Sheikh Muhammad Saqib
Papers
1
Total Citations
3
H-Index
1
About
Sheikh Muhammad Saqib is a rising researcher in computer vision and applied machine learning, with a focus on accessible, real-world object detection systems. His most cited work, a 2025 comparative study on the effectiveness of Teachable Machine, MobileNet, and YOLO for object detection, evaluates these models across diverse categories including birds, horses, laptops, and sandwiches. This research provides practical guidance for selecting efficient detection frameworks, balancing accuracy with computational accessibility. With 3 citations already for this recent paper, Saqib’s work is gaining traction among practitioners seeking deployable AI solutions. His contributions highlight the importance of democratizing computer vision tools, making advanced detection techniques usable for non-experts. Saqib’s research bridges the gap between cutting-edge models and practical applications, offering insights that benefit students, hobbyists, and industry professionals alike. As he continues to explore efficient object detection pipelines, his work promises to shape how everyday users leverage AI for visual recognition tasks.
Research Focus
Key Achievements
Top Papers
- 1