Ingedata’s teams of annotators segment and label all types of images. They are continuously trained to produce rigorously reliable data for ever more efficient models.
From raw images, our teams segment and label large training sets requiring a high level of technical expertise. Ingedata’s ability to assemble teams of annotators who are experts in their field, combined with our annotation tools, allows us to efficiently process very specific classes, in all their forms and nuances.
We provide image annotation services for all branches of image recognition technology, including semantic segmentation, cuboids and landmarks. Your images will be annotated by expert teams with detailed knowledge of the classes you wish to segment or label.
One of the leading data annotation solutions, designed for the production of labeled datasets for AI.
Quality training data for your computer vision algorithms, fast. Scale up your productivity with hundreds of image annotation tools for object recognition including bounding box, semantic segmentation, polygon, 3-D point cloud, landmark positioning and image classification.
Our machine learning experts label images from satellite photos to health X-ray analysis.
Classification of images with single or multiple labels. Classification is performed following a list of classes that was previously consolidated with the relevant domain experts to ensure the market fit of your model.
Mostly used for face recognition, posture analysis and eye tracking applications, our landmark positioning techniques follow anatomical rules that ensure that your model is trained with accurate point locations.
Bounding box is one of the most common segmentation techniques to outline the shape of objects by defining its X and Y coordinates. Bounding boxes are fast to draw, but might include some elements of background that lower the detection accuracy of your model.
Lines and polylines are used to segment rectilinear and curvilinear objects such as road markings. Lines and polylines provide a good compromise between segmentation speed and accuracy, but only apply to these specific shapes.
Polygons are a very popular segmentation technique to accurately outline an area of interest and exclude elements of background that would lower the performance of your computer vision model. Polygons are not as accurate as semantic segmentation but are much faster to draw, making them a strong option to train your model with both accurate and high volume annotations.
The higher performance computer vision models are trained with images that were annotated using semantic segmentation. It provides pixel-precise accuracy, as long as segmentation rules to handle blurry and shadowed areas are well defined together.
Use 3D Cuboids to add 3D information from 2D images. This segmentation technique is useful to estimate the depth of an object.
Whether they are installed on autonomous cars, drones or satellites, LiDAR instruments open a whole new world to scene understanding in a 3D environment. Segmentation of point cloud libraries requires custom softwares and can be made more efficient using sensor fusion and clustering techniques.
Classification of images with single or multiple labels. Classification is performed following a list of classes that was previously consolidated with the relevant domain experts to ensure the market fit of your model.
Mostly used for face recognition, posture analysis and eye tracking applications, our landmark positioning techniques follow anatomical rules that ensure that your model is trained with accurate point locations.
Bounding box is one of the most common segmentation techniques to outline the shape of objects by defining its X and Y coordinates. Bounding boxes are fast to draw, but might include some elements of background that lower the detection accuracy of your model.
Lines and polylines are used to segment rectilinear and curvilinear objects such as road markings. Lines and polylines provide a good compromise between segmentation speed and accuracy, but only apply to these specific shapes.
Polygons are a very popular segmentation technique to accurately outline an area of interest and exclude elements of background that would lower the performance of your computer vision model. Polygons are not as accurate as semantic segmentation but are much faster to draw, making them a strong option to train your model with both accurate and high volume annotations.
The higher performance computer vision models are trained with images that were annotated using semantic segmentation. It provides pixel-precise accuracy, as long as segmentation rules to handle blurry and shadowed areas are well defined together.
Use 3D Cuboids to add 3D information from 2D images. This segmentation technique is useful to estimate the depth of an object.
Whether they are installed on autonomous cars, drones or satellites, LiDAR instruments open a whole new world to scene understanding in a 3D environment. Segmentation of point cloud libraries requires custom softwares and can be made more efficient using sensor fusion and clustering techniques.
Understanding market trends to lead a luxury brand’s decisions on their future collections.
With more than 100 projects, our know-how in production management and quality assurance is based on proven methodologies in the most demanding industries.
At Ingedata, your projects are designed and built in-house, from our secure production centers.
Control the confidentiality of your data by always knowing where and to whom you are sending your data.
Ingedata's annotators have a bachelor's degree, an engineering degree or a doctorate in your field.
All our teams work from our production centers and adapt the preparation of the data to your requirements.
We collect, enrich and categorize your data, manage borderline cases to build you own datasets.
Accelerate the optimization of your algorithm using data prepared just for you.
Rely on a dedicated Ingedata team. Our Know-how relieves you from coordination efforts and ensures team flexibility to adapt to your specific constraints.
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(Bôndy - 2024)