DEEP LEARNING g
enables a history to be kept of the datasets that have been used to train a deep learning model, in addition to what has changed within these datasets. ‘Our market is adjusted to engineers who
create a system and then deploy it. However, with deep learning we believe there will instead be an ongoing process of retraining the system whenever something changes,’ said Czardybon. ‘For that you will need to keep track of what datasets have already been used to train your deep learning model. Online is the perfect place to do this.’ Adaptive Vision is currently in the
process of introducing end-to-end encryption to Zillin, which has proven to be a big requirement for many customers. ‘Maintaining the confidentiality of data has been the number-one issue our customers have had,’ said Czardybon. ‘Tis end-to-end encryption is still in early development. We hope for this to be a killer feature.’
Lessening the labour of labelling Te quality of a deep learning solution stands or falls with the quality of the data used to train it. While deep learning algorithms are capable of describing scenarios of unprecedented complexity, it is vital for the training data to represent the full variety of the application scenario. More complex scenarios require more complex and larger amounts of training data, which itself requires a greater effort to label. ‘Data is the oil of the 21st century. Like
crude oil data must be analysed, filtered and processed very carefully before it can be used properly – otherwise it won’t work well and a certain loss in performance must be expected,’ said Christian Eckstein, product manager for MVTec Software’s deep learning tool. ‘Lastly, it’s always important to keep in mind that a deep learning network can only learn what it sees. Wrongly labelled or unlabelled data can significantly degrade the classification or detection results.’ Te amount of data labelling required
depends on the type of deep learning method used: supervised or unsupervised learning. Supervised methods require a relatively large amount of data labelling in order to teach the network, while unsupervised methods require little to no labelling. In terms of applications, anomaly
detection, for example, will only require good samples for training and only a few for testing – since it only evaluates the differences of an image relative to the average of the training images. Most other applications, however, such as
classification, object detection and semantic segmentation, will require large amounts of data to be labelled and managed.
Adaptive Vision’s online platform Zillin simplifies the management and labelling of training data for deep learning
Zillin enables multiple users to co-ordinate when labelling images for training a deep learning mode ‘Supervised learning, i.e. learning from
data labelled by a subject matter expert, will typically produce by far the best results in terms of accuracy,’ explained Arnaud Lina, director of research and innovation at Matrox Imaging. He advised that, to train, the source data
needs to be split into different sets, and only the training dataset can have augmented data, since only authentic data can be used to evaluate the training process and validate the trained deep neural network. If the data augmentation strategy is not satisfactory, the augmented data can be deleted and newly generated. Ideally, for the best training, Lina said that
the data must be balanced. Each category must have the same amount of training data. However, one problem with using neural networks for industrial inspection is that manufacturing processes do not
12 IMAGING AND MACHINE VISION EUROPE OCTOBER/NOVEMBER 2020
produce a lot of bad widgets, which makes the collection of such image data time consuming. Other challenges Lina noted include
data labeling errors, whether due to an error during the data collection or because of ambiguous data. Biased data is also a problem, such as gathering good data under certain illumination conditions and bad data under different illumination, or performing data augmentation in a way that unknowingly shifts the classification problem. Tese are pitfalls to be avoided. In the Matrox Imaging Library SDK, a
dataset API is provided to simplify data manipulation – a sort of lean database specifically for deep learning. Te API allows the user to retrieve all unlabelled images, as well as images associated with a specific category, augmented images, etc, and easily compare the ground truth
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