A Convolutional Neural Network implemented entirely from scratch in x86-64 assembly using AVX-512, performing cat vs dog image classification without any ML frameworks or libraries.

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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KNN algorithm from scratch for cat vs dog image classification using Python. Machine learning, distance-based classification, and computer vision experiment.

Cat vs dog image classification using cnn

Image classification using CNN Keras

A beginner-friendly deep learning project that uses a CNN to classify images of cats and dogs, demonstrating image preprocessing and model training in Python.

The model classifies a image whether it is a dog or a cat

This project is an image classification project based on a transfer learning approach using with MobileNetV2 architecture.

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.

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This project implements a deep learning–based image classification system to accurately distinguish between cats and dogs Using transfer learning with a pre-trained ResNet50 model

A deep learning CNN project that teaches a model to distinguish cats from dogs.

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Cat vs Dog Image Classification using VGG16 A simple deep learning project using TensorFlow and Keras to classify images of cats and dogs. Uses VGG16 pretrained model with image augmentation, training on local GPU, and supports prediction on new images.

Dog-vs-Cat-image-classification

Pytorch CNN image classification

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This project implements a Support Vector Machine (SVM) model to classify images of cats and dogs. The model is trained on a dataset of images and can predict whether a given image contains a cat or a dog.

Cat Vs Dog Image Classification Project | Deep Learning | CNN

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).

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Cat vs Dog image classification supported by MLFlow, LakeFS, Docker & AWS

基于 PyTorch 的深度学习图像分类实战项目。主要使用了 ResNet-18 预训练模型进行迁移学习，涵盖了二分类（Dogs vs. Cats）和多分类（CIFAR-10）任务，并包含超参数实验、错误样本分析以及类别不平衡（Imbalanced Dataset）处理的解决方案。

Deep dive into ConvNets with a popular Kaggle Cat vs Dog dataset, and techniques that can improve ConvNet performance, particularly when doing image classification

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

Cat vs Dog Image Classification using Transfer Learning with Explainable AI (Grad-CAM). Built using TensorFlow, Keras, and MobileNetV2, including confidence scores and visual heatmap interpretations.

A 6 week project which is to create a model for the dog vs cat image classification with a time period set to 6 week and having different task to be completed in each week.

To start your Deep Learning Journey with Python, Cats vs Dog classification is the best fundamental Deep Learning project. In this project, we will create a model with the help of Python keras which will classify whether the image is of dog or cat. About Cats and Dogs Prediction Project In this project, we will be using a Convolutional Neural Network to create our model which will predict whether the image is of dog or cat. We will create a GUI in which we will directly upload the image and predict whether the image contains a dog or cat. Now, let’s directly dive into the implementation of this project: Dataset we will be using: In this we will be using the famous dataset that is the Asirra( animal species image recognition for restricting access) dataset, which was introduced in 2013 for a machine learning competition. The dataset consist of 25,000 images with equal numbers of cats and dogs. You can download the Dogs and Cats dataset using the following link: Dogs and Cats Dataset Project Prerequisites: You should firstly install all the required libraries in your system with the help of pip installer. The name of libraries you should install are: Numpy Pandas Matplotlib Tensorflow Keras sklearn You can install this using pip by opening your cmd and type (For ex: pip install numpy, pip install tensorflow, pip install pandas, etc).