2022-06-28, 16:30–16:50, PyData
Being able to classify images is at the heart of many recommender systems. In this talk, we will share a simple trick to make the task of building an image classifier as easy as building a standard text classifier.
Building a task-specific image classification solution typically requires leveraging Computer Vision transfer learning techniques. It involves manipulating complex deep learning models, applying non trivial image preprocessing and using expensive hardware.
But what if you could leverage existing image meta-data annotations to classify our images?
In this talk we will share a simple trick to make the task of building an image classifier as easy as building a standard text classifier. This reduction simplifies preprocessing and training and it also dramatically reduces the required hardware & computation time. This reduction is made possible by leveraging ready-made computer vision APIs provided by the public cloud vendors. These APIs extract semantic textual labels from images that in turn can be used to build simple, shallow NLP classifiers.
This simple reduction has helped us deliver fast & cheap Python-based image classification models to production and is widely used in Outbrain products.