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Becoming a Computer Vision Engineer

Explore the transformative power of computer vision AI with Ultralytics. Discover industry applications and learn from expert engineers like Muhammad Rizwan Munawar.

Computer vision (CV) is a field of artificial intelligence that trains computers to interpret and understand the visual world. The technology works much like human sight, but with a few notable differences: humans have lifetimes of context to train how to tell objects apart, how far away they are, whether they are moving, and whether there is something wrong with an image.

CV technology relates to computers not only being able to visualize images, but also extracting the message or purpose of an image, such as determining distances and movements of incoming objects.Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has been able to take great leaps in recent years and has been able to surpass humans in some tasks related to detecting and labeling objects.

CV enables real-world solutions to industries such as the medical industry, for example, where it is extremely useful for diagnosis implementations. However, CV’s utility also extends to numerous other applications, such as sports, retail, agriculture, transportation, manufacturing, and more. At Ultralytics, we make training models and machine learning accessible to everyone. Our goal is to help you take advantage of the power of Artificial Intelligence without having to worry about all the technical details. From our efforts, we’ve seen even middle schoolers get started with training their models with Ultralytics HUB and YOLOv5.

“Computer vision is one of the most remarkable things to come out of the deep learning and artificial intelligence world. The advancements that deep learning has contributed to the computer vision field have really set this field apart.”

Wayne Thompson, SAS Data Scientist

CV engineers apply vision AI and machine learning research to solve real-world problems. CV engineers generally have a significant amount of experience with various systems, such as image recognition, machine learning, edge AI, networking and communication, deep learning, artificial intelligence, advanced computing, image annotation, data science, and image/video segmentation.So, without further ado, we would like to introduce you to a computer vision engineer and share his experience.

Meet Muhammad!

Muhammad Rizwan Munawar

Muhammad Rizwan Munawar is a Computer Vision Engineer. He has completed his bachelor’s in Computer Science with Artificial Intelligence as a specialization field from COMSATS University Islamabad, Wah Campus. His expertise is not limited to the vision area, because he knows that extra skills can help him to grow and level up his career, so he also has knowledge of desktop applications, web front-end, and attractive dashboard development. Currently, he works as a freelancer developing solutions for different use cases based on his clients’ needs.

How did you get into machine learning and vision AI?

"Well, it has been a journey of hurdles and consistent hard work. When I started, I was not even aware of object detection, but I was curious and passionate mainly about vision AI. I was in the final year of my studies, when I started freelancing, just to learn the skills. In parallel, I also started learning basic machine-learning concepts from various YouTube channels. After spending 7-8 months consistently working, I developed a good understanding of vision AI and deep learning and decided to continue my professional career in the CV field."

Tell us about your experience with YOLOv5!

"I have been using YOLOv5 since it was released, but for proper development and modification according to different use cases, I have been using YOLOv5 for 1.5 years.""Initially, the problem I was dealing with related to object detection, so I began exploring different algorithms related to object detection. After spending some time on research, I compared the map for different object detectors and realized the accuracy of YOLOv5 on the coco dataset is very high when compared to other object detectors at the time. So, I labeled my data, and fine-tuned YOLOv5 on my custom data, with the purpose of detecting people."YOLOv5 is very easy to use, modify and fine-tune and its huge community is always available to help if someone encounters an issue. The regular updates of YOLOv5 provide me with day-by-day easiness to do object detection in a very efficient way."    

Muhammad’s 3 Tips for Beginners

  1. Regularly learn new concepts and make your routine consistent. Muhammad attributes consistency as one of the biggest factors in his success.
  2. Keep thinking about new ideas, it doesn’t matter if they are stupid! They will help you to think about things in depth. Try to implement these ideas for a certain level and write them down in some document. Always follow this strategy.
  3. Develop projects related to CV. Regularly working on projects will help you to learn and develop a passion in your mind for the field of CV.

Thanks for reading about Muhammad’s journey! If you want to learn more about his work, check out his website. And, to stay up to date as we share the most recent YOLOv5 and vision AI news with you, follow us on Twitter and Linkedin!  

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