Cat and dog image classification github. Cats" dataset from Kaggle.

Cat and dog image classification github. 01 CNN Cat-Dog Image Classification ( Dataset Creation ).
The app integrates a TensorFlow Lite (TFLite) machine learning model to accurately predict and distinguish between images of cats and dogs. Nov 15, 2023 · Cat-vs-Dog-Image-Classification. Steps to build Cats vs DESCRIPTION Build a CNN model that classifies the given pet images correctly into dog and cat images. 0 and Keras to create a convolutional neural network that correctly classifies images of cats and dogs with at least 63% accuracy. 85% accuracy in classifying between Cats and Dogs. Jul 30, 2022 · Pytorch implementation for Dogs vs. Train the ResNet-50 model achieving an accuracy of 94% on the test set. Now let's take a look at a few pictures to get a better sense of what the cat and dog datasets look like. The project explores the application of SVMs in handling high-dimensional image data, emphasizing feature extraction, model training, evaluation, and parameter optimization. The project scope document specifies the requirements for the project “Pet Classification Model Using CNN. In 2014 Kaggle ran a competition to determine if images contained a dog or a cat. Inspiration from various tutorials and articles on image classification with Keras and Flask. The project has applications in animal recognition and surveillance systems. Cats" dataset from Kaggle. I have developed a CNN Model to classify the images of cats and dogs. Contribute to sharmas1ddharth/Cat_and_Dog_Image_Classification development by creating an account on GitHub. If you want to add a new training image to previously category datasets, you add a image to about category directory and if you have npy files in Data folder delete npy_train_data folder. Microsoft-Cats-and-Dogs-Image-Classification Fine Tune ResNet50 + Random Erasing Augmentation reach 99% Accuracy Random Erasing is a data augmentation method for training the convolutional neural network. The dataset used is a popular benchmark in image classification tasks, and the goal is to build a model that can accurately differentiate between the two classes. It analyzes input images of cats and images of dogs to make predictions. The algorithm is trained on a dataset comprising 25,000 images of cats and dogs and is designed to predict the labels for the test set. dogs: Programming Language: This project extends the Cat-Dog Image Classification project by incorporating pre-trained convolutional neural network (CNN) models. keras. It is an image classification model to distinguish between images of cats and dogs using data science techniques in Python. The input for this task is images of dogs or cats from training dataset, while the output is the classi If you want to add new dataset to datasets, you create a directory and rename what you want to add category (like 'cat' or 'phone'). The model is trained on the Dogs vs. Objective This project uses CNNs and TensorFlow to build an AI model for precise cat vs. dog image classification. The meticulous dataset curation, advanced methodologies, and rigorous evaluation process collectively ensure the delivery of a robust and reliable classifier. This will identify images of cats and dogs, given the network is trained with appropriate datasets. April 12, 2020 - pytorch machine learning. The Dogs vs. 01 CNN Cat-Dog Image Classification ( Dataset Creation ). Cats v/s dogs is the "hello-world" of image classification. This project is an improvement on a previous project in which we built and trained a custom deep CNN from the ground up. This README file will guide you through the setup, usage, and structure of the project. Simple Image Classification using CNN — Deep Learning in python. Overview: This repository features the implementation of a Support Vector Machine (SVM) model for classifying images of cats and dogs from the Kaggle dataset. We use the Cats and Dogs dataset, which provides a balanced number of images for both classes, making it ideal for our classification model. Step-1: Clone or download this repository. Cats Redux: Kernels Edition, Kaggle competition. It is a Cat and Dog Image Classification Model which is made using CNNs. Although the problem sounds simple, it was only effectively addressed in the last few years using deep learning convolutional neural networks. Cats Vs Dogs Classification With Matlab. Classifies Cat and Dog images with 94 % accuracy. - AzizBenAli/Cat-Dog-classification The trained model is tested with custom images to classify them as either dogs or cats. Neural networks can be trained by using batches of images, each of them having a label to identify the real nature of the image (cat or dog) This is a Flask app that can classify images of dog and cat. By leveraging the power of CNNs, this project provides an accurate and robust model capable of classifying cat and dog images with high precision. This application provides a user-friendly interface for image classification, making use of advanced machine learning techniques to deliver reliable predictions. Cat and Dog Image Classifier This project implements an image classification model to distinguish between images of cats and dogs using data science techniques in Python. 