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Schedule Interview NowAt Softaims, I’ve found a workplace that thrives on collaboration and purposeful creation. The work we do here is about more than technology—it’s about transforming ideas into results that matter. Every project brings a mix of challenges and opportunities, and I approach them with a mindset of continuous learning and improvement. My philosophy centers around three principles: clarity, sustainability, and impact. Clarity means designing systems that are understandable, adaptable, and easy to maintain. Sustainability is about building with the future in mind, ensuring that the work we do today can evolve gracefully over time. And impact means creating something that genuinely improves how people work, connect, or experience the world. One of the most rewarding aspects of working at Softaims is the diversity of thought that every team member brings. We share insights, question assumptions, and push each other to think differently. It’s this culture of curiosity and openness that drives the quality of what we produce. Every solution we deliver is a reflection of that shared dedication. I’m proud to contribute to projects that not only meet client expectations but also exceed them through thoughtful execution and attention to detail. As I continue to grow in this journey, I remain focused on delivering meaningful outcomes that align technology with purpose.
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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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