A deep learning project that classifies dog and cat images using a custom CNN built with PyTorch. Includes data preprocessing, training, evaluation, and visualization with modular code for easy experimentation. Achieves 92%+ accuracy with Git LFS support for large datasets.

I used a convolutional network to classify images of dogs and cats

A repository for understanding the model interpretation methods on a simple object localization task.

Dogs vs. Cats classification using CNN in PyTorch.

Create a Vanilla CNN to test the performance of GPUs

Repository ini berisi proyek klasifikasi gambar untuk membedakan antara anjing dan kucing, menggunakan model TensorFlow. Proyek ini dikembangkan sebagai bagian dari kelas pengembangan Machine Learning, mencakup preprocessing data, arsitektur model, dan evaluasi performa.

ML.NET Dogs vs Cats Classification Example

Create an algorithm to classify whether images contain either dog or a cat.

Transfer Learning on Dogs-vs-cats

A basic Dogs vs Cat identifier.

Using Transfer learning to classify dog and cats images

This project focuses on classifying images of cats and dogs using Convolutional Neural Networks (CNNs) with PyTorch. The dataset used for this project is from Kaggle's "Dogs vs Cats Redux" competition.

Simple dogs vs cats classification

Using Densenet121 & Adam Optimizer on a Jupyter Notebook

Creating a convolutional neural network to classify cats vs dogs using the Kaggle dataset

Code that performs image classification by differentiating images of Dogs from Cats.

A project to classify images of dogs and cats using a Convolutional Neural Network (CNN) built with TensorFlow and Keras. The project involves data preprocessing, model building, training, evaluation, and visualization of classification accuracy