🤖 This repository houses a collection of image classification models for various purposes, including vehicle, object, animal, and flower classification. Each classifier is built using deep learning techniques and pre-trained models to accurately identify and categorize images based on their respective classes. Also includes a sample Flask backend!

Project on Vehicle Detection, Classification, and Counting. Done in python using OpenCV.

Traffic analysis using YOLOv4 and OpenCV

Vehica: Vehicle classification based on RetinaNet. 基于 RetinaNet 和 Stanford Car Dataset 的车辆型号检测识别

Classifies 574 vehicle make-models using a ResNet50 architecture and YOLOv5

The goal of the project is to classify the vehicles, count their frequency, track them using unique ID and provide safety measures to the users by flagging the suspicious ones.

Scrapes Google to create a ~700k sample of US passenger vehicle images with 574 distinct make-models

Main aim of the project is to develop a machine learning based system for detecting vehicles that drive up to the gate and to classify them into emergency services and civil cars. This may have a significant impact on the speed of gate opening and reduce the human associated problems and risks.

Intelligent Transportation System based on Artificial Intelligence

Computer Vision Project for Vehicle Detection and Classification Dashboard of Live Singapore Traffic using YOLOv11.

A Simple Vehicle Classifier Based on Keras and Tensorflow + Training Script

It is work about the evaluation of cars according to their characteristics in order to enhance profitability by purchasing appropriate ones

Vehicle type classification using Transfer Learning

SmartGateAI is an AI-powered automated access control system for secure vehicle entry. It combines license plate recognition, vehicle classification, and facial verification using deep learning and computer vision to ensure only authorized drivers are granted access.

Efficient vehicle classification using machine learning and deep learning models for Intelligent Traffic Systems. Classifies five vehicle types with models like SVM, Random Forest, and CNN, utilizing HOG, LBP, and Gabor features for enhanced accuracy in smart city traffic management.