Image classification for dogs and cats with VGG-16 using PyTorch. Model accuracy: 99.6%. Classification API included

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

A CNN to distinguish between cat images and dog images with a fairly high accuracy with less data

a simple CNN to classify a binary class consist of dogs and cats powered by TensorFlow.keras

A deep learning project built with PyTorch and deployed using Streamlit to classify images as either a cat or a dog.

An exploration of the trade-off between image quality and accuracy.

Deep learning model for Cats vs Dogs competition in Kaggle. Also it contains CNN's filters and feature maps visualizations.

Image classifier for cats vs dogs using MobileNetV2 and TensorFlow/Keras

This Repository contain algorithm to classify whether images contain either a dog or a cat. This is easy for humans, dogs, and cats. But computer will find it a bit more difficult.

A convolutional neural network (CNN)-based image classification project developed as part of the FreeCodeCamp Machine Learning with Python certification. The model is trained to distinguish between images of cats and dogs using TensorFlow/Keras.

A simple classifier model that fine-tune the InceptionV3 model