Cat and dog image classification github. Dog and cat image classifier with deep learning.

Cat and dog image classification github The Total number of images available for training is 25,000 and final testing is done on seperate 10,000 images. Use data augmentation for improved performance, achieving an accuracy of 83% on the test set. Cats Redux: Kernels Edition dataset. - Binary-Image-Classification/Python Code The Dogs vs. - GitHub - KavishaLP/cat-dog_prediction: A deep learning project for binary classification of images into two categories: Cats and Dogs. Dec 26, 2024 · This project implements a binary image classifier to distinguish between dogs and cats, with additional testing on breed classification. 25000 structured images of Dog and Cat for ML. Train Jul 27, 2021 · Image classification for dogs and cats with VGG-16 using PyTorch. The objective of this project is to develop a deep learning model using the VGG-16 architecture to classify images of cats and dogs with high accuracy. About Implemented a Transfer Learning model to classify images of cats and dogs. It contains images of different cat and dog breeds, organized into respective directories for training and testing. Cats competition, this notebook demonstrates the process of loading image data, preprocessing it, training a CNN, and evaluating model performance. Contribute to laxmimerit/dog-cat-full-dataset development by creating an account on GitHub. Dogs Image Classification project leverages Transfer Learning with the VGG16 model to classify images of cats and dogs. The model is trained on the Kaggle Dogs vs Cats dataset, utilizing data augmentation and CNN layers. This project aims to develop a deep learning model capable of correctly classifying images of dogs and cats using TensorFlow. - naishafarhat/Cat-Dog Jul 30, 2022 · Pytorch implementation for Dogs vs. 6%. I used Image Data Generator to augment the images through flipping horizontally or Develop a Python-based CNN model using PyTorch to classify 1000 labeled images of cats and dogs. - Nahum-Ab/Cat-vs-Dog-Image-Classification This repository contains a Python script for image classification using a pre-trained VGG-16 model and an SVM (Support Vector Machine) classifier. In this Section we are implementing Convolution Neural Network (CNN) Classifier for Classifying dog and cat images. In this project, we use a Support Vector Machine (SVM) to classify images of cats and dogs. Although the problem sounds simple, it was only effectively addressed in the last few years using deep learning convolutional neural networks. The ultimate goal is to create a model that can accurately distinguish between images of cats and dogs in real-world scenarios. For this challenge, I had to use TensorFlow 2. While the output is the accuracy, the main objective of this project is not to get a high accuracy but rather to learn how to use convolution neural network (CNN) for classification using Pytorch. Modified from Image Classification with Pytorch. The approach ensures efficiency by using pretrained convolutional layers while training custom fully connected layers. Curate this topic This project implements image classification using different techniques to classify images of cats and dogs. Cats Redux: Kernels Edition, Kaggle competition. The repository includes all steps from dataset loading and model training to evaluation, deployment, an Apr 6, 2021 · Dog and Cat Image Classification. 0 and Keras were used to create a convolutional neural network that correctly classifies images of cats and dogs with at least 63% accuracy. The Dog vs Cat Classifier project is designed to accurately distinguish between images of dogs and cats using Convolutional Neural Networks (CNNs). The model is trained on a diverse dataset and achieves high accuracy in distinguishing between these two popular pet categories. The goal is to compare these approaches and evaluate their performance on a standard dataset. Build A deep learning project for binary classification of images into two categories: Cats and Dogs. It includes steps for downloading the dataset, training, evaluating, and making predictions. The script uses PyTorch for data handling and feature extraction, and scikit-learn for training and evaluating the SVM classifier. It leverages the power of the VGG16 architecture and Transfer Learning techniques to achieve highly accurate classification results. The pre-trained weights from the ImageNet dataset, which includes This project is an image classification task where a Convolutional Neural Network (CNN) is trained to classify images of cats and dogs. The model is trained on the Animal Image Classification Dataset from Kaggle, leveraging pre-trained weights from ImageNet. Dog Image Classification This project builds, trains, and evaluates two Convolutional Neural Network (CNN) models for binary image classification using the popular "Cats and Dogs" dataset from Kaggle. fcc_cat_dog_project freeCodeCamp Cat and Dog Image Classifier Project TensorFlow 2. 0 and Keras to create a convolutional neural network that correctly classifies images of cats and dogs at least 63% of the time. Classification API included The dataset used in this project is the Animal Breed - Cats and Dogs dataset from Kaggle. As part of freeCodeCamp's Machine Learning with Python curriculum, I had to work on the project "Cat And Dog Image Classifier" to create a model that classifies mixed images of cats and dogs as either cats or dogs. Feel free to customize the script, explore different model architectures, or adapt it for other image classification tasks. Oct 8, 2025 · This makes them highly effective for tasks like image classification, object detection and segmentation. Cat vs. The system utilizes a CNN architecture for robust feature extraction and classification. Improve this page Add a description, image, and links to the cats-and-dogs-classification topic page so that developers can more easily learn about it. This project demonstrates image classification using a Convolutional Neural Network (CNN) implemented with TensorFlow. CNN for Cat vs. The Asirra (animal species image recognition