Butterfly Classifier Inference API. Finetuned using MobileNetV2.

An Image Classification project w/ MobileNetV2 and DenseNet-121. Leveraging techniques like Hyperparameter Tuning, Transfer Learning, Imagine Preprocessing Techniques and Ensemble Methods.

Este repositorio es el resultado de mi trabajo de fin de máster en Data Science & Business Analytics

Implementation of popular deep learning networks with TensorRT network definition API

Final Year project developed using MobileNetV2 and implemented REST API using Django

This Python script calculates the similarity between a base image and a dataset of images using structural similarity and color histogram comparison. The results are sorted by similarity, can be showed with matplotlib and saved to a JSON file.

This is a project focused on identifying the presence of pneumonia in chest X-ray images. Each image can be classified into one of three categories: Bacterial Pneumonia, Viral Pneumonia, or Normal.

Utilize a MobileNetV2 encoder and Pix2Pix decoder to perform precise semantic segmentation, distinguishing objects in images, such as identifying flooded areas in flood images. The purpose is to enable accurate object delineation for applications like disaster response and environmental monitoring.

Breast Cancer H&E classification of Images and Image Generation

2024-1 XAI611 고급빅데이터분석 Project Proposal

MoodSculpt is a project that analyzes user emotions using facial recognition technology and suggests songs and movies based on the detected mood. It utilizes Transfer Learning, Spotify API for music recommendations, and OMDB API for movie recommendations.

A project requirement for the subject 'CS333-M - Data Analytics'

Image forgery detection using CNN fusion model achieving 85% test accuracy. Features ELA preprocessing and fusion of InceptionV3, VGG16, and MobileNetV2. Ideal for digital forensics.

This is an ongoing project of applying evolutionary computing to TensorFlow.js as alternative to their local search default routines

Pytorch based Android app, which classifies images using MobileNet-V2 model, takes image using CameraX API

Using transfer learning, trained a mobilenetV2 to tell apart different breeds of dogs using Kaggle information.

Your Friendly Neighborhood GPU Superhero Fighting COVID in Real-Time! With the power of MobileNetV2 and a lightning-fast NVIDIA RTX 3060, this system detects facemasks at warp speed (60 frames per second), ensuring safety with a 95% accuracy rate.

in this project we used image processing Technique to classify 9 class malwares our final goal is to reach an appropriate model with high accuracy and small size and computational cost

Predicts Eye Disease from Images

This repository contains the code and resources for a deep learning project focused on food classification from images. The project aims to develop models capable of accurately categorizing food items based on their images for an online food delivery application.