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New Amazon tool helps machine learning models identify unique objects – TechCrunch

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Amazon announced a new capability today called Amazon Rekognition Custom Labels to help customers train machine learning models to understand a set of objects when there is a limited set of information. Typically, machine learning models have to work on large data sets to learn something like what's a picture of a dog, as opposed to some other animals. Amazon Rekognition Custom Labels can work with a limited data set to teach the algorithm a group of objects specific to a given use case. "Instead of having to train a model from scratch, which requires specialized machine learning expertise and millions of high-quality labeled images, customers can now use Amazon Rekognition Custom Labels to achieve state-of-the-art performance for their unique image analysis needs," the company wrote in a blog post announcing the new feature. For example, you may want to teach the model to identify a set of engine parts, a limited set of information, which has a lot of meaning to a specific use case.


Detecting playful animal behavior in videos using Amazon Rekognition Custom Labels

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Historically, humans have observed animal behaviors and applied them for different purposes. For example, behavioral observation is important in animal ecology, such as how often the behaviors are, when the behaviors occur, or whether there is individual difference or not. However, identifying and monitoring these behaviors and movements can be hard and can take a long time. To provide an automation for this workflow, a team from the agile members of pharmaceutical customer (Sumitomo Dainippon Pharma Co., Ltd.) and AWS Solutions Architects created a solution with Amazon Rekognition Custom Labels. Amazon Rekognition Custom Labels makes it easy to label specific movements in images, and train and build a model that detects these movements.


Training a custom single class object detection model with Amazon Rekognition Custom Labels

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Customers often need to identify single objects in images; for example, to identify their company's logo, find a specific industrial or agricultural defect, or locate a specific event, like hurricanes, in satellite scans. In this post, we showcase how to train a custom model to detect a single object using Amazon Rekognition Custom Labels. Amazon Rekognition is a fully managed service that provides computer vision (CV) capabilities for analyzing images and video at scale, using deep learning technology without requiring machine learning (ML) expertise. Amazon Rekognition Custom Labels lets you extend the detection and classification capabilities of the Amazon Rekognition pre-trained APIs by using data to train a custom CV model specific to your business needs. With the latest update to support single object training, Amazon Rekognition Custom Labels now lets you create a custom object detection model with single object classes.


Global Big Data Conference

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One of Amazon's most recent announcements was the release of their new tool called Amazon Rekognition Custom Labels. This advanced tool has the capability to improve machine learning on a whole new scale, allowing for better data analysis and object recognition. Amazon Rekognition will help users train their machine learning models more easily and allow them to understand a set of objects out of limited data. In other words, this capability will make machines more intelligent and capable of recognizing items with far less data sets than ever before. Machine learning includes a scientific study and adoption of algorithms that allow computers to learn new information and functionalities without needing direct instructions.


Build Natural Flower Classifier using Amazon Rekognition Custom Labels

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Building your own computer vision model from scratch can be fun and fulfilling. You get to decide your preferred choice of machine learning framework and platform for training and deployment, design your data pipeline and neural network architecture, write custom training and inference scripts, and fine-tune your model algorithm's hyperparameters to get the optimal model performance. On the other hand, this can also be a daunting task for someone who has no or little computer vision and machine learning expertise. This post shows a step-by-step guide on how to build a natural flower classifier using Amazon Rekognition Custom Labels with AWS best practices. Amazon Rekognition Custom Labels is a feature of Amazon Rekognition, one of the AWS AI services for automated image and video analysis with machine learning. It provides Automated Machine Learning (AutoML) capability for custom computer vision end-to-end machine learning workflows.