Flask Blueprint & RESTful application with various image classification models.

An image classification algorithm using CNN in Pytorch

Deploy a pre trained(tf) model using TensorFlow serving and Flask

本项目基于TensorFlow训练了一个CNN模型，开发了一个简易的猫狗识别器。整个项目较好体现了面向对象思想，相信有点编码基础的同行能够非常快速地理解整个代码结构。ReadMe中具有详细的最终效果，博客讲解链接、参考教程等详细信息。

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

Task 3 of the Prodigy InfoTech ML internship which involves Implementing a support vector machine (SVM) to classify images of cats and dogs.

Using VGG16 feature extractor with Scikit-learn Support Vector Machine to train the model to classify dogs and cats. Model accuracy: 94.71%

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

A Convolutional Neural Network (CNN) model to predict Cat/Dog with Keras

Contains applications related to Deep Learning Classification. The two applications perform classification using different trained models.

Miscellaneous AI/ML examples

Dog cat classification using pytorch, neural network

A collection of Streamlit-based applications showcasing deep learning in action, including movie recommendations, disease detection, and image classification. Pre-trained models and datasets are hosted on Google Drive for seamless integration.