Training data for Computer Vision

The human solution to your machine learning project

Finding right annotators for your project

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.

An annotation workflow designed to optimize your production

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.

Computer Vision Expertise

CLASSIFICATION

LANDMARK POSITIONING

BOUNDING BOX

LINES AND POLYLINES

POLYGONS

SEMANTIC SEGMENTATION

3D CUBOIDS

POINT CLOUD

Classification

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.

classification

Landmark positioning

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.

landmark

Bounding Boxes

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.

fashion-annot

Lines and polylines

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.

line-and-polylines-cv

Polygons

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.

polygon

Semantic segmentation

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.

semantic-segmentation-CV2

3D cuboids

Use 3D Cuboids to add 3D information from 2D images. This segmentation technique is useful to estimate the depth of an object.

Cars_3D_cuboid

Point cloud

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.

point-cloud

CLASSIFICATION

Classification

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.

LANDMARK POSITIONING

Landmark positioning

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

Bounding Boxes

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

Lines and polylines

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

Polygons

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.

SEMANTIC SEGMENTATION

Semantic segmentation

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.

3D CUBOIDS

3D cuboids

Use 3D Cuboids to add 3D information from 2D images. This segmentation technique is useful to estimate the depth of an object.

POINT CLOUD

Point cloud

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.

Luxury fashion

Trend monitoring - Retail

Understanding market trends to lead a luxury brand’s decisions on their future collections.

  • Data collection of social media images from key fashion influencers
  • Product recognition to segment and classify them
  • Features analysis to further add metadata to the products
  • Dataset analysis to ensure data homogeneity

Why trust us?

With more than 100 projects, our know-how in production management and quality assurance is based on proven methodologies in the most demanding industries.

Confidentiality

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.

Dedicated teams

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.

Datasets specific

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.

Autonomous management

Rely on a dedicated Ingedata team. Our Know-how relieves you from coordination efforts and ensures team flexibility to adapt to your specific constraints.