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.

This project implements a Dog vs Cat classifier using deep learning techniques. It utilizes convolutional neural networks (CNNs) to accurately classify images of dogs and cats. Built with Python and TensorFlow, this model can be trained on a dataset of labeled images to distinguish between the two animals, enabling efficient image classification.

My attempt on learning how the Convolutional Neural Network Works (CNN) on a simple Image Classification problem, Predicting whether the given image is of a cat (0) or a dog(1).

Cats vs Dogs image classification deep learning CNN model built using Python

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

CNN Image classification ( Dogs vs Cats )

Cats vs Dogs : Image Classification using CNN

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.

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This is an Image Classification project to detect and classify Cats or Dogs in a given image.

Dogs vs Cats Image Classification using VGG16 CNN - 4Geeks Bootcamp

Classification d’images (chats vs chiens) avec CNN sous TensorFlow/Keras : traitement d’images, entraînement d’un modèle convolutif, régularisation, évaluation (matrice de confusion, précision) et prédiction sur de nouvelles images.

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

Image classification project using SVM on 'Cats vs Dogs' dataset. Trained with CNN and augmented data for accurate classification of cat and dog images.

Image classification on the Cats vs Dogs dataset using CNNs from scratch and transfer learning (LeNet, AlexNet, VGGNet, ResNet, MobileNet).

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

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%

Dog vs Cat Image Classification using Transfer Learning (Deep Learning project). This project uses a pre-trained CNN model to classify images of dogs and cats with high accuracy.

CNN-based image classification project using the Dogs vs. Cats Kaggle dataset. Explores different CNN architectures and preprocessing pipelines to optimize model accuracy for binary image classification. Final submission includes score screenshot and modeling notebook.

A Convolutional Neural Network (CNN) model for classifying images of cats and dogs. This project uses deep learning techniques to predict whether an image contains a cat or a dog, trained on the popular Dogs vs Cats dataset. The model demonstrates the power of CNNs for image classification tasks and achieves high accuracy on test data.

Cat vs Dog Classification is a DL project that uses a Convolutional Neural Network (CNN) to classify images of cats and dogs. This project involves training a CNN model on labeled image data, enabling accurate prediction of whether an image contains a cat or a dog. Ideal for learning & applying image classification techniques using neural network.

Dogs vs. Cats Classification is a project focused on the image classification task of distinguishing between dog and cat images using Convolutional Neural Networks (CNNs). This repository implements advanced deep learning techniques to preprocess data, train models, and evaluate performance on this classic image recognition problem

This repository includes two image classification experiments built with CNNs: - Cats vs Dogs (binary classification) - MNIST Digits (10-class classification: 0 to 9) The project provides a graphical user interface (`interface.py`) to run preprocessing, training (Baseline/Alternative and MinPooling variants), model loading, and image prediction.

This repository contains the implementation and experiments for Assignment 1 in image classification using convolutional neural networks (CNNs) and transfer learning. The goal is to evaluate the effectiveness of transfer learning by comparing training strategies using the Cats vs Dogs dataset and the Stanford Dogs dataset.

Cats vs. Dogs – CNN Image Classification

CNN Image Classification (Cats vs Dogs)

Dogs vs Cats Image Classification -- CNN

Cat vs Dog image classifier built with TensorFlow/Keras, including dataset cleanup and CNN training pipeline.

Multi image classification (CNN) Cats vs Dogs

Cats vs Dogs Image Classification using CNN