Image annotation means placing predefined tags on images that help computers “see.” And since people perform this task, it’s also called human annotation. The labeled visuals form training datasets for computer vision (CV) models. So for machine learning image classification and annotation are essential. The effectiveness of ML algorithms will depend on the work quality of data labelers.
And we can help you hire experienced practitioners of picture tagging with expertise in your industry. Our recruiters need only several weeks to build a dedicated image labeling team that meets all your requirements. When specialists perform picture annotation image labeling, or photo classification, processing happens quickly and accurately. That’s due to the advanced software and practical techniques they use.
Are you planning to equip your ML project with experienced image annotators? We offer a fast and cost-effective option — outsource them from Ukraine! We’ll help you find, interview, hire, onboard, and administer your dedicated team. And here’s how our data annotators can help you.
The specialists we hire for you worked for top image and video annotation companies, and they can perform the following services with high quality and speed:
Our teams can handle simple tasks like annotating objects, places, people, shapes, colors, or other visual attributes. Also, professionals perform more complicated assessments and assign predefined tags of feelings, emotions, and similar concepts. This way, you'll structure your image libraries and help customers find your products faster.
The number of tools driven by computer vision (CV) grows. And the accuracy of the AI algorithms behind those instruments depends on the image annotation machine learning models use. The annotators we hire will categorize objects and segment images to create high-quality datasets. This will help your ML models differentiate patterns and generate accurate predictions.
This technique is one of the most straightforward in image annotation AI projects use. Still, 2D box labeling is the basis for software used to monitor plants' health, assess car damages, or identify vehicles on the road. And no matter the scope and size of your box annotation assignments, our labeling practitioners will ensure the consistency, quality, and pace you need.
For CV Artificial Intelligence image annotation of volumetric objects with volume is the next critical task. Marking 3D boxes on images is more complicated not only because data labelers have to add more points and lines. The main challenge is to estimate where to put key points and locate them, and advanced software is the primary assistant to our tag specialists.
This image annotation tool and technique assumes that a labeler accurately identifies particular objects on images. A specialist puts dots and joints them with lines, marking each pixel of the object with irregular shapes. This way, sellers know what lies in your shopping basket, and construction companies see the exact footprints of buildings.
Semantic image segmentation comes into play when you need to categorize every pixel area on an image. And the data labelers you hire with us can separate each object class effectively and quickly. This is due to specific tools and methods which allow them to extract representations, derive correlations, and remove the noise.
Facial and gesture recognition apps and counting applications learn from datasets marked with keypoint and landmark annotation methods. They help computer vision models detect human poses and gestures and understand facial expressions or emotions. Also, this annotation method trains machines to count human traffic in trading centers or supermarkets.
This is a fundamental task of tagging the entire image with one or several labels. Such classification is critical for photo banks since users can find images they need through keywords. However, our image annotation process outsourcing service assumes your raw data gets pre-processed. Specialists remove duplicates and irrelevant images, which can harm prediction accuracy.
Training data datasets with marked lines, polylines, and splines is the heart of autonomous driving. Helping machines see road markings and signs is quite a simple but responsible task. And we can find labelers with extensive expertise in annotating bus stops, crosswalks, pavements, bike lanes, double and dotted lines, and other traffic elements.
Some companies keep a lot of readable information in the image format. And our teams do image transcription to extract those texts using top-notch AI-powered tools. Once you need to categorize data security specific elements or, for instance, add semantic segmentation, our annotation practitioners offer various text annotation services.
Image labeling is highly popular among computer vision projects. And these projects belong to various industries:
Identifying objects with 2D bounding boxes and cuboids and marking lines and splines on images with traffic makes self-driving possible. With humans, machines can learn to drive safely. However, this will be possible with autonomous vehicles due to high-quality image data labeling.
Growing hectares of plants is a task requiring significant investments. And CV technologies can help agriculture companies cut their costs. AI can detect plant diseases and damage caused by pests and find the best time to use agrochemicals and fertilizers.
Computer vision surveillance during production can detect defective details and inform the engineer before it's used. Some companies use pictures to manage their inventories. Large factories can use self-driving loaders that follow floor line-markings.
AI is super effective in detecting dysfunctions on X-ray, computer tomography, and ultrasound images. Well-trained ML algorithms won't miss even minor signs of tumors, tissue degeneration, or abnormal changes in organ sizes. And AI support in healthcare starts with professional picture labeling.
Insurers can use pre-processed pictures of houses and roofs to estimate risks before underwriting. For instance, AI will detect trees growing nearby or non-durable roof materials from aerial photos. Also, CV-driven models can assess car damages, fraud detection, and speed up compensations.
Training datasets for surveillance software help improve our safety. However, to make it agile, Machine Learning models have to process gigabytes of annotated images. This way, AI-powered systems learn to analyze traffic motion, pedestrian movement, object detection, or night vision.
Many companies choose us as their image annotation company because of the benefits we offer:
Salaries are the main expenses businesses suffer when contracting employees. But we help you cut these costs as we hire top data labelers based in Ukraine. Their payrolls are significantly lower than the remuneration of their colleagues from the US and Europe. This is due to the lower living index in Ukraine.
One of the issues with outsourced teams is that they can be based in regions with different time zones. Ukraine is located in the middle of Europe, so it has one hour of difference from Western Europe and overlaps the critical business hours with the US companies.
Our managers thoroughly explore your project needs and requirements for each candidate. This way, we ensure we find highly skilled and easily trained specialists for your assignments. So you get experts in data annotation who will perform effectively. Moreover, we make sure your practitioners are equipped with supreme annotation tool tools.
We understand that there'll never be a one-fits-all option. So feel free to choose the cooperation format that fits your needs and opportunities. Whether it's a part-time or full-time annotator or a dedicated team, just let us know. And, of course, we remain flexible and are ready to add new members to your team anytime.
The first payment you make will be your team members' payrolls and related admin fees. So we do the recruiting at no cost to you. This means we take on job postings, analyze CVs, pre-screen candidates, arrange interviews, and send job offers. These recruiting tasks are time-consuming and require dedication. And we're glad to help you with them.
The cooperation model we use assumes that our clients need to think only of business-related tasks. And our managers deal with all administrative tasks. Crafting job offers, formalizing employment contracts, renting offices, buying equipment, paying salaries, and resolving everyday issues are our risks after we sign a cooperation agreement with you.
Our hiring specialists leverage several factors to build large and small teams of data annotators. They create a pool of talents and ensure these professionals are ready to start working asap. Also, our recruiters have a vast network of channels to advertise vacancies. And finally, they use advanced methods and tools for searching for relevant candidates.
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We want every client to maximize the benefits from our cooperation so that you can choose the format suiting your needs:
This option will work for ongoing projects requiring regular image labeling service. We can advise on the number of specialists and build a dedicated team within weeks. You’ll manage your team’s workload and have data labelers engaged full-time. And annotation experts, in turn, will explore your project and get to work immediately.
Not all ML projects need an entire team of data labelers. If the daily volume of images can be processed by only one specialist, we recommend outsourcing a full-time data annotator. You control this person’s working schedule and pay a fixed monthly fee. In return, you have tasks completed on time and a responsive professional dedicated to your company.
Smaller computer vision projects need the same professional approach to image classification and labeling. And keeping the startups’ limited budgets in mind, we added this cooperation model. It allows businesses to engage one annotator part-time and have all the image datasets be labeled for several hours a day. This format assumes you pay for the worked hours and minimize costs.
