Data annotation is one of the main pillars of Artificial Intelligence and Machine Learning development, and as an essential part of most processes related to those developments, it must be properly handled in order to make valuable and trustworthy results. Selecting the right tool which is adjusted to your/your company’s needs on a high percentage becomes a challenging choice because there are numerous tools available on the market, with different features and performances. Before incorporating the data in Machine Learning model training you must prepare it in order to get a respectful data outcome which is highly valuable and suitable for further processing. Data annotation tools that I had a chance to work with and collaborate with companies that provide those services are:
Tasq.ai – An absolute champion in delivering high-value service and data outcomes through an undisturbed workflow. Our image annotation tool was the first one that met all expectations regarding data complexity and its processing. Also, we offer a wide range of data annotation and related services.
Heartex – They are using already prepared and pre-trained model data to annotate the tasks automatically or simply costume one through SDK. Automated annotations can boost annotators’ work effectiveness and save time for more complex tasks. The platform is based on Active Learning algorithms, which are selecting diverse data information and annotations from numerous unlabeled data. The combination of Machine learning and Active Learning algorithms enables this platform to automatically labels up to 95% of datasets included.
CVAT – Became popular because of its performance in labeling millions of objects which contain different properties. It also supports collaborative work, so members of the team can work together on the same project, regardless of their location. Users commonly used this feature and they create a public task, so others can join in and split the responsibilities in project development.