For 2/3 of AI projects that do not succeed, lack of quality data is the first cause of failure. Indeed, the training of AI algorithms depends on both the quality and quantity of visual data AI engineers work with. Furthermore, for the created images to be useful, they need to be labelled accurately. From data creation to image labelling, we help you constitute a dataset that will allow your Image Recognition project to kickstart under the best conditions.
Be it through physical recording, crowdsourcing or digital sourcing, we create a pipeline that will generate images adapted to your business environment. Following the constitution of the dataset, our in-house tools allow the automatic labelling of the data using Deep Learning algorithms (YOLOv3, MaskRCNN). A network of partner companies can also contribute to the labelling process if a manual intervention is necessary. A Lead Data Scientist and an Agile Coach work with business-integrated teams to control that the quality of the generated labels suits business objectives.
At the end of a dataset creation mission, you are in possession of: - A batch of images allowing the development of an AI algorithm tailor-made to your business requirements. - Properly labelled images, available in quantities to increase the efficiency of AI algorithms.
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Discover what the main challenges you might face in this critical phase are
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