Give your applications
the ability to see.

Computer Vision allows a computer system to analyse and interpret images or videos. In particular, it can automatically detect, recognise, count, classify, segment or track visual elements.

Quality control, OCR, document analysis, counting, object detection, video analysis, motion tracking or industrial inspection are among the common cases. The solution depends heavily on the real conditions in which the images will be captured.

Not necessarily. Pre-trained models often make it possible to start with much more limited volumes. The requirement nevertheless depends on the complexity of the problem, the diversity of situations and the level of accuracy sought.

Yes. We can define the collection and annotation strategy, the classes to identify and the quality rules required. A well-built dataset is often as important as the choice of model itself.

It depends on the use case, the data and field conditions: light, angles, resolution, movement or the variability of the objects. We measure performance on independent data before determining whether it is sufficient for the intended use.

Yes, provided the infrastructure and the model meet the latency constraints. Depending on the need, processing can be carried out in the cloud, on a local server or directly on Edge equipment.

Yes, where the available processing power and the model allow it. We can optimise and convert models in order to reduce their consumption and their latency on Edge equipment.

The architecture must take into account the GDPR, the purpose of the processing, the data actually required and, where applicable, the rules applying to biometric data. Anonymisation, local processing and retention limits can be among the measures considered.

The scope depends above all on the dataset, the level of accuracy sought, the hardware, the real-time need and the final integration. A POC phase often makes it possible to check performance before committing to industrialisation.

Depending on the case: automated inspections, defects detected, inspection time saved, volume processed, errors reduced or additional operations made possible. We favour indicators directly linked to the process that the model is meant to improve.