-Keras-Project

CNN material using cats vs dogs kaggle data and keras framework

This repository contains a Convolutional Neural Network (CNN) built with TensorFlow and Keras for binary image classification. The model is trained and evaluated on the Kaggle "Dogs vs. Cats" dataset, demonstrating a complete computer vision workflow from data preprocessing to prediction

No description available

No description available

Practiced CNN implementation using Kaggle competition of Dogs vs Cats

keras implementation of CNN for cats vs dogs classification with visualization of the conv layers

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

CNN on small Dogs vs Cat dataset of keras official

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.

Cats vs Dogs Keras CNN classifier.

Dogs vs Cats classifier using CNN (dependency: keras)

No description available

Tutorials how to build CNN model with Keras to classify dogs and cats

Image Classifier using Keras