Looking for an AWS DeepLens project to get started with? Here’s a fascinating one — using the Amazon SageMaker built-in object detection algorithm and AWS DeepLens to identify bird species at the edge.

This project demonstrates how to train a custom object detection model using Amazon SageMaker’s built-in algorithm on a dataset of bird images, and then deploy that model to an AWS DeepLens device for real-time inference. The DeepLens camera captures video, and the model running on the device identifies and classifies bird species directly on the edge — no need to send frames to the cloud for processing.

The approach uses transfer learning with a pre-trained model, fine-tuned on bird species data to achieve accurate detection. Once trained in SageMaker, the model is optimized and deployed to DeepLens, where it runs locally with low latency. It’s a great example of how edge ML can be applied to real-world use cases like wildlife monitoring and citizen science.

Read the full tutorial on AWS

Originally posted on LinkedIn