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
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%
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.
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.
in seconds.
- Visual identification startup
- Search for similar items on your website (eg clothing)
- Distinction of animals and actions (ex: dog running)
- Unlock / identity authentication (ex: phone)
- Search for locations on the map (eg monuments)
- Identification of dangerous or prohibited objects
- Identification of deformities or anomalies in objects / shapes
Get started on your
next-gen project