Data Labeling for ML in 2024: A Comprehensive Guide

What is computer vision?

Computer vision is a field of computer science that deals with how computers can be made to gain a high-level understanding of digital images or videos. Simply put, it's the ability of computers to understand and interpret what they see.

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Computers can use cameras and sensors to recognize objects, comprehend scenes, and make choices using visual information.

What is data labeling?

In ML, if you have labeled data, that means a data labeler has marked up or annotated data to show the target, which is the answer you want your machine learning model to predict. Data labeling can generally refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.

What is data annotation?

Data annotation generally refers to the process of labeling data. Data annotation and data labeling are often used interchangeably, although they can be used differently based on the industry or use case.

Labeled data calls out data features - properties, characteristics, or classifications - that can be analyzed for patterns that help predict the target.

In computer vision retail shelf analysis, a data labeler can use image-by-image labeling tools. These tools help to show where products are located.

They also indicate if products are out of stock. Additionally, they can identify if there are promotional displays. Lastly, they can detect if price tags are incorrect.

Or, in computer vision for satellite image processing, a data labeler can use image-by-image labeling tools to identify and segment solar farms, wind farms, bodies of water, and parking lots.

What is training data in machine learning?

Training data is the enriched data you use to train an ML algorithm.

This is different from test data, which is a sample of the data or a dataset that you can use to evaluate the fit for your training dataset within your ML model.

What are the labels in machine learning?

Labels are what the human in the loop uses to identify and call out features that are present in the data. It&#;s critical to choose informative, discriminating, and independent features to label if you want to develop high-performing algorithms in pattern recognition, classification, and regression. In machine learning, the process of choosing the features you want to label is highly iterative and deeply influenced by your workforce choice.

What is human in the loop?

Human in the loop (HITL) is a way of designing AI systems that integrate humans into the process. This can be done at any stage, from collecting and labeling data to training, evaluating, and deploying the system into production.

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HITL systems are often used for data labeling tasks that machines cannot perform independently, such as detecting objects in images or transcribing audio recordings.

By incorporating human feedback, HITL systems can produce more accurate and reliable labeled datasets, leading to better-performing machine learning models.

What is ground truth data?

Accurately labeled data can provide ground truth for testing and iterating your models.

&#;Ground truth&#; is borrowed from meteorology, which describes on-site confirmation of data reported by a remote sensor, such as a Doppler radar.

In ML and computer vision, ground truth data refers to accurately labeled data that reflects the real-world condition or characteristics of an image or other data point. Researchers can use ground truth data to train and evaluate their AI models.

This ground truth data is used as a standard to test and validate algorithms in image recognition or object detection systems.

From an ML perspective, ground truth data is one of two things:

1. An image that is annotated with the highest quality for use in machine learning. For example, a data labeler annotating an image that shows soup cans on a retail shelf accurately and precisely labels the cans of a brand&#;s soup and those of its competitors. The worker&#;s exact labeling of those features in the data establishes ground truth for that image.
2. An image used for comparison or context to establish ground truth for another image. For example, a data labeler can use a high-resolution panoramic image of a grocery store to inform the labeling for other, lower-resolution images of a display shelf in the same store.

How are companies labeling their data?

Organizations use a combination of software tools and people to label data. In general, you have five options for your data labeling workforce:

1. Employees - They're on your payroll, either full-time or part-time. Their job description includes data labeling. They may be on-site or remote.
2. Contractors - They're temporary or freelance workers (e.g., Upwork).
3. Crowdsourcing - You use a third-party platform to access large numbers of workers at once (e.g., Amazon Mechanical Turk).
4. BPOs - General business process outsourcers (BPOs) have many workers but may lack the expertise or commitment needed for data annotation tasks.
5. Managed teams - You leverage managed teams for vetted, trained data labelers (e.g., CloudFactory).

CloudFactory has been annotating data for over a decade. Over that time, we&#;ve learned how to combine people, process, and technology to optimize data labeling quality.