Cats vs Dogs Image Classification using Logistic Regression

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.

Pytorch implementation for Dogs vs. Cats Redux: Kernels Edition, Kaggle competition. Modified from Image Classification with Pytorch. Homework of Deep Learning, UCAS course 081203M05009H. Kaggle猫狗分类比赛的Pytorch实现, 修改自Image Classification with Pytorch, 中国科学院大学深度学习作业.

Dogs-vs-Cats image classification

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.

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

The Image Classification Project compares Google Cloud AutoML vs. Custom Training (TensorFlow) using a cats vs. dogs dataset, analyzing accuracy, training time, and computational efficiency for real-world applications. 🐱🐶🚀

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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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.

I will implement the binary-class image classification using the VGG-16 Deep Convolutional Network as a Transfer Learning framework where the VGGNet comes pre-trained on the ImageNet dataset. For the experiment, we will use the Kaggle dogs-vs-cats dataset and classify the image objects into 2 classes. The classification accuracies of the VGG-16 model will be visualized using the confusion matrices.

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).

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。

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

This project tackles the classic "Dogs vs. Cats" image classification problem using a machine learning approach. It leverages Histogram of Oriented Gradients (HOG) for feature extraction and a Linear Support Vector Machine (SVM) for classification, achieving a notable accuracy in distinguishing between images of dogs and cats.

Image classification from scratch, starting from JPEG image files on disk, without leveraging pre-trained weights or a pre-made Keras Application model to demonstrate the workflow on the Kaggle Cats vs Dogs binary classification dataset

one vs one is implemented for multiclass classification on the datasets of images containing cats,dogs,horses,humans. SVM Classifiers using Pegasos algorithm is used and nC2 Classifiers are Trained

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

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).

Cat-Dog Classifier using CNN

Deep Neural Network to distinguish the images of dogs from cats

CatsVsDogs_ImageClassification

CNN Image classification ( Dogs vs Cats )

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Cats vs Dogs : Image Classification using CNN

Cats-vs-Dogs-Image-Classification-with-ML

This is a project I am going to fiddle with to enhance my understanding of ML.

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Image classification of Dogs vs. Cats using Transfer Learning from Keras InceptionNetV3

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