Abstract
The article provides an analysis of image annotation process for artificialintelligence models within modern detection systems learning using modern tools for annotation. Software application requirements and parameters list has been formed for image annotation, which are sufficiently consistent with the annotation process. Provided charts displays key parameters for image annotation process in modern applications. Mass factor approach role importance reviewed in accordance with annotation task solving in modern recognition systems. Yoloanno application has been developed, whichincorporates all requirements for an annotation process: functional and timing, - and provides tools to solve the task, what was proven during the experiments. Obtained results could be used for image annotation task practical solution, as well as provided approaches could be used for new image annotation applications creation. Ref. 8, pic. 4
Highlights
Artificial intelligence-based image recognition systems are becoming popular nowadays
Image annotation process was examined as a part of artificial intelligence models based on Yolo detection system training, and all mandatory same as optional process stages were defined
Annotation process could be significantly automated using software applications. Major requirements for such applications are enough abilities for process coverage, minimal time consumption factor based graphical user interface, user actions count value should be least possible without repetitions and excessiveness
Summary
Artificial intelligence-based image recognition systems are becoming popular nowadays. Scopes of neural networks application varies from speech recognition tasks to large data processing centers for decision-making and prediction. Effective usage of image recognition systems requires software tools, providing enough possibilities for learning data annotation. Data requirements specifics for artificial intelligence models training is directly connected with recognition tasks. Attention increase for artificial intelligence models usage caused numerous available software applications appearance. Such programs provide tools for annotation processes automatization, it’s more effective execution. Artificial intelligence models implementation could vary with many parameters, but mostly train data quantity factor directly proportional to recognition precision and may require from hundreds and thousands of images till tenth of thousands. For enough performanced annotation of large images counts, all suitable for automatization processes should be performed by the software, so letting the annotation process last for a foreseeable period of time
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