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. This CNN model is trained on few thousand images of cats and dogs, and later be able to predict if the given image is of a cat or a dog. Model Architecture The CNN model follows the VGG architecture, known for its simplicity and effectiveness in image classification tasks. The input for this task is images of dogs or cats from training dataset, while the output is the classification accuracy on test dataset. Compares the accuracy of KNN, HOG/SVM and CNN for classifying an image as cat or dog. Modified from Image Classification with Pytorch. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. This project focuses on building a powerful image classification model to distinguish between cats and dogs using a Convolutional Neural Network (CNN) - vishal815/Cat-vs-Dog-Image-Classification-making-a-prediction TFDS is a collection of datasets ready to use with TensorFlow, Jax, - tensorflow/datasets In this project, we build an algorithm, a deep learning model to classify whether images contain either a dog or a cat. 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. Cats dataset and can predict whether an input image is a cat or a dog. Repository for a deep learning model that classifies images as either cats or dogs using deep learning techniques. -Both the models were trained and tested with a subset of cats and dogs of 4000 images. computer-vision tensorflow numpy sklearn keras pillow kaggle cv2 tensorflowhub matplotlib-pyplot tensorflow2 cat-vs-dog-classification More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. Cats. Move the downloaded model into project folder. Homework of Deep Learning, UCAS course 081203M05009H. This project is an image classification project based on a transfer learning approach using with MobileNetV2 architecture. Cat and Dog image Binary Classification. Topics The model inference time (prediction time) is 2607 ms (2. Cats" dataset. 32 %. This project uses collections of dog and cat images obtained from the Asirra (Animal Species Image Recognition for Restricting Access) dataset, which is available on Dogs vs. This model achieves over 80% accuracy. Cat_Dog: Folder containing the original dataset of images classified as cats and dogs. Cat and dog image classification using CNN and Tensorflow - GitHub - SnehaR1/Cat-dog-Image-Classification: Cat and dog image classification using CNN and Tensorflow Results after training 18,000 images of cats and dogs: number of epochs = 15; training data / validation data split = 80/20; MODEL CONV 3x3 filter layers with batch norm - 32 x 64 x 96 x 96 x 64 Image classification is a fundamental task in computer vision, where the goal is to categorize images into different classes or categories. Cats dataset is a standard computer vision dataset that involves classifying photos as either containing a dog or cat. The project utilizes a dataset of cat and dog images, split into training and test sets. Image classification of the cat-dog dataset from Kaggle using logistic regression. The Cats vs Dogs image classification project uses deep learning models to classify images of cats and dogs. For both cats and dogs, we have 1,000 training images and 500 test images. The model is built using the Fastai library, which provides high-level abstractions for training deep learning models. Resolution: The dimensions of the image in pixels. By following these steps, you should be able to set up, run, and use the Dogs vs Cats Image Classifier project on your local machine. The problem is to classify each breed of animal presented in the dataset. In this model I've taken a sample dataset from kaggle that contains different images of cats and dogs You signed in with another tab or window. This project is an image classification project using a deep-learning based on Convolutional Neural Networks (CNNs) with Keras. Acknowledgments The model is based on the dataset from the Kaggle competition: Dogs vs. I used Flask for local deployment of the model. This is a image classification project based on Cat of Dog Kaggel competition. Label: The class label of the image (Cat or Dog). -Cats-Image-Classification-Using-CNN-Keras Nov 7, 2022 · Image Classification is one of the most interesting and useful applications of Deep neural networks and Convolutional Neural Networks that enables us to automate the task of assembling similar images and arranging data without the supervision of real humans. ipynb: Notebook for creating the dataset specifically for MobileNet. - nitish6121999/CNN You signed in with another tab or window. ” Apart from specifying the functional and nonfunctional requirements for the project, it also serves as an input for project scoping. The amount of data