Using Shutterstock’s image datasets to train your computer vision models
Image classification and object detection technology allows you to build scalable artificial intelligence models for business cases like visual search, product recommendations, autonomous vehicle object recognition, content moderation, and more. Today, services like Amazon Rekognition offer APIs to perform image analysis and object recognition. However, if your use case requires a more custom image classification model, you must have access to an image dataset that not only fits your custom use case, but also contains enough image samples to accurately train your machine learning model.
In this blog post, I show you how to gain access to and train computer vision models on custom image datasets using Shutterstock Image Datasets in AWS Marketplace. These datasets are collections of curated images from Shutterstock’s library of more than 370 million images. You can subscribe to one of the preexisting collections, such as Food & Beverage, Clothing, or Hospitality, or you can work with the Shutterstock Data Exchange team to request a custom collection of images for your use case. Each image includes a descriptive title with up to 200 characters and an optimal 7-50 keywords.
For this post, I use the Free Sample: Images & Metadata of “Whole Foods” Shoppers dataset from Shutterstock’s Image Datasets to demonstrate how to train a multi-label image classification model using Shutterstock’s pre-labeled image assets. This dataset contains images of Whole Foods shoppers. Each image is tagged with 7-50 keywords describing what is seen in the image. The first image shows a male store employee behind a deli counter selecting an item for a female customer holding a shopping basket. The second image shows a couple choosing fresh produce. The third image shows a woman with a shopping cart holding a head of lettuce.
