Easy-to-use finetuned YOLOv8 models.

In this project, I used Python and TensorFlow to classify traffic signs. Dataset used: German Traffic Sign Dataset. This dataset has more than 50,000 images of 43 classes. I was able to reach a +99% validation accuracy, and a 97.3% testing accuracy.

Rank images using TrueSkill by comparing them against each other in the browser. 🖼📊

This repository presents a couple of approaches to the problem of multi-view image classification. I faced this challenge during a hackathon in which I participated, and decided to share my code here. I've also written a Medium article to provide further details and explanations. Feel free to check it out !

A curated dataset of malware and benign Windows executable samples for malware researchers

Face recognition based attendance system

Traffic sign classification for the German Traffic Sign Dataset

The project aims early detection of 'Autism' and 'ADHD' through a web platform fueled by machine learning.

Site where you can draw digits and they are recognized

AI Assisted Image and Video Training Data Labeling @ Scale

Deploy an Image classification website with Ngrok

Image Classification Web app built using Hasura

A text and media analysis service for Meedan Check, a collaborative media annotation platform

Image Classifier website with Deep Learning using Flask

A collection of datasets and neural networks for microorganism image classification

classify plant seedling into weed seedling and crop seedling using CNN

☘️ Deploys a trained PyTorch image classification model with Flask to a FloydHub model API

A Django based web project that uses custom YOLO model, trained on google open images, to detect and count number of fruits(Apple, Mango, Orange, Pomegranate, tomato) in an image.

Pytorch Baby-Vibes Happy/Crying Image Classifier built using fastai, Python-flask, javascript, html, css, bootstrap and deployed on Raspberry Pi 4

Dataset Analysis and CNN Models Optimization for Plant Disease Classification.