used in this project: Training data: Dog: 500 images; Cat: 500 images; Testing data Dog: 100 images; Cat: 100 images; Sample Images Cat Jul 27, 2024 · You signed in with another tab or window. aims to create a system capable of recognizing cat and dog images. The dataset used is the "Dogs vs. python opencv tensorflow numpy keras cnn convolutional-neural-networks flatten binary-classification max-pooling sigmoid-function relu-activation cat-and-dog-classifier This project is about classifying the images of dogs & cats from kaggle dataset. Aug 13, 2020 · Dog Cat Image Classifier. Contribute to nihaldewangan/Cat-Dog-Image-Classification-using-CNN development by creating an account on GitHub. It leverages Flutter's cross-platform capabilities to run seamlessly on Android, iOS, and Windows. The model is trained on a diverse dataset and achieves high accuracy in distinguishing between these two popular pet categories. Training set consists of 2000 photos (83,33%) and testing set consists of 400 photos (16,66%) This is my first nice machine learning model, This model gave a 97. This dataset is a common benchmark in the field of computer vision and helps in understanding model behavior on real-world data. The objective of this project is to classify images of dogs and cats using Convolutional Neural Networks (CNN). I don't know yet if different image sets or models may effect the result. The dataset used for training and testing the model is the Kaggle Dogs vs Cats dataset, consisting of 25,000 images in total, with 12,500 images of dogs and 12,500 images of cats. Contribute to SnookyDru/Cat-and-Dog-Classification development by creating an account on GitHub. The dataset used is the Kaggle Dogs vs. Cat and Dog Image Classifier : Develop an image classification model to distinguish between images of cats and dogs using data science techniques in Python. - jpriyankaa/Dogs-vs. ipynb: Jupyter notebook containing the complete code for data preprocessing, model building, training, evaluation, and prediction. The objective of this project is to classify images as either cats or dogs using a Convolutional Neural Network (CNN). The dataset used to train and test the model consists of 25,000 labeled images from the training zip folder provided via Kaggle's repository for the "Dogs vs. This repository contains code for an image classification algorithm that distinguishes between images of cats and dogs. Classification API included This Cat and Dog Image Classifier project for Bharat Intern stands as a testament to the dedication and expertise applied in leveraging state-of-the-art techniques for image classification. Overfitting was addressed using "BatchNormalization" and "Dropout," enhancing generalization. Our basic task is to create an algorithm to classify whether an image contains a dog or a cat. After assembling a labeled dataset comprising diverse cat and dog images, I meticulously preprocessed the data, ensuring uniformity in image sizes and pixel values normalization. The underlying model is a CNN trained using Keras framework - mvmanh/dog-cat-classification This project utilizes a CNN model to classify cat and dog images through training and testing processes. -Dogs-Image-Classification-with-Convolutional-Neural-Network This repository contains a Python script for building a Convolutional Neural Network (CNN) using TensorFlow and Keras to classify images of cats and dogs. Cat_Dog_resized: Folder containing the resized images of cats and dogs, standardized to 224x224 pixels. - GitHub - ash1shraj/Bharat-Intern--Cat-and-Dog-Image-Classifier: Introducing "Cats & Dogs Image Classifier" on GitHub! 🐱🐶 Built with Python and TensorFlow Cats and Dogs Classification with CNN & Image Augmentation | Improve Accuracy - laxmimerit/Deep-Learning-Tutorial-4-cats-vs-dogs-classification cat_dog_classification. Jan 22, 2020 · Image classification for dogs and cats with VGG-16 using PyTorch. ; 01 MobileNet CNN Cat-Dog Image Classification ( Dataset Creation ) Transfer Learning. This project is geared towards understanding the importance of using the State-of-the-art image classification models on your own small image classification problem - AdeboyeML/Cat-and-Dog-image-classification Jan 7, 2024 · Cat and Dog Image Classification Business Goal. Step-2: Download the model from here. -These models were created as a part of a research paper on "Comparative Analysis of SVM and CNN in Image Classification". The accuracy on the test dataset is not going to be good in general for the above-mentioned reason. Contribute to Haditamaaa/image_classification development by creating an account on GitHub. Note: You are currently reading this using Google Colaboratory which is a cloud-hosted version of Jupyter Notebook. The model's performance is evaluated and visualized using accuracy and loss plots. Cats and Dogs Image Classification using Pytorch. -Image classification of Cats and Dogs using SVM and CNN models. You signed in with another tab or window. It involves data