In Labellerr, we have implemented active learning to autolabel datasets by selecting the most informative samples for labeling, complemented by zero-shot learning. The results in the datasets are excellent. Here's how to perform auto label jobs on Labellerr:
Options on the right side include 'accept', 'review', and 'client_review'—these indicate the statuses of annotated files in current or past projects that can be used as labeled data for labeling new data. Select 'Select labels to train' to choose the labels needed for the current project. After selecting the required options, click 'Next'.
<aside> 💡 An epoch is when all the training data is used at once and is defined as the total number of iterations of all the training data in one cycle for training the machine learning model.
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Click 'Start Job'. The job will begin, and the details will be displayed on your autolabel screen while the job is in progress.