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.

Dog and cat image classifier with deep learning

A simple binary classifier to predict if the given image is a cat or a dog.

Classifying cat and dog images using Kaggle dataset

This classifier use Convolution Neural Network approch for kaggle problem to classify Cat vs Dog images.

CNN-based image classification system leveraging TensorFlow and Keras to accurately distinguish between dog and cat images.

End To End Deep Learning Project For Classifying Cat vs Dog Images, using PyTorch

A classifier for cat and dog images (Response to Siraj's challenge of the week)

Image Classifier with Flask and Keras CNN

Cat Dog Image Classifier

Convolutional Neural Network to classify images as either cat or dog, along with using attention heatmaps for localization. Written in python with keras.

This is a Flask app that can classify images of dog and cat. The underlying model is a CNN trained using Keras framework

Computer Vision Case Study in image recognition to classify an image to a binary class, based on Convolutional Neural Networks (CNN), with TensorFlow and Keras in Python, to identify from an image whether it is an image of a dog or cat. (Includes: Data, Case Study Paper, Code)

Using Teachable Machine Learning to generate the dog vs cat classifier and integrate in Flutter

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An example program for TensorflowSharp - classify an image as a dog or cat using a fine-tuned mobilenet model. GPU tuning parameters are added via a byte-array. (Possibly outdated! Updated 2018)

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. 🚀

Cat Dog Classifier

This Repository contains cat dog image classifier model implemented Using Convolutional Neural Network.

Training a deep learning model to recognize real images of cats and dogs in order to classify an incoming image as one or the other.

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Flask Blueprint & RESTful application with various image classification models.

The famous Cats-vs-Dogs dataset. I have used a self laid ConvNet to classify the image into 2 classes either a Dog or a Cat. The images used are of 100*100 pixels each. The images are first converted to the numpy array of pixel values using the python ZipFile module. The images are then divided into the training ,cross-validation,testing set containing 20000 , 5000 , 12500 images respectively. Also I have used data augmentation technique to avoid chances of overfitting the model. Finally I achieved a decent accuracy of about 88 % on the validation set.

Using pytorch and transfer learning to achieve a cat and dog classifier.

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This is a Flutter Application which will classify whether it is a dog or a cat according to the image you provide from the gallery or from the camera.

Using TensorFlow, a deep learning model is trained to distinguish between cat and dog faces. The model analyzes image features to accurately classify whether the face belongs to a cat or a dog, demonstrating a practical application of machine learning for image recognition.

Cat and dog face find in picture with haar cascade classifier

We successfully built a deep neural network model by implementing Convolutional Neural Network (CNN) to classify dog and cat images with very high accuracy 97.32 %. In addition, we also built a Flask application so user can upload their images and classify easily.

This is a flask web app which classify whether images contain either a dog or cat.