这是一个基于tensorflow和python的猫狗分类算法

My Deep Learning Work

Implementation of binary classification with Dog and Cat images using VGG16 architecture and Transfer Learning techniques

This Project is based on Neural Network to classify between Dogs and Cats.

Developing a web app of machine learning model using flask is quite easy. One should have some basic knowledge in web development,not so much but quite a bit. It is just a introductory web app in flask classifying cat vs dog by deep learning model.

This tutorial demonstrates data augmentation: a technique to increase the diversity of your training set by applying random (but realistic) transformations, such as image rotation. You will learn how to apply data augmentation in two ways: Use the Keras preprocessing layers, such as tf. keras.

Binary classification, SHAP (Explainable Artificial Intelligence), and Grid Search (for tuning hyperparameters) using EfficientNetV2-B0 on Cat VS Dog dataset.

Deployed Cat Dog Classification using pre trained model VGG16

A REST API for ml model using Django REST framework

In this project, a comparative study was done between Transfer Learning using VGG16 and a multi-layered CNN Image Classifier.

VGG Deep Convolutional Network fine tuned on Cat vs Dog dataset using Transfer Learning. VGG was trained on famous IMAGENET dataset.