headaches in past deep learning projects. ‘When you start working with deep


learning you end up with dozens, if not hundreds, of datasets from different projects, and then multiple versions of each of these datasets,’ said Michał Czardybon, CEO of software firm Adaptive Vision. ‘Each of these has to be annotated when creating a deep learning model. ‘What makes things even more


complicated is that when you have multiple people working on a number of different projects, it can be very challenging to keep everything in order and organised. ‘In addition, as the projects progress,


customers continue to send in more batches of sample data, while also changing parameters between batches, such as lighting, lenses and location. ‘Tis creates an increasing amount of


chaos and complexity when trying to keep track of everything.’ According to a study by Forbes in 2016,


data scientists spend about 80 per cent of time on data management tasks, contrasted with four per cent on refining their algorithms. What’s more, the majority of the scientists reported data management to be the least enjoyable part of their work. Tankfully, in recent years a number of


software tools for efficient data management and labelling have emerged. Such tools enable time and effort to be saved during the management and labelling of training data for deep learning, which can ultimately lead to a lower cost of the resulting solution.


Online data management For Adaptive Vision, the future of data management and labelling for deep learning projects is online. Czardybon explained that traditionally


many machine vision customers are accustomed to having all of their images on a disk drive, which are then shared within the company using a local shared computer with a big enough disk. ‘Tis can lead to issues with data


management,’ he said. ‘Datasets end up being exchanged in an unco-ordinated way, which leads to confusion as to where the most up-to-date version of each dataset actually is, or which models are trained with which datasets.’ While a number of offline tools have


emerged for data labelling for machine vision, Czardybon remarked that these are more suited to deep learning projects being developed by a single person controlling everything. For team-based projects, however, there is a need for more professional dataset sharing and co- ordinated image annotations. ‘One data labelling tool that has emerged for the general market in recent years


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is Labelbox, which is owned by several investment funds, including one from Google,’ he said. ‘Tere has been quite a bit of buzz around this tool as the whole process of data labelling has been moved online. We see this as a very disruptive feature, especially for a highly conservative industrial market such as machine vision.’ Adaptive Vision therefore bought the online data annotation platform Zillin in January, and has since invested heavily into its further development. ‘While Labelbox covers a wide range of


general features for deep learning, our focus with Zillin is solely on machine vision,’ said Czardybon. ‘We have therefore focused on making it a very intuitive, easy-to-use tool for machine vision professionals for organising and annotating images.’


‘Data is the oil of the 21st century. Like crude oil it must be analysed, filtered and processed before it can be used properly’


He explained that by moving data


management and annotation online, the convenience of working with images for deep learning projects can be improved dramatically. Rather than transferring images to and


from the customer via physical storage devices such as USB drives (as used to be the norm for Adaptive Vision) with Zillin, image folders can be dragged and dropped into a common online workspace. A number of team members with varying levels of editing permissions can then be added to this space. ‘Tis helps us organise everything to a much higher standard,’ said Czardybon. In addition, with online platforms such


as Zillin, not only is the transfer of data between a firm and its customers now much simpler, but customers can continually be engaged throughout the training of the deep learning model. ‘Te customer can see what you are


annotating and even participate in this process,’ Czardybon confirmed. ‘Tis is a very important feature as annotations must be discussed. In fact, 95 per cent of projects that we’ve had ourselves in recent years have required discussion – “is this a defect or is it not?”. With Zillin you can call the customer and open and look at the same image, allowing you to confer with them, while annotating the data.’ Moving data online not only provides


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