Deep Learning Software
ViDi Suite offers the first ready-to-use Deep Learning based vision software dedicated to industrial image analysis. ViDi Suite is a field-tested, optimized and reliable software solution based on a state-of-the-art set of algorithms in Machine Learning.
ViDi Suite is :
It allows tackling otherwise impossible to program inspection & classification challenges.
This results in a powerful, flexible and straightforward solution for countless challenging machine vision applications. Succesful applications are in the Pharmaceutical, Medtech, Automotive, Textile, Printing, Logistics and Watch Industry.
ViDi Suite comes with three distinct tools.
ViDi blue is used to find and detect single or multiple features within an image. Be it strongly deformed characters on very noisy backgrounds or complex objects in bulk; the blue tool can localize and identify complex features and objects by learning from annotated images.
To train the blue tool, all you need to provide are images where the targeted features are marked.
ViDi red is used to detect anomalies and aesthetic defects. Be it scratches on a decorated surface, incomplete or improper assemblies or even weaving problems in textiles; the red tool can identify all of these and many more problems simply by learning the normal appearance of an object including its significant but tolerable variations. ViDi red is also used to segment specific regions such as defects or other areas of interest. Be it a specific foreign material on a medical fabric or the cutting zone on lace; the red tool can identify all of these regions of interest simply by learning the varying appearance of the targeted zone.
ViDi green is used to classify an object or a complete scene. Be it the identification of products based on their packaging, the classification of welding seams or the separation of acceptable or inacceptable defects; the green tool learns to separate different classes based on a collection of labelled images.
To train the green tool, all you need to provide are images assigned to and labelled in accordance with the different classes.