Skip to content

detecting model and the name of the cars with deep neural networks like VGG-16 , YOLOv5 and YOLOv8

License

Notifications You must be signed in to change notification settings

CWLeonis/Car_detection_Deep_learning

Repository files navigation

Pay attention to the path of files in the codes.You have to change the path of files to the desires path. also if you have any issue running the codes feel free to ask :)

Car_detection_Deep_learning

detecting model and the name of the cars with deep neural networks like VGG-16 , YOLOv5 and YOLOv8 This project tries to detect a car name and its model in an image or a video. VGG-16 uses tensorflow object detection model to detect cars and from the detections in each frame each vehicle can be tracked across a video. YOLOV5s and YOLOv8s uses pythorch. the focus of this project is on YOLOv5s. this algorithm trained on a dataset contains of 2100 images of 5 classes. you can use the dataset provided for this project.It's availabel on roboflow platform.

Getting_Started

The project created in python by using tensorflow so you must be familiar with tensorflow and basic object detection and you must also know basic maths for understanding the tracking algorithm. You must be also familiar with Deep neural networks and how it works.If you already know about these fields, you can go to the next phase ;).

Prerequisites

Python packages to be installed: Tensorflow (Tensorflow-gpu if you have Nvidia GPU), openCV, imutils, Pillow, numpy, urllib, matplotlib

Results

The results of trianed model:

YOLOv5s:Picture1 jpg

YOLOv5s vs VGG16 vs YOLOv8s:Un

At the end...

hope you enjoy this project, also don't forget that give me a star, thanks in advance ;)

Releases

No releases published

Packages

No packages published