Automate multi-modality, parallel data labeling workflows with Amazon SageMaker Ground Truth and AWS Step Functions

Source by AWS

This is the first in a two-part series on the Amazon SageMaker Ground Truth hierarchical labeling workflow and dashboards. In Part 1, we look at creating multi-step labeling workflows for hierarchical label taxonomies using AWS Step FunctionsIn Part 2 (coming soon), we look at how to build dashboards for analyzing dataset annotations and worker performance metrics on data lakes generated as output from the complex workflows and derive insights.

Data labeling often requires a single data object to include multiple types of annotations, or multi-type, such as 2D boxes (bounding boxes), lines, and segmentation masks, all on a single image. Additionally, to create high-quality machine learning (ML) models using labeled data, you need a way to monitor the quality of the labels. You can do this by creating a workflow in which labeled data is audited and adjusted as needed. This post introduces a solution to address both of these labeling challenges using an automotive dataset, and you can extend this solution for use with any type of dataset.

For our use case, assume you have a large quantity of automotive video data filmed from one or more angles on a moving vehicle (for example, some Multi-Object Tracking (MOT) scenes) and you want to annotate the data using multiple types of annotations. You plan to use this data to train a cruise control, lane-keeping ML algorithm. Given the task at hand, it’s imperative that you use high-quality labels to train the model.

First, you must identify the types of annotations you want to add to your video frames. Some of the most important objects to label for this use case are other vehicles in the frame, road boundaries, and lanes. To do this, you define a hierarchical label taxonomy, which defines the type of labels you want to add to each video, and the order in which you want the labels to be added. The Ground Truth video tracking labeling job supports bounding box, polyline, polygon, and keypoint annotations. In this use case, vehicles are annotated using 2-dimensional boxes, or bounding boxes, and road boundaries and curves are annotated with a series of flexible lines segments, referred to as polylines.

Second, you need to establish a workflow to ensure label quality. To do this, you can create an audit workflow to verify the labels generated by your pipeline are of high enough quality to be useful for model training. In this audit workflow, you can greatly improve label accuracy by building a multi-step review pipeline that allows annotations to be audited, and if necessary, adjusted by a second reviewer who may be a subject matter expert.

Based on the size of the dataset and data objects, you should also consider the time and resources required to create and maintain this pipeline. Ideally, you want this series of labeling jobs to be started automatically, only requiring human operation to specify the input data and workflow.

The solution used in this post uses Ground Truth, AWS CloudFormation, Step Functions, and Amazon DynamoDB to create a series of labeling jobs that run in a parallel and hierarchical fashion. You use a hierarchical label taxonomy to create labeling jobs of different modalities (polylines and bounding boxes), and you add secondary human review steps to improve annotation quality and final results.

For this post, we demonstrate the solution in the context of the automotive space, but you can easily apply this general pipeline to labeling pipelines involving images, videos, text, and more. In addition, we demonstrate a workflow that is extensible, allowing you to reduce the total number of frames that need human review by adding automated quality checks and maintaining data quality at scale. In this use case, we use these checks to find anomalies in MOT time series data like video object tracking annotations.

We walk through a use case in which we generate multiple types of annotations for an automotive scene. Specifically, we run four labeling jobs per input video clip: an initial labeling of vehicles, initial labeling of lanes, and then an adjustment job per initial job with a separate quality assurance workforce.

We demonstrate the various extension points in our Step Function workflow that can allow you to run automated quality assurance checks. This allows for clip filtering between and after jobs have completed, which can result in high-quality annotations for a fraction of the cost.

For more https://aws.amazon.com/blogs/machine-learning/automate-multi-modality-parallel-data-labeling-workflows-with-amazon-sagemaker-ground-truth-and-aws-step-functions/

About the Authors

 Vidya Sagar Ravipati is a Deep Learning Architect at the Amazon ML Solutions Lab, where he leverages his vast experience in large-scale distributed systems and his passion for machine learning to help AWS customers across different industry verticals accelerate their AI and cloud adoption. Previously, he was a Machine Learning Engineer in Connectivity Services at Amazon who helped to build personalization and predictive maintenance platforms.

Jeremy Feltracco is a Software Development Engineer with the Amazon ML Solutions Lab at Amazon Web Services. He uses his background in computer vision, robotics, and machine learning to help AWS customers accelerate their AI adoption.

Jae Sung Jang is a Software Development Engineer. His passion lies with automating manual process using AI Solutions and Orchestration technologies to ensure business execution.

Talia Chopra is a Technical Writer in AWS specializing in machine learning and artificial intelligence. She works with multiple teams in AWS to create technical documentation and tutorials for customers using Amazon SageMaker, MxNet, and AutoGluon.

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