Deep learning, and specifically convolutional neural networks, are now standard techniques for tackling computer vision tasks. Their results on benchmarks are impressive, but moving from curated standard datasets to a messy real-world setting still presents challenges. We explore some of the challenges encountered when deploying a grocery products classifier to a live in-store environment.
Deep learning, and specifically convolutional neural networks, are now standard techniques for tackling computer vision tasks. Their results on benchmarks are impressive, but moving from curated standard datasets to a messy real-world setting still presents challenges.
In this talk we explore some of the challenges encountered when deploying a simple model to a live in-store environment, specifically for the task of classifying grocery products.
The talk will cover: * Difficulties obtaining & preprocessing data, especially when dealing with legacy data sources. * Unanticipated situations encountered in real world data. * Label quality (both from automatic systems and due to differences in interpretation with human labelers) * Dealing with highly imbalanced datasets. * Distribution shift when deploying models to new (even very similar) settings
We will finish with some future perspectives, looking at the general problem of classifying the whole range of 10s of thousands of products sold in a large store.