Trained dataset is crucial for AI
Updated: Oct 21, 2020
The AI revolution has been accompanying and accelerating many markets over the past decade. This revolution relies on neural networks for which, datasets are the primary building block. Essentially, training dataset is the part of the data which data scientists use to help the machine learning model make predictions. Their models run on these sets of data exhaustively, churning out results which the data scientists can use to develop the algorithm. Accordingly, everyone who develops computer vision algorithms must have datasets that will enable the training of the neural networks that are the heart of nowadays applications. However it’s not that simple to get the right datasets so according to Dataconomy Media GmbH (leading data-driven technology) 76% of the business that develops AI attempt to label and annotate training data on their own while 63% went so far as to try to build their own labeling and annotation automation technology.
In due course, the quality and quantity of the training datasets dictates the maximum capability of todays’ computer vision algorithms. Therefore, inferior datasets will unquestionably result in poorer network, and subsequently the algorithms that rely on it will not be able to perform their duties. In other words, when the developers train a computer vision system with incomplete data sets it may give disastrous results in certain AI-enabled models like autonomous vehicle driving.
How ProjAIX helps Data scientist on their daily work
During the last 5 years, Data scientists’ numbers increased intensely in many industries including: Automotive, Agriculture, Banking & Financial Services, FMCG, Energy, Education and Healthcare. At work, they all focus on productivity and in overall, improving the world for us all - using artificial intelligence. At the opening of each workday, once such a data scientist turns on his workstation, its foremost goal is to improve the value of the computer vision algorithms. Obviously, the data scientist is also aware to the fact that superiority and quantity of its available training datasets will eventually determine the algorithms performance. Until a few years ago, most businesses would have produced their own datasets, and some of them even went on an adventure of annotating with their own teams. Since these projects are found to be better outsourced, almost all of them at present already work with manual annotation tools and outsource the work to carry out the Sisyphean mission. Nevertheless, today a little over 40% of the data scientists stated that they’re relying in whole or in part on off-the-shelf, pre-labeled training datasets.
Data scientists team leaders manage the neural networks training as a reiterative project as progressively more datasets are needed to be annotated at all times. For this purpose, proper volumes of well-annotated, quickly and inexpensively datasets must be obtained by any computerized vision development team. For them, ProjAIX platform offers ready to use trained datasets and all the services involved in annotating the customers’ datasets including the Cleansing, Labeling, Augmentation, Aggregation and Identification that enables the data scientists to focus on their main work – the Model Training and Tuning that are essential for the algorithms’ development. ProjAIX platform focuses on providing the required volumes of accurate datasets with the correct data presentation for any customer, in the desired useful format
Furthermore, ProjAIX provides a daily report designed for the development teams which states how effective their neural network is and track it. Additionally, an up-to-date BI report with time-series provide the computer vision team with insight – how they’re progressing, and how they’re doing about each of the items they detect or recognize.
How to improve your dataset with Cloud base platform
Neural Networks specialists say that the larger the dataset, more perfected the AI model will turn out to be. Obviously, value ground truth dataset is vital to the success of any AI model. Suitable and precise outcomes of AI models completely depend on these ground–truth datasets. This is why many companies have collected a significant number of datasets, nonetheless these dataset’s quality is incomplete, so training neural networks based on those sets isn’t effective. The reason the quality of the datasets is poor is that their upgrading is complex to manage, and sometimes too expensive. In this situation, many development managers come to the conclusion that it is better to get along with what is presented, in terms of cost-effectiveness.
Since size and quality of training data are the main blocks to applying neural network based computerized vision, in recent years companies have used to perform in-house annotations with their teams to address the quality of datasets issue. Over time it became clear that these in-house initiatives grow to unproductive expensive projects, so nowadays they are carried out by outsourcing.
Instead of sending the work to teams outside your organization, you can simply upload your existing datasets to PROJaiX platform that will perform and return an accurate standardize outcome and provide you with a detailed report that ensure a required level of quality. Another option is to simply purchase existing large datasets that PROJaiX platform offers for sale as a ready to use dataset which has been processed by de-duplication, removing inconsistencies and many other important time consuming benefits.