preprocessing, normalization, and CNN architecture with pooling layers to learn patterns from images. Task Definition Our basic task is to create an algorithm to classify whether an image contains a dog or a cat. Additionally, a function to predict and display the classification of a single image is included. ##Conclusion This project showcases various techniques in deep learning, including image augmentation, dropout regularization, and transfer learning, to classify images of dogs and cats. About. The model is built using Keras with TensorFlow as the backend. This repository presents a solution to the image classification problem using a two-step process: This project serves as a foundation for a Flutter application designed to classify images of cats and dogs. Cat & Dog Classification using Convolutional Neural Network in Python Sep 7, 2019 · 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 repository includes scripts for data preprocessing, model training, evaluation, and prediction. Deep Learning Project for Beginners – Cats and Dogs Classification. This is easy for humans, dogs, and cats. Introducing "Cats & Dogs Image Classifier" on GitHub! 🐱🐶 Built with Python and TensorFlow, this project uses deep learning to classify images of cats and dogs with high accuracy. Problem Statement A tag already exists with the provided branch name. The three phases of the problem are: preprocessing, training and testing. Image classification for dogs and cats with VGG-16 using PyTorch. keras is the neural network library which work on top of the Tensorflow or we can say tf as the backend for this library. 6%. Python script for performing image classification of dogs and cats using the VGG16 pre-trained model with data augmentation. The script uses the Keras library to create a CNN model that is trained on a dataset of images of cats and dogs, and then evaluated on a separate test set. Performing Transfer Learning on 200 Images: 100 dog images, 100 cat images. - Slimcent/Cat-and-dog-image-classification Repository for a deep learning model that classifies images as either cats or dogs using deep learning techniques. Contribute to octavianwr/cat-dog-images-classification development by creating an account on GitHub. The test set had two folders, one containing 1000 images of dogs and the other containing 1000 images of cats Classification of images between two class cat and dog using CNN with image augmentation . Image classification for dogs and cats with VGG-16 using Apr 12, 2020 · Cats vs Dogs - Part 3 - 99. File Type: The file format of the image. Overview. In addition, we also built a Flask application so user can upload their images and classify easily. Note: the demo model has only two classes - dog and cat - thus it will try "predict" whatever it sees to either dogs or cats. Use data augmentation for improved performance, achieving an accuracy of 83% on the test set. While detecting an object is trivial for humans, robust image classification is still a challenge in computer vision applications. Classification API included You can load your custom image in img_cat/img_dog variable to test the model with random images. This project demonstrates the use of Convolutional Neural Networks (CNNs) to classify images of dogs and cats. - abhaybd/Cat-Dog-CNN-Classifier Develop a Python-based CNN model using PyTorch to classify 1000 labeled images of cats and dogs. I used TensorFlow 2. - donisingh7/Dogs-and-cat-recognition-project Aug 8, 2016 · Cat-and-Dog-Image-Classification. image import ImageDataGenerator. Cats dataset. Dataset; We have access to a dataset consisting of images of cats and dogs. The model processes the image, extracting relevant features, and then employs its trained knowledge to predict whether the input image contains a cat or a dog. Contribute to radiadus/Cat-and-Dog-Image-Classification development by creating an account on GitHub. Both models use the same dataset, which consists of labeled images of dogs and cats. The aim is to leverage the transfer learning technique to improve the classification accuracy of images containing cats and dogs. The Cat vs. Develop an image classification model to distinguish between images of cats and dogs using data science techniques in Python. Developed an image classification model to distinguish between images of cats and dogs using machine learning and neural networks in Python. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. CNN Architecture: The project employs a deep CNN model built with TensorFlow, capable of learning intricate image features. This dataset include 12500 dogs and 12500 cats images, we use 10000 for training and 2500 for testing our model from each of them. Jul 12, 2024 · Image Classification: Cats vs. It involves preprocessing the data, training deep learning models using convolutional neural networks, and evaluating their accuracy. The problem Statements. - Abir0606/Cats-vs. It involves gathering labeled data, training a convolutional neural network (CNN), evaluating its accuracy The Dogs vs. 