Dog vs Cat Classifier is a deep learning model built with TensorFlow to classify images as either dogs or cats. It utilizes a Convolutional Neural Network (CNN) and is trained on the Dog vs Cat dataset from Kaggle. The repository includes training and prediction scripts, allowing users to train the model and classify new images easily. 🚀

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.

Deep learning project for classifying cat vs. dog images, built from scratch as part of my preparation for MSc AI. Includes multiple CNN architectures, training workflows, and final evaluation.

A comprehensive Cat vs Dog Classifier using TensorFlow and Keras, featuring CNN models, transfer learning, data augmentation, and visualizations for robust binary image classification.

🐶🐱 Dog vs Cat Image Classifier using Deep Learning A deep learning project that classifies images as either a dog or a cat using a Convolutional Neural Network (CNN) built with TensorFlow/Keras. The model is trained on a labeled dataset of images and includes support for predictions via a command line script or a simple Flask web app. 🔧 Feature

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

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Deep Learning project that classifies images of cats and dogs using a Convolutional Neural Network (CNN) built with TensorFlow/Keras. Includes data augmentation, regularization, and early stopping for improved performance.

Deep learning project that classifies images of cats and dogs using Convolutional Neural Networks (CNN). Includes image augmentation, model training, and evaluation using TensorFlow

Classify an image into dog vs. cat category using CNN

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CNN Image Classifier Python project using TensorFlow/Keras to classify cat vs. dog images. Features GUI, TensorBoard, and model persistence.

Cat vs Dog CNN – Classifies cat and dog images using a CNN in TensorFlow. Achieved 97.5% test accuracy with train/test split, model training, and prediction.

A CNN-based Deep Learning web app that classifies cat vs dog images in real time using TensorFlow and Streamlit.

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.

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.

End-to-end deep learning pipeline to classify cat vs. dog images using CNN, ResNet, and ResNet50V2 with advanced tuning, evaluation, and deployment-ready predictions.

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.

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.

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.

This project implements a Convolutional Neural Network (CNN) to classify images of cats and dogs. CNNs are a class of deep learning models specifically designed for analyzing visual data. Using the popular Cats vs. Dogs dataset, the model is trained to identify whether an input image contains a cat or a dog with high accuracy.

Classifier for Cat & Dog Images,Cat-vs-Dog-Classification-DL-CNN-

CNN-based Image Classifier for Cat vs Dog

CNN to classify cat vs dog images

CNN-based Cat vs Dog image classifier using TensorFlow

Cat-vs-Dog-Image-Classifier | CNN & Transfer Learning Project

Cat vs Dog Image Classifier using CNN (TensorFlow/Keras)

CNN-based Cat vs Dog Image Classifier using TensorFlow

"CNN Model for classifying Cat vs Dog images"