4 posts
What is YOLO? YOLO (You Only Look Once) is a real-time object detection architecture that locates and classifies objects in an image in a single neural network pass. This guide: a clear definition, why YOLO matters, how it works, bounding boxes and the grid, YOLO versions, sector examples from Türkiye, KVKK, comparisons, and FAQs.
What is deep learning? Deep learning is more than simply using neural networks with many layers. It is a way of learning representations from data, capturing patterns at multiple levels of abstraction, and optimizing complex decision systems end to end. This is why it has become central in computer
Choosing a model in computer vision is no longer just a question of “which architecture has higher accuracy.” With the rise of Vision Transformers, engineering teams and organizations now need to make more deliberate choices between the long-established practical strengths of CNNs and the scalable representation power of transformer-based visual models. But this decision is often discussed too narrowly through a single benchmark number. In reality, CNNs and Vision Transformers differ substantially in data requirements, inductive bias, training stability, compute profile, inference cost, explainability, edge deployment suitability, and task-specific behavior. This guide compares CNNs and Vision Transformers not only theoretically, but also across classification, detection, segmentation, multimodal systems, and production constraints, showing which approach tends to fit which problem more naturally.
High benchmark accuracy in vision systems is not enough to guarantee reliable real-world behavior. A model may perform strongly in controlled evaluation settings yet degrade significantly in production due to camera variation, lighting changes, background diversity, label quality issues, class imbalance, rare scenarios, device differences, seasonal changes, and workflow drift. That is why modern computer vision projects are not only about model architecture. They require strong data quality management, domain shift analysis, slice-based evaluation, error-cost awareness, production monitoring, and continuous improvement loops. This guide explains how to manage data quality, diagnose domain shift, measure real-world performance, and build robust vision systems that remain reliable beyond the lab.