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 fully The second version cat-dog-classifier-v2 is created for answer in Kaggle competition, which requires answer to be probability of image, if the value is near 0, it's mean the input image is likely to be a cat, otherwise, it's likely to be a dog. 56% on Validation Data which is pretty good. Project Insructions. We'll build an image classifier using tf. Classification API included - anhphan2705/Cat-Dog-Classification-VGG To utilize the classifier, users can input an image containing either a cat or a dog. Cat and Dog Classification using CNN Welcome to the Cat and Dog Classification project using Convolutional Neural Networks (CNN). Cats | Kaggle. The dataset is labeled, meaning each image is assigned a class label indicating whether it contains a cat or a dog. 6 secs) per image, which is not very fast, with mostly good results. This process amalgamates advanced image processing techniques with machine learning methodologies to achieve accurate classifications. - GitHub - gadhane/Binary-Classification-using-keras-and-Deep-Learning-: This will identify images of cats and dogs, given the network is trained with appropriate datasets. It is a simple task for humans, but requires training for a machine to achieve. Apr 6, 2021 · Dog and Cat Image Classification. Here i am using the keras to do classification. Dogs SVM Project Overview: This repository features the implementation of a Support Vector Machine (SVM) model for classifying images of cats and dogs from the Kaggle dataset. We successfully built a deep neural network model by implementing Convolutional Neural Network (CNN) to classify dog and cat images with very high accuracy 97. A better model Add a description, image, and links to the cats-and-dogs-classification topic page so that developers can more easily learn about it. - ReiCHU31/Cat-Dog-Classification-Flask-App Project-Cat-vs-Dog-Image-Classification Project Report: Cat vs Dog Image Classification Introduction. Cats is a classic problem for anyone who wants to dive Cat and Dog Image Classification Using CNN with Tensorflow 2. ; Step-3: In command prompt,go to this project directory. The dataset includes 25,000 images with equal numbers of labels for cats and dogs. The script loads a pre-trained VGG16 model without the top classification layer, adds custom layers for binary classification, compiles the model, and trains it using a dataset of dog and cat images. This user-friendly approach allows for straightforward integration into various applications. The model is created using the Keras library on the TensorFlow backend. Mar 10, 2012 · Cats and dogs images classifier using Python CNN ResNet50. 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. The objective of the Cat and Dog Classification project is to develop a Deep learning model that accurately classifies images of cats and dogs into their respective categories. Dataset: Cats and Dogs dataset. preprocessing. For this challenge, I had to use TensorFlow 2. Explore a robust CNN model trained on a diverse dataset to accurately distinguish between cat and dog images with high precision. Binary Image Classification using CNN w/ residual layers (Dogs & Cats) (Tensorflow, TFLearn, OpenCV) - sebsquire/Dogs-and-cats-image-classification-CNN Cat-Dog Classification using Pytorch This is a common computer vision project to classifier images whether it is cat or dog. Image Classification using CNN. Cats And Dogs Image Classification we are building this model with neural networks. - 07Sushant/Animal-Classifier-App Convolutional Neural Network (CNN) is an algorithm taking an image as input then assigning weights and biases to all the aspects of an image and thus differentiates one from the other. . Custom Dataset: We have curated a custom dataset of labeled dog and cat images for model training and evaluation. 0 and Keras in this project to create a convolutional neural network that correctly classifies images of cats and dogs with at least 63% accuracy. My basic task is to create an algorithm to classify whether an image contains a dog or a cat. The code performs binary classification of images of cats and dogs using a convolutional neural network in TensorFlow and Keras, by training on a dataset of labeled images and testing on a separate set of images, and then using the model to make predictions on individual images. We are developing image classification model to distinguish between images of cats and dogs. Conclusion A CNN is the best approach to this dataset with a 91% accuracy. Notes: Any additional notes or comments about the image. This GitHub repository presents a deep learning solution for cat vs dog image classification utilizing Convolutional Neural Networks (CNNs). The Architecture and parameter used in this network are capable of producing accuracy of 97. Computers find it a bit more difficult. This Jupyter notebook contains a script that uses convolutional neural