With Amazon Rekognition Custom Labels, you can identify the objects and scenes in images that are specific to your business needs. For example, you can find your logo in social media posts, identify your products on store shelves, classify machine parts in an assembly line, distinguish healthy and infected plants, or detect animated characters in videos.
Developing a custom model to analyze images is a significant undertaking that requires time expertise, and resources, often taking months to complete. Additionally, it often requires thousands or tens-of-thousands of hand-labeled images to provide the model with enough data to accurately make decisions. Generating this data can take months to gather and require large teams of labelers to prepare it for use in machine learning.
With Amazon Rekognition Custom Labels, we take care of the heavy lifting for you. Rekognition Custom Labels builds off of Rekognition’s existing capabilities, which are already trained on tens of millions of images across many categories. Instead of thousands of images, you simply need to upload a small set of training images (typically a few hundred images or less) that are specific to your use case into our easy-to-use console. If your images are already labeled, Rekognition can begin training in just a few clicks. If not, you can label them directly within Rekognition’s labeling interface, or use Amazon SageMaker Ground Truth to label them for you. Once Rekognition begins training from your image set, it can produce a custom image analysis model for you in just a few hours. Behind the scenes, Rekognition Custom Labels automatically loads and inspects the training data, selects the right machine learning algorithms, trains a model, and provides model performance metrics. You can then use your custom model via the Rekognition Custom Labels API and integrate it into your applications.
Accurately measure brand coverage
Marketing agencies need to accurately report on brand coverage of their clients in various media. Typically they manually track appearances of their clients’ logos and products in social media images, broadcast, and sports videos. With Amazon Rekognition Custom Labels, agencies can create a custom model specifically trained to detect their client logos and products. Instead of painstakingly trying to follow traditional and social media manually, they can process images and video frames through the custom model to find the number of impressions.
Discover content for syndication
Content producers typically have to search through thousands of images and videos to find the relevant content they want to use for producing shows. For example, a sports broadcaster often needs to assemble highlight films about games, teams, and players for affiliates, which can take hours to manually assemble from archives. By training custom models to identify teams and players by jersey and number, and to identify common game events like goals scored, penalties, and injuries, they can quickly develop a relevant list of images and clips that match the subject of the film.
Improve operational efficiency
Agriculture companies need to rate the quality of their produce before packing them. For example, a tomato producer may manually classify tomatoes into 6 ripeness groups from mature green to red, and packs them accordingly to ensure maximum shelf life. Instead of manually examining each tomato, they can train a custom model to classify tomatoes based on their ripeness criteria. By integrating the model with their manufacturing systems, they can automatically sort the tomatoes, and pack them accordingly.
Simplify data labeling
The Rekognition Custom Labels console provides a visual interface to make labeling your images fast and simple. The interface allows you to apply a label to the entire image or to identify and label specific objects in images using bounding boxes with a simple click-and-drag interface.
Alternately, if you have a large data set, you can use Amazon SageMaker Ground Truth to efficiently label your images at scale.
Automated machine learning
No machine learning expertise is required to build your custom model. Rekognition Custom Labels includes AutoML capabilities that take care of the machine learning for you. Once the training images are provided, Rekognition Custom Labels can automatically load and inspect the data, select the right machine learning algorithms, train a model, and provide model performance metrics.
Simplified model evaluation, inference, and feedback
Evaluate your custom model’s performance on your test set. For every image in the test set, you can see the side by side comparison of the model’s prediction vs. the label assigned. You can also review detailed performance metrics such as precision/recall metrics, f-score, and confidence scores. You can start using your model immediately for image analysis, or iterate and re-train new versions with more images to improve performance. After you start using your model, you track your predictions, correct any mistakes and use the feedback data to retrain new model versions and improve performance.
Creating syndicated content
“In today’s media landscape, the volume of unstructured content that organizations manage is growing exponentially. Using traditional tools users can have difficulty in searching through the thousands of media assets in order to locate a specific element they are looking for. By using the new feature in Amazon Rekognition, Custom Labels, we are able to automatically generate metadata tags tailored to specific use cases for our business and provide searchable facets for our content creation teams. This significantly improves the speed in which we can search for content and more importantly it enables us to automatically tag elements that required manual efforts before. These tools allow our production teams to leverage this data directly and provides enhanced products to our customers across all of our media platforms.“
Brad Boim, Senior Director, Post Production & Asset Management - NFL Media
Boosting marketing performance
"With the introduction of Amazon Rekognition Custom Labels, marketers will be equipped with advanced capabilities within our Agile Creative Studio, enabling them to build and train the specific products (custom labels) that they care about within their ads, at scale, within minutes. Using VidMob’s integration of Amazon Rekognition, customers have historically been able to identify common objects but now the new ability for custom labels will make our platform even more targeted for every business. With a lift of 150% in creative performance and 30% reduction in *human analyst *time, this will adaptively extend their ability to measure their creative performance using VidMob’s Agile Creative Studio.”
Alex Collmer, CEO - VidMob
Detecting anomalies in store receipts
Prodege is a data-driven marketing & consumer insights platform comprised of consumer brands—Swagbucks, MyPoints, Tada, ySense, InboxDollars, InboxPounds, DailyRewards, and Upromise—along with a complimentary suite of business solutions for marketers and researchers.
"Prodege uses Amazon Rekognition Custom Labels to detect anomalies in store receipts. By using Amazon Rekognition Custom Labels, Prodege was able to detect anomalies with high precision across store receipt images being uploaded by our valued members as part of our rewards program offerings. The best part of Amazon Rekognition Custom Labels is that it is easy to set up and requires only a small set of pre-classified images (a couple of hundred in our case) to train the ML model for high confidence image detection. The model’s endpoints can be easily accessed using the API. Amazon Rekognition Custom Labels has been an extremely effective solution to enable the smooth functioning of our validated receipt scanning product and helped us save a lot of time and resources performing manual detection. I can’t even thank the AWS Support Team enough who has been diligently providing us help with all aspects of the product through this journey.”
Arun Gupta, Director, Business Intelligence - Prodege, LLC