![]() ![]() Some of the drawbacks of existent solutions are that they usually require the user to buy the image deduplication software or pay monthly for a cloud solution, they are big in size or are hard to install and use.ĭespite all of these options, especially in the case of scraping the images from the internet, once stored they can still be unorganized or of a lower quality than expected, with images needed to be sorted out each in their respective class folder in order for the user (e.g. Finding duplicate images manually can be very hard for a human user and a time-consuming process, this being the reason why a software solution to execute such a task is crucial. It is recommended that before training a DL classification model, one should always check and make sure that there are no duplicate images found in the dataset. The importance of image deduplication can be seen in the fields of Computer Vision and DL where a high number of duplicates can create biases in the evaluation of a DL model, such as in the case of CIFAR-10 and CIFAR-100 datasets. when the user takes all the photos and labels them himself), which can be impossible most of the time because of a low-budget, a low-quality camera or time constraints. In general, data can be acquired either by a) buying it from marketplaces or companies such as Quandl and URSA b) searching it for free on platforms like Kaggle c) crawling it from internet resources with the help of search engine crawlers d) paying to a 24 × 7 workforce on Amazon Mechanical Turk like the creators of the ImageNet dataset did to have all of their images labeled e) creating it manually for free (e.g. Another bottleneck is that, because the amount of data needed to train a DL model is usually required to be very large in size and because most of this important data is not released to the general public but is instead proprietary, the need of an original dataset for a particular DL project can be very crucial. Regarding Computer Vision applications for image classification tasks, a major bottleneck before training the necessary DL models is considered to be the data collection which consists mainly of data acquisition, data labeling and improvement of the existing data in order to train very accurate DL models. ![]() ![]() Additionally, also new career positions were created recently such as Machine Learning Engineer and Data Scientist, being some of the top paid positions in the industry. training accurate models for real-life scenarios, in recent years, new specializations were introduced in Universities around the world such as Machine Learning and Data Science, to name only a few. Because the Machine Learning lifecycle consists of four stages such as data management, model learning, model verification and model deployment, in order to collect, analyze, interpret and make use of this data, e.g. Data is at the core of every DL application. ![]()
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