Let our experienced annotators help you create datasets for neural network training.
Bounding-box is one of the simplest and most common methods for image/videos annotation used for training computer vision systems. Labeling objects by rectangles in the images, you can train models to classify and localize objects using the right amount of data and accurate datasets.
Our experts will help you create a dataset by accurately detecting the necessary objects with bounding-box annotation.
Similar to bounding-box (2D-annotation), cuboid (3D) annotation using for three-dimensional objects annotation calculating the length, width, and approximate depth of target objects such as cars, buildings, or even people to get their total volume.
Cuboid annotation commonly used in construction, Autonomous transportation systems, augmented reality models, and other computer vision solutions.
Our experts will help you create a dataset by accurately detecting the necessary objects with cuboid annotation.
Polygon annotation is an ideal solution when working with irregular shapes objects. Unlike bounding-box annotation, which can capture many unnecessary objects and noise around the target, polygon-segmentation is more accurate when it comes to localization and reduce confusion when training your computer vision models.
Our experts will help you create a dataset by accurately detecting the necessary objects with polygon annotation.
Classification is the process of classifying your products into categories, usually used as an initial step when building computer vision models. For any computer vision model, assigning descriptive attributes to each image can provide invaluable context and search ability.
Semantic segmentation, or pixel-level labeling, using to label each pixel in an image into segments that you need to recognize using a computer vision algorithm. Unlike polygon annotation, which is used specifically to detect a specific object of interest, full semantic segmentation provides a complete understanding of each pixel of the scene in the image and can be ideal when the amount of source data is insufficient.
Our experts will help you create a dataset by accurately detecting the necessary objects with semantic segmentation.
Key-point annotation is used to recognize faces or skeletal features of the human body to provide motion-based algorithms for tracking human movements. We provide services for identifying key points of objects, such as faces, cars, and other patterns that match the task.
Our experts will help you create a dataset by accurately detecting the necessary objects with key-point annotation
Line annotation helps to detect all types of lanes on different roads, including city streets or highways for driverless vehicles. Line annotation separates different objects from each other on images.
Our experts will help you create a dataset by accurately detecting the necessary objects with line annotation.
With video annotation, we mark and track objects through frames in a sequence of images using bounding-box and up semantic segmentation. Video annotation is used to train algorithms for various tasks, from simple classification to tracking objects in multiple frames.
Our experts will help you create a dataset by accurately detecting the provided video materials.
Our annotators provide best-in-class image detection and classification accuracy.
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Personal manager will control all processes-from setting a task to getting the final result.
We will increase or decrease the size of the team according to your needs.
You will be able to monitor the progress of the project in real time.