for restricting access) dataset was introduced in 2013 for a machine learning competition. This project focuses on image classification using a Convolutional Neural Network (CNN) to distinguish between images of cats and dogs. A refined classifier designed to distinguish between cats and dogs, built with PyTorch and fine-tuned on the ResNet-50 architecture. It utilizes image processing techniques and a CNN architecture to achieve high accuracy in distinguishing between the two classes. Cats-Vs-Dogs-Image-Classification The problem Statements Given a set of labeled images of cats and dogs, a machine learning model is to be learnt and later it is to be used to classify a set of new images as cats or dogs. This project builds an image classification model to distinguish between dogs, cats, and snakes using ResNet50 with transfer learning in Keras. Inspired by the classic Kaggle Dogs vs. Aug 13, 2020 · For this challenge, you will complete the code below to classify images of dogs and cats. Explore different architectures such as VGG16, VGG19, ResNet50, InceptionV3, DenseNet121, and MobileNetV2 fine-tuned for accurate classification. (Extra credit if you get it to 70% accuracy!) Some of the code is given to you but some code you must fill in to complete this challenge. GitHub Gist: instantly share code, notes, and snippets. Includes pre-processing scripts, model architecture, and evaluation metrics for seamless implementation Sep 7, 2019 · Dogs vs Cats - Binary Image Classification 7 minute read Dogs v/s Cats - Binary Image Classification using ConvNets (CNNs) This is a hobby project I took on to jump into the world of deep neural networks. The implementation includes data preprocessing with augmentation, model training with callbacks, fine-tuning, evaluation with Cat and Dog Classification with YOLOv8 This project implements a real-time pet classification model using YOLOv8 to distinguish between images of cats and dogs. Understand the dataset and batches. The primary objective is to classify images as either "dog" or "cat" based on a custom dataset created for this purpose. Read the This project aims to classify the images in the given dataset as cats or dogs using convolutional neural networks (CNN) This repository contains my solution for the freeCodeCAmp challenge 'Cat and Dog Image Classifier'. In this post, the algorithm that we will use to solve this problem is with Convolutional Neural Networks (CNN), using the VGG16 arquitecture with ImageNet as a pre-train model with Transfer Learning. This repository contains code for classifying images of dogs and cats using a CNN model built with TensorFlow and Keras. The project utilizes a Kaggle dataset consisting of thousands of labeled images of dogs and cats, making it an ideal choice for building and training deep learning Dec 16, 2024 · Image classification is a fundamental application of deep learning in computer vision. This repository contains implementation and evaluation scripts for various pre-trained deep learning models applied to binary classification of cats and dogs using transfer learning on a balanced dataset. This project implements a Support Vector Machine (SVM) model to classify images of cats and dogs. The objective is to accurately classify images into either the 'cat' or 'dog' category. We will follow these steps: Explore the example data Build a small convnet from scratch to solve our classification problem This project is a simple and interactive web application built using Streamlit that classifies uploaded images as either Cat or Dog using a pre-trained deep learning model. Cats dataset is a standard computer vision dataset that involves classifying photos as either containing a dog or cat. The cat and dog classifier I developed is a powerful computer vision model that utilizes the combined capabilities of OpenCV and TensorFlow for accurate image classification. The Dogs vs. Dog Image Classification Exercise 1: Building a Convnet from Scratch Estimated completion time: 20 minutes In this exercise, we will build a classifier model from scratch that is able to distinguish dogs from cats. Run the main script (cats_vs_dogs_classification. The goal is to compare a deeper, more complex CNN architecture against a lighter one and analyze their performance. The model will be trained on a dataset of labeled images and evaluated. Sep 8, 2017 · As such we will build a CNN model to distinguish images of cats from those of dogs by using the Dogs vs. The project explores traditional feature extraction methods like Histogram of Oriented Gradients (HOG) and deep learning techniques like the VGG16 convolutional neural network. The trained model is capable of classifying images into two categories: dogs and cats. The model is trained on the Oxford Pets dataset and can reliably identify the two classes, achieving robust performance across various image conditions. The project demonstrates various techniques such as image augmentation, Deep Learning Assignment — This project involves building a Cat vs Dog classifier using CNNs. For environments without Apple Silicon, the model seamlessly supports inference on CUDA-enabled devices, ensuring Contribute to 77RRR/Cat-and-Dog-Image-Classifier development by creating an account on GitHub. The accuracy on the test dataset is not going to be good in general for the above-mentioned reason. Homework of Deep Learning, UCAS This repository contains Python scripts for training and evaluating an image classification model based on the VGG-16 architecture using PyTorch. Apr 16, 2025 · Dataset: Dog and Cat Classification Dataset – Kaggle This dataset contains 12,000+ images of cats and 12,000+ images of dogs. This project focuses on binary image classification, distinguishing between images of dogs and cats. Includes pre-processing scripts, model architecture, and evaluation metrics for seamless implementation - Cat-Vs-Dog-Image-Classification/CatVsDogs This Cats vs. Oct 16, 2020 · Convolutional Neural Network - Cat-Dog Classifier Previously, We built Artificial Neural Network for Fashion MNIST classifier. py) to train the model and see the training progress. The