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Computer vision

Algorithms trained with your own images or videos, capable of detecting emotions, facial expressions, objects, shapes, marks, and much more.

People · Faces

Activities

Objects

Animals

Anomalies

Identification

People · Faces

Activities

Objects

Animals

Anomalies

Identification

CNN Technology

Visual Recognition algorithms

Multi-category algorithms that learn as they are used, capable of creating interesting combinations of clothing, detecting expressions in individuals, anomalies in objects, etc. The possibilities are endless.

Average Accuracy assured: 90%

Boost your potential
and beat the market

CSE regularly trains itself on thousands of Google images, high and low quality photos, pixelated images, and objects with swapped elements (eg 3-legged tables). Thus achieving robust and precise starting intelligence.

We develop custom models adapted to each client, although they all start from a common training base: our Clintell Smart Eye (CSE) algorithm.

Boost your potential
and beat the market
  • Significant improvement of the user experience
  • Search speed
  • Trends tracker
  • Image classification
  • Revenue
  • Security

accuracy

Convolutional neural networks

Precision

Our visual recognition algorithms are born from the combination of several artificial intelligence models, ensuring an accuracy greater than 90% in most cases. The final model is made up of convolutional neural networks and RGB/HEX classification models. 

If the elements to identify are complex (for example: scratches on rough walls), the algorithm could be less accurate. However, in the business cases presented, it has always been high.

Validate, categorize,
in seconds.

Get started on your
next-gen project