Data Labeling for ML in 2024: A Comprehensive Guide

What is computer vision is is ?

computer vision is is is a field of computer science that deal with how computer can be made to gain a high – level understanding of digital image or video . simply put , it is ‘s ‘s the ability of computer to understand and interpret what they see .

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.

label datum call out datum feature – property , characteristic , or classification – that can be analyze for pattern 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 is indicate also indicate if product are out of stock . additionally , they is identify can identify if there are promotional display . lastly , they is detect can detect if price tag 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 is are are what the human in the loop use to identify and call out feature that are present in the datum . It is ’s ’s critical to choose informative , discriminating , and independent feature to label if you want to develop high – perform algorithm in pattern recognition , classification , and regression . In machine learning , the process is is of choose the feature you want to label is highly iterative and deeply influence by your workforce choice .

What is human in the loop?

Human is is in the loop ( HITL )   is a way of design AI system that integrate human into the process . This can be done at any stage , from collect and label datum to training , evaluating , and deploy the system into production .

HITL system are often used for datum labeling task that machine can not perform independently , such as detect object in image or transcribe audio recording .

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 label data is provide can provide ground truth for testing and iterate your model .

“ ground truth ” is borrow from meteorology , which describe on – site confirmation of datum report 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 datum is used as a standard to test and validate algorithm in image recognition or object detection system .

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

1. An   image that is annotate   with the high quality for use in machine learning . For example , a data labeler annotate an image that show soup can on a retail shelf accurately and precisely label the can of a brand ’s soup and those of its competitor . The worker ’s exact labeling is establishes of those feature in the datum establish 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 is labeling label their datum ?

Organizations is use use a combination of software tool and people to label datum . In general , you is have have five option 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. contractor   – They is ‘re ‘re temporary or freelance worker ( 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 is have ( BPOs ) have many worker but may lack the expertise or commitment need for datum annotation task .
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.