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Schedule Interview NowMy name is Yacine R. and I have over 5 years of experience in the tech industry. I specialize in the following technologies: OpenCV, Data Science, Python Scikit-Learn, Deep Learning, NumPy, etc.. I hold a degree in , , . Some of the notable projects I’ve worked on include: Face Recognition with Python, Dlib, and Deep Learning, Real-Time Vehicle Detection, Tracking and Counting in Python, Automatic Number Plate Recognition with YOLO book. I am based in Longjumeau, France. I've successfully completed 3 projects while developing at Softaims.
I thrive on project diversity, possessing the adaptability to seamlessly transition between different technical stacks, industries, and team structures. This wide-ranging experience allows me to bring unique perspectives and proven solutions from one domain to another, significantly enhancing the problem-solving process.
I quickly become proficient in new technologies as required, focusing on delivering immediate, high-quality value. At Softaims, I leverage this adaptability to ensure project continuity and success, regardless of the evolving technical landscape.
My work philosophy centers on being a resilient and resourceful team member. I prioritize finding pragmatic, scalable solutions that not only meet the current needs but also provide a flexible foundation for future development and changes.
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Thales Group
In this project, I demonstrate how to perform face recognition using the Dlib library and deep learning. I used a pre-trained network provided by Dlib. This network has been trained on a dataset of over 3 million images. The network is called the ResNet-34.
By combining the power of YOLOv8 and DeepSORT, in this project, I show how to build a real-time vehicle tracking and counting system with Python and OpenCV.
I created a comprehensive guide that provides detailed explanations, practical examples, and step-by-step tutorials to help readers master YOLO. It teaches how to train the YOLO model to accurately detect and recognize license plates in images and real-time videos, as well as how to build an end-to-end ANPR system with YOLO from data collection to deployment. The book includes source code, hands-on coding experience, and a step-by-step guide with clear explanations and code examples, allowing readers to gain practical skills that can be applied to real-world projects.
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