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Hamza A. AI, Machine Learning and Computer Vision Platforms

My name is Hamza A. and I have over 5 years of experience in the tech industry. I specialize in the following technologies: Machine Learning, Computer Vision, Deep Learning, FastAPI, Natural Language Processing, etc.. I hold a degree in Bachelor of Computer Science (BCompSc), . Some of the notable projects I’ve worked on include: Facial Recognition Offline App, Virtual Dress Try On, Facial Recognition App, Facial Recognition Based Attendance System. I am based in Lahore, Pakistan. I've successfully completed 4 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.

Main technologies

  • AI, Machine Learning and Computer Vision Platforms

    5 years

  • Machine Learning

    3 Years

  • Computer Vision

    4 Years

  • Deep Learning

    4 Years

Additional skills

Direct hire

Potentially possible

Previous Company

Techlogix

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Experience Highlights

Facial Recognition Offline App

I have developed a comprehensive offline facial recognition application designed to function entirely without an internet connection, ensuring privacy and security for its users. This app leverages state-of-the-art technologies, including RetinaFace for face detection and a pre-trained model for facial recognition, to deliver high accuracy and performance.

Virtual Dress Try On

In this project, I spearheaded the development of a state-of-the-art virtual try-on solution leveraging cutting-edge technologies such as cloth masking, densepose, parse agnostic, openpose, and image parsing. Our goal was to provide users with a seamless and immersive virtual shopping experience, allowing them to try on clothes virtually before making a purchase. *Key Features and Technologies:* Cloth Masking: Implemented advanced cloth masking techniques to accurately overlay virtual garments onto user images. This involved creating precise masks around clothing items to ensure realistic integration with the user's body. Densepose and Parse Agnostic: Leveraged densepose and parse agnostic techniques to accurately map the user's body pose and shape. This allowed for precise alignment of virtual garments with the user's anatomy, enhancing the realism of the virtual try-on experience. OpenPose Integration: Integrated OpenPose, a state-of-the-art pose estimation library, to accurately detect and track the user's body keypoints and skeletal structure. This enabled real-time tracking of the user's movements and poses, ensuring a dynamic and interactive virtual try-on experience. Image Parsing: Utilized image parsing algorithms to segment and classify clothing items in user images. This enabled automatic detection of clothing regions and facilitated seamless integration of virtual garments with user images. Project Achievements: Developed a robust and scalable virtual try-on platform capable of handling a wide range of clothing styles and body types. Achieved high levels of realism and accuracy in virtual garment rendering, resulting in increased user engagement and satisfaction. Seamlessly integrated advanced pose estimation and cloth masking techniques to deliver a truly immersive virtual try-on experience. Collaborated closely with designers, developers, and stakeholders to ensure the successful implementation and deployment of the virtual try-on solution. Technologies Used: Python OpenCV TensorFlow PyTorch OpenPose Densepose Parse Agnostic Image Parsing Algorithms Conclusion: Our virtual try-on solution represents the convergence of cutting-edge AI technologies and e-commerce, enabling users to explore and try on clothing in a virtual environment with unprecedented realism and accuracy. By leveraging advanced cloth masking, pose estimation, and image parsing techniques, we have created a platform that revolutionizes the way people shop for clothes online.

Facial Recognition App

I have worked on the Backend part of the FR app use InceptionResNetV2 and Retinanet

Facial Recognition Based Attendance System

i have worked on different Machine Learning and Data Science Relevant Projects and one of the project which i have mentioned is that Facial Recognition Based Attendance System Using Machine Learning Algo at the Backend and used PyQt5 as frontend

Education

  • Lahore Garison University

    Bachelor of Computer Science (BCompSc) in AI devolpment

    2019-01-01-2023-01-01

  • WorldQuant University

    in Masters in financial engineering

    2023-01-01-2025-01-01

Languages

  • English
  • Panjabi, Punjabi
  • Urdu

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