Simple Example of Image Recognition

Using transfer learning to predict if there exists a cotton disease in the plant or not. The best were the inceptionv3 model and the ResNet50 model and then finally made a model for the web using flask for an end-to-end deployment of this project.

A Artifitial Intelligent Securtiy system that detects humans using a camera and beeps an alarm .

Here is an implementation of InceptionV3 and VGG-16 models in Python from scratch. These models were then trained on a dataset of handwritten alphabets. An experiment was carried out to achieve higher accuracy by using different combinations of optimizers and learning rates. These models were then compared to the inbuilt models in Python.

Smart bike using deep learning and iot

an implementation of the Convolutional Neural Network model and Transfer Learning (InceptionV3) model to classify horse or human images.

Food Crop disease detection model using InceptionV3 architecture

Jupyter notebook was made for doing machine learning which classify images

Identifying vehicle and appliance damage from an image on a scale of low, moderate, high

Its a convNet built upon InceptionV3 and trained on 928 pokemon classes.

Udacity's Deep Learning Nanodegree Project - Dog-Breed Classifier

Inception_v3 in Tensorflow 2

Comparison between different DL models such as VGGnet,InceptionV3,Resnet for copy move forgery detection

Image caption generator is a task that involves computer vision and natural language processing concepts to recognize the context of an image and describe them in a natural language like English.

This repository contains implementations of Squeeze and excitation networks on ResNet, ResNeXt, and InceptionV3 models. More detail in the report.

With transfer learning approach on imbalanced dataset we've achieved 81% accuracy @inceptionV3

Implementation of SE-ResNet models and other SE-Nets