networks (CNNs) to classify images of cats and dogs. Dependencies: PyTorch / Torchvision Oct 16, 2020 · Image Classification with Cat and Dog. Reload to refresh your session. Sequential model and load data using This has been a Kaggle tradition since 2013 - a classifier that can confidently distinguish between cat and dog! The evaluation metric is the log loss function that takes into account how confident the prediction is. I made it using a pre-trained base model MobileNet V2 , and after that i added a global average pooling and then a dense layer for categorization between two classes ( cats and dogs) , i used only one dense neuron in last layer e… Cats vs Dogs image classification fastai v1. In this post, we will implement the Image classification (especially on Cat and dog dataset in kaggle) with Convolutional Neural Network You signed in with another tab or window. You signed out in another tab or window. A Machine learning project on Cat v/s Dog image classification using CNN, VGG-16 and VGG-19 in Python - smsraj2001/CAT-DOG-IMAGE-CLASSIFICATION This project utilizes a convolutional neural network (CNN) to classify images of dogs and cats. The specific tools used in the project might depend on the programming language and chosen libraries, but here's a general breakdown of the typical tools involved in an SVM image classification project for cats vs. Image classification for dogs and cats with VGG-16 using About. - GitHub - ajiwatode/cat_dog-image-classification-using-CNN: aims to create a system capable of recognizing cat and dog images. The Asirra (animal species image recognition for restricting access) dataset was introduced in 2013 for a machine learning competition. First, configure the matplot parameters: Filename: The name of the image file. Contribute to sourish-ml/SVM_for_Cat_and_Dog_Image_Classification development by creating an account on GitHub. The training set consisted of two folders, one with 4000 images of dogs and the other with 4000 images of cats. About This repository contains a convolutional neural network (CNN) model trained on 20,172,545 trainable parameters designed to distinguish between images of cats and dogs. Cats-Vs-Dogs-Image-Classification. In this tutorial, we will discuss how to classify images into pictures of cats or pictures of dogs. The first step was to classify breeds between dogs and cats, after doing this the breeds of dogs and cats were classified separatelythe, and finally, mixed the races and made the classification, increasing the degree of difficulty of problem. The dataset comprised 1000 training images and 100 test images. The cat and dog image classification project entails building a machine learning model to differentiate between images of cats and dogs. You switched accounts on another tab or window. - Pari2806/Cat-Dog-Image-Classification Jul 27, 2021 · Image classification for dogs and cats with VGG-16 using PyTorch. 6 - ReJackTion/cat-dog-image-classification The Dogs vs. Nov 21, 2023 · Image Classification CAT and Dog. Partitioning Data: 60% Training, 20% Cross Validation, 20% Testing Note - Data will be partiotioned 80/20 to begin, and 80% will be used by the Classification Learner App. It is possible to Achieve more accuracy on this dataset using deeper network and fine tuning of network parameters for training. - yuhexiong/cat-and-dog-classification-CNN-ResNet50-python Convolutional Neural Network to classify images as either cat or dog, along with using attention heatmaps for localization. GitHub Gist: instantly share code, notes, and snippets. 1% Accuracy - Binary Image Classification with PyTorch and an Ensemble of ResNet Models. We are using image augmentation to increase the amount of training data using augmentation by using from keras. Dog Image Classification project is a deep learning project that aims to distinguish between images of cats and dogs using Convolutional Neural Networks (CNNs). - ajna101/CAT_VS_DOG_CLASSIFIER The Oxford-IIIT Pet Dataset. 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 task is a classic example of binary image classification, where the model must learn to distinguish between two classes: cats and dogs. The task at hand revolves around designing an algorithmic to determine the content of images, specifically discerning if the image features a dog, a cat. This repository contains my solution for the freeCodeCAmp challenge 'Cat and Dog Image Classifier'. ⚫ Dataset consists of 2400 photos of cats and dogs in csv format. Model accuracy: 99. For example, an image classification algorithm may be designed to tell if an image contains a human figure or not. Curate this topic Add this topic to your repo Classifying whether an image is a cat or dog using transfer learning and convolutional neural network. This project demonstrates how to build and train a CNN to classify images of cats and dogs. Written in python with keras. ipynb: Notebook for creating the dataset. oyufap octij fpzq kkge suvwl lmubpt scfbms bleyrg fgf pwggzx