Data labeling is an essential process in machine learning and artificial intelligence that involves the tagging, classification, and annotation of data. However, manual data labeling can be time-consuming and expensive, which has led to the development of automated data labeling platforms. These platforms use various techniques such as computer vision

Many artificial intelligence (AI) products and services that we employ on a daily basis are built around image annotation. Additionally, it is among the most significant activities in the process of creating training data for Computer Vision (CV) technologies. Annotators utilize tags or metadata to identify the features of data

A machine learning project's quality ultimately depends on how you handle three crucial aspects: data collection, data preparation, and data labeling work. Labeling, commonly referred to as data annotation, is frequently labor-intensive and complicated. For instance, bounding boxes surrounding certain objects are frequently needed for picture identification systems,

Computer engineers are becoming increasingly interested in artificial intelligence (AI) and machine learning (ML), which they can use to expand the use of this cutting-edge technology into untapped industries or enhance the effectiveness and performance of the current fields. The availability of training data for machine learning is a fundamental

Data are necessary for machine learning models. Even the most efficient algorithms might become useless without a base of high-quality training data. In fact, when they are initially trained on insufficient, erroneous, or irrelevant data, strong machine learning algorithms can be severely hindered. A well-known adage still holds brutally when

The images that are used to develop, verify, and evaluate any algorithms for computer vision will have a big impact on how well your AI project turns out. To teach an AI algorithm to recognize items similarly to how a person can, every image in your collection must be carefully

There are two companies which are independently working on creating a computer vision model for representation in a competition. They have limited time to complete the projects. Company A created their computer vision model, they acquired the data, took help from a data training platform and then, moved on to

Data that has been artificially annotated is known as synthetic data. It is produced via simulations or computer algorithms. When genuine data is unavailable or needs to be kept hidden due to compliance risks or personally identifiable information (PII), synthetic data production is typically used. The healthcare, manufacturing, agricultural, and

Continuing to our last blog https://www.labellerr.com/blog/these-10-datasets-you-wont-find-on-kaggle-part1/ Are you someone who deals with datasets every new day and are always looking for a new variety of datasets? If you are a data enthusiast, then CVPR ( IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR)) is something

Are you someone who deals with datasets every new day and are always looking for a new variety of datasets? If you are a data enthusiast, then CVPR (IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR)) is something that is extremely important for you. Recently, they have published a

For today's enterprises, data is the ultimate kind of wealth. The proper use of any data has been shaping society favorably as technology like artificial intelligence gradually takes over most of our daily activities. Effective data separation and labeling allow machine learning (ML) algorithms to identify problems and

Image annotation is the most time consuming task in object detection model development journey. Although the following best practices are generally accurate, it's vital to keep in mind that image labeling guidelines heavily depend on the task in hand. Additionally, images labeled for one activity might not be