An implementation of Federated Learning using Pytorch and PySyft
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Updated
Sep 1, 2021 - Jupyter Notebook
An implementation of Federated Learning using Pytorch and PySyft
Cat and Dog Classification with Convolutional Neural Networks (CNNs)
A deep learning model to classify between dogs and cats using transfer learning with RESNET50
In this repository, you will find a deep learning python project to classify 3 classes of images
This repository contains an ipython notebook which implements Convolutional Neural Networks to do a binary image classification.
Train a Deep CNN using images acquired automatically from google search with Selenium
a CNN classifier to classify among the images of cats and dogs
A flask based web application to differentiate between cats and dogs
This is my first nice machine learning model, This model gave a 97.85% accuracy in classifying between Cats and Dogs. 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…
AIMS-Ghana Assignment on writing an algorithm to classify whether images contain either a dog or a cat.
Cats vs Dogs Classification using CNN
Image Classification problem, Cats v/s Dogs Model. Browse to https://imgclassification.herokuapp.com/ for the deployment via Heroku
Classify whether an image contains a cat or a dog and what is it's breed
This Repository contains my implementation on Constitutional Neural Networks on Cats and Dogs dataset
Building basic models with different type of data for practice
Desafio Prático de Machine Learning do Instituto CTS.
This repository aims to train a model that is really really good at seeing the difference between a cat and a dog.
This repository contains an ipython notebook which implements a Convolutional Neural Network to do a binary image classification. I used this to classify Cats vs Dogs and you can get the dataset from here https://www.kaggle.com/c/dogs-vs-cats/data . (This model trains with thousands of input images so be patient.)
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