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AWS Computer Vision Jump Starter Kit

author: dylatong@amazon.com

This repository is a collection of content to help enable engineers and data scientists to succeed on their Computer Vision projects on AWS. The repository currently includes labs for:

  1. Amazon Rekognition Custom Labels: Computer Vision AutoML for Image Classification and Object Detection
  2. Amazon GroundTruth: Managing Machine Learning Annotations at Scale
  3. Amazon SageMaker Built-in Algorithms: A brief follow-up to the GroundTruth Lab for building a SSD Object Detector using the SageMaker built-in Algorithm.
  4. Amazon SageMaker and GluonCV for Object Detection: Training and Deploying YOLOv3 on GluonCV and SageMaker.
  5. Amazon SageMaker and GluonCV for Pose Estimation: Training and Deploying an Inference Pipeline for 2D Human Pose predictions.

Lab guides are shared here.

2020 Overview Presentation is shared here

Related Content:

  • 2019 Image Classification and Object Detection Partner Webinar
  • Image Similarity using PyTorch AWS ML Blog (references to repository)
  • Jetson Nano Smart Cam Repository

Sample Workshop Packages

Examples of modules that you can use together to tailor a workshop for your audience.

I. Object Detection Workshop Package (est. 4 hours)

Below is content you can package up into a Object Detection workshop for SageMaker. You can put together a 4-5 hour agenda with this content.

  1. AWS CV Introduction Presentation

  2. Lab1: Ground Truth:

    • Learn to create and manage a quality data set at scale using SageMaker GroundTruth.
    • Manage annotation workforces: private, public (Mechanical Turk), and 3rd party vendors.
    • Create a labeling job (for Object Detection)

  3. Lab2: SageMaker Algorithms- Object Detection:

    • Learn to build a custom object detection (Single-shot Detection) from the training data you created in Lab1 without having to write code.
    • Learn about hyper-parameter tuning automation.

  4. Lab3: Bring Your Own Script- Object Detection:

    • Learn how to bring your own script from a deep learning framework.
    • In the lab we’ll bring a GluonCV script to train an object detection model (YOLOv3 on mobileNet).
    • Learn how to programmatically launch a hyperparameter tuning job, SageMaker local training as well as perform incremental training.
    • Learn how to deploy a real-time endpoint for inference.

II. Complete AWS Computer Vision Workshop (est. 16 hours)

  1. AWS CV Introduction Presentation
  2. Rekognition Custom Labels Lab
    • Provides a first hands-on experience with Rekognition Custom Labels through a logo detection use case.
    • Learn the iterative process and get a sense of how to extend the learnings to deliver a production-ready deployment.
  3. [Object Detection Series]:

    • Learn the end-to-end experimentation process for CV projects on Amazon SageMaker

    • Learn the different ways to approach CV problems on Amazon SageMaker and acquire an understanding of the pros and cons.

    • Lab1: Ground Truth:

      • Learn to create and manage a quality data set at scale using SageMaker GroundTruth.
      • Manage annotation workforces: private, public (Mechanical Turk), and 3rd party vendors.
      • Create a labeling job (for Object Detection)

    • Lab2: SageMaker Algorithms- Object Detection:

      • Learn to build a custom object detection (Single-shot Detection) from the training data you created in Lab1 without having to write code.
      • Learn about hyper-parameter tuning automation.

    • Lab3: Bring Your Own Script- Object Detection:

      • Learn how to bring your own script from a deep learning framework.
      • In the lab we’ll bring a GluonCV script to train an object detection model (YOLOv3 on mobileNet).
      • Learn how to programmatically launch a hyperparameter tuning job, SageMaker local training as well as perform incremental training.
      • Learn how to deploy a real-time endpoint for inference.

    • Lab4: BYOS and Inference Pipelines- Pose Estimation:

      • Learn how to implement an inference pipeline of multiple Computer Vision models.
      • Learn how to build and deploy a pose estimation model.
  4. AWS CV@Edge online serices
    • This is a online series with hands-on labs to teach you about CV@edge and associated tools available to you on AWS.