AI models can be trained on a set of data to perform specific decision-making tasks. Simply speaking, these models are developed to replicate the thinking and decision-making process of human experts. Similar to humans, artificial intelligence methods require data sets to learn from (ground-truth) to apply the insights to new data. The data collection process is crucial for developing an efficient Machine Learning (ML) model. The quality and quantity of your dataset directly effects the AI model’s decision-making process. And these two factors determine the robustness, accuracy, and performance of the AI algorithms. As a result, collecting and structuring data is often more time-consuming than training the model on the data. The data collection is followed by image annotation, the process of manually providing information about the ground truth within the data. In simple words, image annotation is the process of visually indicating the location and type of objects that the AI model should learn to detect. For example, to train a deep learning model for detecting cats, image annotation would require humans to draw boxes around all the cats present in every image or video frame. In this case, the bounding boxes would be linked to the label named “cat.” The trained model will be able to detect the presence of cats in new images.

Source: A Guide to Data Collection For Computer Vision in 2022 |

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