Succeed at your AI project
Nowadays all is about speed and efficiency — fast search, fast payments, fast commuting. The field of computer vision is catching up with the power of machine learning: take a photo of text to translate it, point at a flower to find out its species, detect a tumor from a single computer tomography scan. Machine learning applied to visual recognition provides fast and reliable processing of images, and successful use cases inspire companies to implement it in their business or build entire products with deep learning algorithms.
Everyone goes AI
The desire to be in trend can end up being extreme, and using artificial intelligence in a workflow or a product, “no matter what”, can come to a point where one of the most impactful technologies is simply misused.
Visual recognition can be an unforgettable experience for users and add value to an end product, but digitalisation is a non-trivial task, and awareness of potential pitfalls is crucial for a successful project accomplishment. One has to understand, which benefits deep learning brings and what questions are going to be solved by this technology. The technology should be seamlessly integrated into the product, so that the user would not even notice its presence.
Accuracy of machine learning algorithms
Accuracy of some machine learning tasks is astonishing — object recognition, quality inspection, classification, outliers detection. However, recognition performance may fail and result in undesirable consequences, which is linked to the data used in the machine learning algorithms, real testing conditions and, what can be very likely, the whole scope of the application.
Imagine having a perfect detector for musical instruments. Point at your dog and get a result of a ”mandolin”. Did the system fail? Actually no, since its main function was to recognise between musical instruments and it was not aimed at anything else. Another example, using fixed light source to take images of objects and then realise that the lightning conditions are not corresponding to production environment thus giving wrong results all the time.
On the other hand, limitations of computer vision can boost research on a particular topic and result in a new technology that would bring better results to the current solution. One can always say: “It can not be done by deep learning.. Yet”, because technology development never stops.
Want to apply deep learning right now?
When considering improving your system with the help of deep learning in computer vision tasks, these are good questions to ask yourself:
- Why are the current solutions not serving our needs? (speed, ease of use, accuracy)
- Which aspect of my process I want to improve with machine learning (reducing manual work, speed up operations, increasing recognition accuracy)
- Do I have the data for training a machine learning model that will cover my use case?
- How reliable the system should be and can I use other methods to support visual recognition?
- Will I need to expand the solution in the near future?
Knowing the answers beforehand will speed up the project planning and bring confidence for its successful completion.
At MoonVision, we are constantly aiming for improvement of existing technology. The MoonVision Toolbox already helps users to perform labelling tasks on images and put life into projects in various fields of application — surface quality inspection, object detection and recognition. We think about the end product and are always excited to bring custom solutions to the market using a small amount of training data, hence reducing the time between project start and production launch.
Check out our MoonVision Toolbox and try the free features we offer!
