Our philosophy at Label Your Data is simple: quality image annotation services lead to top-performing models. Ready to transform your visual data?contact us
Arguably the most common image annotation method, bounding boxes (or 2D boxes) annotation draws a frame around the target object, allowing its identification and tracking in the images. Our clients often require bounding boxes for the classification of objects in an image.
A polygon is an outline drawn around the object during the annotation process. It allows the machine to see the shape of the object and learn to recognize similar objects in the images. Polygon annotation is also great at classifying different objects in the annotated images.
When you need the machine to identify multiple similar objects in an image, semantic segmentation helps to draw a map that relates each pixel of the image to a certain class of objects. Instance segmentation is used for clearer identification of objects and their classification. These types of image markup are widely used for training self-driving cars.
Also known as landmark image annotation, key points mark the meaningful points on the natural object to define their shape. Key point annotation is useful in facial and emotional recognition, identification of human poses, and so on.
Certain tasks require the zoning of the images into meaningful areas. Polylines tell the computer where the lines of the division are. They can also be referred to as lines splines or line image annotation and usually used to draw the road lanes for autonomous vehicles.
A level-up twist on 2D boxes is 3D cuboids, which is a three-dimensional box around the object used to add depth to the training images for AI and tell the computer how the object is located in space.
Optimize your visual data with our secure and customized image annotation techniques. We have several compelling benefits to offer:
Experience the pinnacle of image annotation outsourcing quality with Label Your Data. Our team delivers high-precision, secure annotations while granting full client access to our tool, ensuring total quality control and transparency every step of the way.
At Label Your Data, we offer a wide range of output formats to choose from, including custom options designed specifically to fit your requirements. While we do provide standard formats like JSON, XML, and masks, our tech team is ready to support in creating custom output files for the client.
Efficient data exchange is part of a great image labeling service. We provide seamless integration options such as API, S3, and other cloud storage solutions to ensure smooth data transfers. Our team is well-versed in integrating with various cloud storage providers to ensure top-tier picture annotation.
Label Your Data is an image annotation services provider that employs a meticulous methodology to ensure a thorough labeling process:
Data collection usually happens on the client’s side. But if you don’t supply any data, our team performs data collection at your request. You determine the type of data to gather, the volume, and the method for acquiring it.
At this stage, we coordinate with you the key project details. Together, we decide on the process, policies, data labeling criteria, and annotation tools to create a complete image dataset.
As we receive the first batch of data, our annotators run a small annotation sample to verify all the edge cases with you. A free pilot helps you decide whether our image annotation service can satisfy your demands.
Once the pilot is done and the results are satisfactory, we proceed to full-scale annotation by assigning a dedicated team to the project. On request, we can set up on-site teams and provide the option of working in the office. We perform image annotation in batches, allowing you to track progress.
Before sending the completed annotations, we ensure their quality and validity. To ensure the number of mistakes is negligible, Label Your Data delivers a thorough QA
Our 10+ years of experience in building remote teams allows us to expertly navigate 500+ data annotators and provide high-quality image annotation services in 55 languages. If you choose our image annotation outsourcing services, you choose the winning mix of quality, speed, and security of your video data.
Training human experts to know the most important points of human faces
Key points and bounding boxes
A B2B enterprise in IT was developing ML-based technology able to provide predictions of consumer behavior based on emotional analysis. The Label Your Data team was assigned to annotate 5,000 facial images by placing key points and drawing 2D boxes on significant areas of human faces to train the algorithm for recognizing emotions. Our annotators faced a challenging task of combining sculpture and human biology training to accurately identify and mark essential facial features, ensuring precision in the image annotation process.
Large volume of documents and data specificity level
Optical Character Recognition
The Client developing a document processing platform came to Label Your Data with a unique image annotation task: to process invoices and other financial documents using OCR technology. Our team consisted of 10 skilled annotators who were labeling certain entities such as merchant, tax IDs, dates, amounts, etc. They also transcribed handwritten text if they came across it. The workforce allocation for this client was fixed, and they have been working with us for an extended period.
Image annotation of different products on the weights
A tech manufacturing company turned to Label Your Data with an interesting image annotation task. We were asked to annotate 100,000 images of food products with extra precision and high-quality. To tackle the task, our team used polygonal annotation to indicate the products and data tagging to classify the images, as well as a meticulous annotation QA. This was one of our top image annotation performances with the combination of high quality, limited deadline, and top-notch data protection.
An image annotator’s primary task is to analyze the content of images and add relevant tags such as object boundaries, semantic labels, and other descriptive attributes, enabling ML algorithms to accurately detect objects within images.
Picture annotation can be challenging due to factors such as the need for large amounts of high-quality annotated data, the complexity of images, and the subjectivity involved in labeling. These are the factors that an image annotation company usually deals with.
Annotated images are used in various sectors such as autonomous vehicles, medical image analysis, and facial recognition, to improve the accuracy of computer vision systems.