Empowering Customized Object Detection: Amazon Rekognition’s Custom Labels

Introduction

In today’s competitive business landscape, image analysis has become increasingly important for organizations across various industries. Amazon Rekognition’s custom labels, an automated machine learning (AutoML) service, offer a powerful solution to recognize objects and scenes in photographs that can be tailored to meet specific business needs. Whether identifying items on store shelves, locating logos on social media posts, classifying machine parts on assembly lines, or determining plant health, custom image recognition models can drive operational efficiency and inform strategic decision-making.

However, developing custom image recognition models is a significant undertaking that requires a considerable investment of time, resources, and expertise. Traditionally, generating enough data to train these models would take months and require a large team of labelers to prepare the data.

What is the need for Amazon Rekognition Custom Labels?

• Amazon Rekognition Custom Labels allow for highly accurate image recognition, which can help businesses to automate processes and improve productivity. For example, a retail company could use custom image recognition models to automatically identify items on store shelves, improving inventory management and reducing the need for manual labor.
• The highly customizable service allows users to train models specific to their needs and requirements. This means that businesses can develop models tailored to their products, branding, or unique features, resulting in even higher levels of accuracy and specificity.
• Amazon Rekognition Custom Labels are scalable and cost-effective. The service can handle large volumes of images and easily integrate them into existing workflows and applications. Additionally, the pricing structure is based on usage, meaning businesses only pay for what they use and can easily scale up or down as needed.

Features of Amazon Rekognition Custom Labels

• Simplify data labeling – by using its visual interface
• Automated machine learning- includes AutoML capabilities
• Simplified model evaluation, inference, and feedback – model’s performance

Amazon Rekognition Custom Labels now offers the ability to Copy Trained Computer Vision Models between AWS Accounts

Custom labels require no prior knowledge or computer vision skills. Users can copy a trained custom label model from their AWS account to another within the same region. This new feature allows customers to easily move custom label models between different environments, such as development, QA, integration, and production, without copying the original training/test data sets and retraining the model. Custom label mock-ups can be copied between accounts within 10 minutes.

AWS partners and customers use multiple AWS accounts provisioned based on software development phases (build, test, staging, deployment, etc.) or business functions (data science, engineering, etc.), or a combination of both intentions often do. Previously, custom label models were only available in AWS accounts where they were trained. Customers who developed a model in a development environment and wanted to deploy it to production had to copy the training dataset to each AWS account and train the model from scratch. This was a time-consuming process and slowed the deployment of models to production.

This new feature allows an AWS partner to develop and tune a model in his AWS development account and copy the latest version of the model to a customer-operated production account. Similarly, enterprise customers with global operations can incorporate ML Ops best practices by developing and testing their custom label models in an AWS development account and migrating to a production account when ready for deployment. No need to retrain the model from scratch. Customers in regulated industries such as finance and insurance can now share models across development, test, and production accounts without sharing sensitive training records.

Amazon Rekognition Custom Labels Now Supports Autoscaling of Inference Units

Amazon Rekognition Custom Labels enables customers to create custom computer vision models to identify their unique objects and scenes without deep machine learning. An automated machine learning (AutoML) service that allows you to recognize skills. Starting today, Custom Labels can automatically scale the inference units of a trained model based on the customer’s workload. This reduces model inference costs as customers no longer need to provide inference units to support spiked or fluctuating image volumes.

Previously, custom-label customers with unpredictable workloads had to specify a minimum inference unit to support the peak volume of images they expected to process. This is expensive as it consumes the smallest inference unit, even if the volume is low or non-existent. Autoscaling support now allows customers to specify both minimum and maximum inference units. Custom labels dynamically adjust the inference units up or down within the specified minimum and maximum inference units based on the volume of the image. Customers are billed only for the inference units they consume. Note that the minimum allowable reasoning unit is 1. Suppose the customer specifies 1 as the minimum unit of reasoning and 5 as the maximum unit of reasoning. If a customer’s workload consumed 5 inference units in 5 hours and 1 inference unit for the rest of the day, the customer would only be charged $176 (19 hours x $4 per hour x 1 inference unit + 5 hours x $4 per hour x 5 inference units). Without autoscaling, the customer would be billed $480 (24 hours x $4 per hour x 5 inference units).

Conclusion

Amazon Rekognition Custom Labels is a powerful tool for businesses and developers looking to improve image recognition accuracy, automate processes, and reduce costs. Its highly customizable and scalable nature makes it a great choice for various use cases, from retail and manufacturing to healthcare and finance.

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