Image or video annotation typically involves human-powered work defined as the task of annotating an image or video with labels. What and how images or videos need to be annotated, depends on main model training reasons like classification, object detection, or semantic segmentation.
The best set of methods, classes, and attributes is defined by the customer and our engineer who is an expert in this field.
Bounding boxes help algorithms to find what they're looking for in an image and associate the detected object with what they were initially trained for.
Segmentation annotation adds much more details to training imagery by separately labeling objects belonging to the same class.
We can classify images and objects within images based on taxonomy, including features of residential real estate, land, type of buildings, or, for example, aerial maps.
Specialists of image classification transform image data into detailed content information required by AI and ML models.
Polygon annotation provides more details into object annotation than rectangular bounding boxes and allows for better fit to its shape.
For specific use cases, such as facial recognition or emotion detection, keypoint annotation will be the most suitable annotation technique.
When data and models start working for you, you should control how they perform. At the moment, there is no better method than manual data validation and control.
As part of the cooperation, we can undertake this task and help you, to free your team from spending hours on the manual checks providing reliable ground truth data.
Annotation expert training – entry level training for specialists who we are teaching the foundations of how to become an expert data annotator.
High performance data annotation team – dedicated for managers, who we introduce in team structure, roles, responsibilities, performance indicators and reporting.
Domain expert – we share our knowledge how to seamlessly use CRISP-DM methodology in Data Science projects.
Outsourcing is a great way to help your business as it progresses through its various growth stages, especially when talking about projects, which need to be fed with large datasets, there is always manual work to be done, like data cleansing, annotation, or augmentation.
It’s more cost-effective to outsource the work than it is to build your own in-house team in a relatively short period of time or engage highly paid engineers in annotations.
You need expertise in this field and we can provide specialists. Learn from us how to avoid failures and keep your AI/ML projects on track.
We are flexible, after you finish this heavily engaging stage of your project, we can easily scale down the annotation team to your current needs waiting for new challenges.
What sets us apart is our commitment to unparalleled service quality with high communication, performance, credibility standards. Operational excellence is our goal. We ensure that every interaction with our team is a seamless and satisfying experience.Team of seasoned experts goes above and beyond to deliver high quality solutions tailored to your unique needs.
We are committed to providing you with reliable and secure annotation services following safety good practices and procedures. Once your project is completed, we go the extra mile to ensure complete data confidentiality. We have a strict policy in place where we permanently delete all data related to the project, leaving no trace behind. Your privacy is our top priority.
Our task is not to impose separate processes on our clients, but to fit as much as possible into the existing process, part of which is data preparation, annotation, and finally transfer them for implementation in the client’s environment.
We do have all the needed tools and knowledge to support our customers, and as part of the cooperation, we are happy to share our experiences and support our clients in implementing two basic frameworks that we use during the implementation of our projects: CRISP-DM and Data-Driven Scrum.
In general, the most important thing for us is to understand the client’s needs and determine the best strategy for data annotation, for this purpose our experts try to find answers to the following questions:
Understanding data
Customer consultation
Data preparation
Project setup
Training for annotation team
Tasks assignment
Annotation of test data sample
Final adjustments
Annotation process set-up, Quality check
Delivery of data to the customer in iterations and customer feedback collection
Outsourcing is a great way to help your business as it progresses through its various growth stages, especially when talking about projects, which need to be fed with large datasets, there is always manual work to be done, like data cleansing, annotation, or augmentation.
A data annotation guru with extensive knowledge gained from a variety of AI/ML projects, will assist with data labeling strategies and develop a detailed performance evaluation plan.
A master of ceremonies who will prepare the right team for you, take care of tasks and communication with your team on a daily basis. He won't leave you without answers to your questions and will help remove any obstacles to achieving your project goal.
This is where the daily battle with the efficiency and quality of annotating your data takes place, these are the people who are trained by us and well prepared to deal with your data.
Two but crucial tasks for success - control of the labeled data and validation of the effectiveness of the models trained on the datasets prepared by our specialists. These people are as precise as sappers disarming bombs, they don't accept mistakes!
Computer vision, a rapidly growing technology, continually discovers new applications across various industries. It offers automation, cost-saving, and the potential to enhance products and processes,
Computer vision algorithms are fed with visual data with additional layers of information typically provided by human annotators. This layer holds information on the content
AI image generators Image generators are large neural networks created and trained to generate realistic images with desired features. These features can be given in
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