At Planorama, we leverage artificial intelligence to help our clients boost their retail execution and merchandising. Our solutions which incorporate breakthrough deep learning algorithms can instantly analyze and recognize millions of product items based on shelf pictures from any source.
In the deep learning computer vision approach, the system learns how to recognize a target by itself. Based on hundreds of raw pictures of the given target, deep learning algorithms can build their own optimal set of visual features to recognize this target. The more pictures, the more accurate the system.
For visual product recognition — the kind we perform with Planorama — deep learning has several advantages that make it an ideal approach. The system can be fed with many good examples of the targets under all sorts of conditions — products on shelves. Over time, the system learns to self-adjust and recognize new examples of products it has never seen. Without the need for explicit rules, the system can recognize products even with low-quality inputs. It’s a generic system, fast to roll out for new product bases, adaptive, and robust to real conditions to get truly accurate results.
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Deep learning: the new secret to great retail execution
Technology is evolving at an increasingly rapid pace these days, and one of the latest developments could revolutionize image recognition in machines. It’s called “deep learning.” While the concept got exciting press coverage in the gaming world last year — when Google’s AlphaGo system beat a champion player in the ancient board game of Go — its importance extends much further.