model is trained on labeled images and predicts whether a given image contains a cat or a dog. You will use Tensorflow 2. We will follow these steps: Explore the example data Build a small convnet from scratch to solve our classification problem Jan 19, 2025 · This repository demonstrates a complete workflow for classifying cat and dog images using machine learning. Challenge was achieved by creating a model which is correctly identified 72. Sep 1, 2025 · This project implements a deep learning solution for binary image classification, distinguishing between cats and dogs using a custom Convolutional Neural Network architecture. The model is built using TensorFlow and Keras and aims to predict whether an image contains a cat or a dog. It works in image classification, but to do that, it requires numerous of parameters. In this project, I built a Convolutional Neural Network (CNN) model to classify images of dogs and cats with Image classification is a crucial task in computer vision. . About This project is an image classification project using a deep-learning based on Convolutional Neural Networks (CNNs) with Keras. The dataset consists of labeled images of cats and dogs, which are preprocessed and fed into the model to train it to make accurate predictions. This project focuses on building an image classification model that automatically distinguishes between cats and dogs using deep learning techniques. Jan 22, 2020 · Task 3 of the Prodigy InfoTech ML internship which involves Implementing a support vector machine (SVM) to classify images of cats and dogs. The model achieves 85%+ validation accuracy through strategic data augmentation and optimized network design. PyTorch Image Classification Classifies an image as containing either a dog or a cat (using Kaggle's public dataset), but could easily be extended to other image classification problems. 0% of the images of cats and dogs. Contribute to ardamavi/Dog-Cat-Classifier development by creating an account on GitHub. Kaggle training dataset. Image classification Dog-and-Cat-Project-for-CNN-from-Kaggle-dataset This repository contains a comprehensive project for classifying images of dogs and cats using Convolutional Neural Networks (CNNs). The dataset includes 25,000 images with equal numbers of labels for cats and dogs. Pre-trained deep CNNs typically generalize easily to different but similar datasets with the help of transfer learning. It identifies whether the animal in the image is a cat or a dog, and provide accurate predictions. A image classifier for classifying images of cats and dogs Data set used: Cat & Dog Use pre-trained ResNet50 with FC layer trained with the data set Model details presented as below: About Competition As part of Kaggle competitions, this one refers to a classification between cats and dogs according to a set of images. Additionally, there is an API script that implements the trained model and allows users to classify multiple images as either dogs or cats. 0 and Keras to create a convolutional neural network that correctly classifies images of cats and dogs with at least 63% accuracy. Implementing Cat and Dog Classification using CNN Feb 1, 2025 · Cat vs Dog Classification using CNN This project implements a Convolutional Neural Network (CNN) to classify images of cats and dogs. Use the provided code snippet in the README to predict the class of a new image using the trained model. It contains several Dense (or Fully Connected) Layer which node has its weight. I used Keras with TensorFlow backend to build my custom convolutional neural network, with 3 subgroups of convolution, pooling and activation layers before flattening and adding a couple of This project focuses on classifying images of cats and dogs using Convolutional Neural Networks (CNNs). This repository contains an ipython notebook which implements Convolutional Neural Networks to do a binary image classification. The dataset used for training and testing consists of two classes: "train" and "test," imported from Kaggle. It demonstrates a complete end to end machine learning workflow — from data preprocessing and visualization to model building, training, evaluation, and deployment. The model is trained on a large dataset of dog and cat images to learn and differentiate between the two classes. Through deep learning techniques, the model has been trained on a vast dataset of cat and dog images, enabling it to About This project is a deep learning application designed to classify images of cats and dogs using a Convolutional Neural Network (CNN). Cat-Dog Classification using Pytorch This is a common computer vision project to classifier images whether it is cat or dog. The project uses deep learning techniques to build and train a CNN model for image classification. In this article we will build a CNN-based classifier to distinguish between images of cats and dogs. Transfer Learning for Binary Classification - Cats vs Dogs This project focuses on binary classification of two classes: cats and dogs, using various state-of-the-art deep learning models. The model is trained on a dataset of images and can predict whether a given image contains a cat or a dog. It covers data preprocessing, training models (SVM, Random Forest, Logistic Regression, CNN, and K-means), and building a Flask-based web application with an interactive frontend for image upload and classification. Cats is a classic problem for anyone who wants to dive deeper into deep-learning. Optimized for inference using Apple's Metal Performance Shaders (MPS), it efficiently leverages macOS hardware acceleration for superior performance. This project showcases the application of deep learning techniques in image classification, covering data preprocessing, model building, training, evaluation, and deployment. Dog and cat image classifier with deep learning. Model accuracy: 99. The dataset used in this project consists of labeled images of cats and dogs. Repository for a deep learning model that classifies images as either cats or dogs using deep learning techniques. qumyn ltxreqz yykwebja kzgwe hipir zqvi tmmxg qmuq yekmk ausvi outjw dwzw jcyb wvat bnju