HAQM Rekognition Custom Labels Community Showcase

In our Community Showcase, HAQM Web Services (AWS) highlights projects created by AWS Heroes and AWS Community Builders. 

We worked with AWS Machine Learning (ML) Heroes and AWS ML Community Builders to bring to life projects and use cases that detect custom objects with HAQM Rekognition Custom Labels.

The AWS ML community is a vibrant group of developers, data scientists, researchers, and business decision-makers that dive deep into artificial intelligence and ML concepts, contribute with real-world experiences, and collaborate on building projects together.

HAQM Rekognition is a fully managed computer vision service that allows developers to analyze images and videos for a variety of use cases, including face identification and verification, media intelligence, custom industrial automation, and workplace safety.

Detecting custom objects and scenes can be hard, and training and improving a computer vision model with growing data makes the problem more complex. HAQM Rekognition Custom Labels allows you to detect custom labeled objects and scenes with zero Jupyter notebook experience. For example, you can identify logos in streaming media, simplify preventative maintenance, and scale supply chain inventory management. ML practitioners, data scientists, and developers with no previous ML experience benefit by moving their models to production faster, while HAQM Rekognition Custom Labels takes care of the heavy lifting of model development.

In this post, we highlight a few externally published getting started guides and tutorials from AWS ML Heroes and AWS ML Community Builders that applied HAQM Rekognition to a wide variety of use cases, from at-home projects like a fridge inventory checker to an enterprise-level HVAC filter cleanliness detector.

AWS ML Heroes and AWS ML Community Builders

Classify LEGO bricks with HAQM Rekognition Custom Labels by Mike Chambers. In this video, Mike walks you through this fun use case to use HAQM Rekognition Custom Labels to detect 250 different LEGO bricks.

Training models using Satellite imagery on HAQM Rekognition Custom Labels by Rustem Feyzkhanov (with code samples). Satellite imagery is becoming a more and more important source of insights with the advent of accessible satellite data from sources such as the Sentinel-2 on Open Data on AWS. In this guide, Rustem shows how you can find agricultural fields with HAQM Rekognition Custom Labels.

Detecting insights from X-ray data with HAQM Rekognition Custom Labels by Olalekan Elesin (with code samples). Learn how to detect anomalies quickly and with low cost and resource investment with HAQM Rekognition Custom Labels.

Building Natural Flower Classifier using HAQM Rekognition Custom Labels by Juv Chan (with code samples). Building a computer vision model from scratch can be daunting task. In this step-by-step guide, you learn how to build a natural flower classifier using the Oxford Flower 102 dataset and HAQM Rekognition Custom Labels.

What’s in my Fridge by Chris Miller and Siaterlis Konstantinos. How many times have you gone to the grocery store and forgot your list, or weren’t sure if you needed to buy milk, beer, or something else? Learn how AWS ML Community members Chris Miller and Siaterlis Konstantinos used HAQM Rekognition Custom Labels and AWS DeepLens to build a fridge inventory checker to let AI do the heavy lifting on your grocery list.

Clean or dirty HVAC? Using HAQM SageMaker and HAQM Rekognition Custom Labels to automate detection by Luca Bianchi. How can you manage 1–3,000 cleanliness checks with zero ML experience or data scientist on staff? Learn how to detect clean and dirty HVACs using HAQM Rekognition Custom Labels and HAQM SageMaker from AWS ML Hero Luca Bianchi.

Conclusion

Getting started with HAQM Rekognition Custom Labels is simple. Learn more with the getting started guide and example use cases.

Whether you’re just getting started with ML, already an expert, or something in between, there is always something to learn. Choose from community-created and ML-focused blogs, videos, eLearning guides, and much more from the AWS ML community.

Are you interested in contributing to the community? Apply to the AWS Community Builders program.

The content and opinions in the preceding linked posts are those of the third-party authors and AWS is not responsible for the content or accuracy of those posts.

About the Author

Cameron Peron is Senior Marketing Manager for AWS HAQM Rekognition and the AWS AI/ML community. He evangelizes how AI/ML innovation solves complex challenges facing community, enterprise, and startups alike. Out of the office, he enjoys staying active with kettlebell-sport, spending time with his family and friends, and is an avid fan of Euro-league basketball.