'Use Cases and Roll-Out Tips for Image Recognition in Retail' by ITRexGroup imagerecognition imageprocessing
. Bringing image recognition into their technology mixes, retailers hope to optimize inventories, simplify checkouts, and boost customer experience.
A basic CNN used for retail image recognition features two components — an object detector and an object classifier. The approach described above makes up the base for many retail image recognition models. Two of the most popular ones are R-CNN and YOLO. Both are deep learning model families, and both apply well for retail product recognition. Let’s briefly recap the details about each.The R-CNN family includes such techniques as R-CNN, Fast R-CNN, and Faster R-CNN explicitly designed for object localization and recognition.
The authors of Faster R-CNN make more improvements to the original architecture and achieve even more excellent outcomes. Faster R-CNN is ten times speedier than Fast R-CNN and 250 times speedier than R-CNN, which makes it an optimum choice for latency-critical applications.The YOLO family is a bit less accurate than the R-CNN family. Its lower predictive accuracy can be traced back to occasional localization errors. The upside of the YOLO model is its high processing speed.
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