Lowering the barrier to entry


Imaging firm IDS has developed an online deep learning platform where image data can be uploaded, managed and labelled. Named IDS NXT Lighthouse, the solution is designed to lower the barrier of entry into deep learning by working with IDS’ Rio and Rome cameras and requiring no programming skills or an IT infrastructure, besides a PC, to operate. The software, which


is supported by Amazon Web Services, is included in IDS’ NXT Ocean package, which, the firm says, includes everything required to set up an inference camera for an application. Ideally, training data is acquired in an application-related scenario by the same camera onto which the deep learning solution will be deployed. This helps to build a representative


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with the predicted category. Also, MIL SKD includes its Copilot tool, which can be used to explore and prototype machine vision recipes, as well as generate functional program code. It now has the functionality needed to prepare for deep learning training. Lina said: ‘Te overall system must allow


quick review of the source (data quality, augmented data and labelling) and the result of the training process to determine the corrective actions to undertake to improve the accuracy of the deep learning solution.’ Neural networks require large amounts of


images in order to perform well, potentially in the tens or hundreds of thousands of images. Pretrained models, such as those included in MVTec’s deep learning tool, can bring this down to a few hundred images. ‘With our algorithms and pretrained


networks we try to bring the number of images required for an acceptable result as low as possible,’ said Eckstein. ‘For the images that need to be labelled, we want to make the completely integrated workflow as smooth, transparent and efficient as possible.’ For a successful deep learning


application, the whole data pipeline must be managed. Tis includes image acquisition, integration into the correct project and datasets, labelling, training, retraining and model management. ‘Most labelling solutions only support


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dataset. Oliver Senghaas, head of marketing at IDS, said: ‘For a simple good/bad decision a few tens or hundreds of images will suffice to train a first model. With the knowledge gained from frequent real-world evaluations, the dataset can be expanded step by step to meet the scenario’s requirements. Our goal is to make this improvement as quick and easy as possible.’


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a subset of these aspects,’ noted Eckstein. ‘Tis is especially important, since deep learning data pipelines are not a linear but an iterative process, where data is continually collected, and the neural networks are improving over time.’ Before labelling, the key is to define


the label classes well. Defect classes in industrial applications, for example, tend to be extremely hard to differentiate to non-experts – and even they may disagree in many cases. ‘Te [labelling] tool should support this


process, for example by displaying reference images to labellers,’ said Eckstein. ‘We have seen projects fail because of a small number of label errors in the dataset. Te labelling tool should help find and remove those errors easily.’ With its deep learning tool, MVTec targets


the improvement of efficiency when data labelling. ‘We measure the amount of time and user actions required for labelling specific test cases, and try to improve the workflow to shave off as many seconds as we can,’ Eckstein explained. ‘Another method is so-called


bootstrapping, where the model is first trained on part of the dataset in order to make label suggestions. Tis can help about halfway through the process.’ For semantic segmentation, the network


has to assign individual pixels to classes. Here, labelling is especially costly, according to Eckstein. ‘For this method, our research


department has had promising results for segmentation methods that should cut down the labelling time by several factors,’ he said. ‘Since labelling effort has been holding this technology back, we hope that these new methods will facilitate a breakthrough in industrial applications.’ O


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