You work for a scientific research company where you need to gather data on tree specimens.
You have scientist peers who go out in the field across the globe and photograph tree species.
The images that they gather need to be classified and labeled to use them in your training datasets in your machine learning models.
What is the best way to label your image data most accurately and in the most cost-efficient manner?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: D.
Option A is correct.
Human labelers may be able to label all of your images correctly.
But they will be slow and expensive.
Option B is incorrect.
While the Amazon Rekognition service analyzes image data, it does not have the human labeler to active learning model loop that trains an automatic labeling model that Amazon SageMaker Ground Truth has.
Therefore, a labeling process based on Rekognition will be more costly and less accurate than a process based on Amazon SageMaker Ground Truth.
(See the Amazon Rekognition overview and the Amazon SageMaker Ground Truth overview)
Option C is incorrect.
An open-source image labeling solution may label some images automatically, and a human labeling team that you hire can label the ones the open-source software cannot label.
This process lacks the human labeler to active learning model loop that trains an automatic labeling model that Amazon SageMaker Ground Truth has.
Therefore, a labeling process based on an open-source image labeling solution will be less accurate than a process based on Amazon SageMaker Ground Truth.
Option D is correct.
As documented in the Amazon SageMaker Ground Truth overview: “Amazon SageMaker Ground Truth uses a process that starts with an active learning model that is trained from human-labeled data.
Any image that it understands is automatically labeled.
Ambiguous data is sent to human labelers for annotation.
Then the human-labeled images are sent back to the active learning model to retrain the model to improve its accuracy incrementally.
(See the Amazon SageMaker Ground Truth service overview)
Reference:
See the Amazon SageMaker Ground Truth service overview) and the Amazon Rekognition overview.
The best way to label image data for scientific research purposes would be option D, which involves using AWS SageMaker Ground Truth to automatically label images, and using AWS Ground Truth human labelers to label images that cannot be automatically labeled.
AWS SageMaker Ground Truth is an AWS service that helps users create high-quality training datasets for machine learning by labeling data. It combines automatic labeling with human review to ensure that the labeled data is of high quality. AWS Ground Truth human labelers are trained and skilled in labeling data and can help ensure that the data is accurate and consistent.
Using an automated labeling tool like AWS SageMaker Ground Truth can significantly reduce the time and cost of labeling large datasets. Automatic labeling is usually faster than manual labeling, and it eliminates the risk of human error. However, automated labeling is not always accurate and can make mistakes, especially for complex or ambiguous images. In such cases, human review can be used to correct the errors and ensure that the data is labeled correctly.
Therefore, option D is the best solution as it leverages the strengths of both automatic labeling and human review to produce high-quality labeled data. It also ensures that the labeling is done in a cost-efficient and timely manner, while still maintaining high levels of accuracy.