Labellerr provides the export feature to generate reports quickly by applying filters based on the Annotater ids, Datasets, Date and time ranges, Objects and Classifications.
In Labelbox, sometimes there is a lag in generating Reports.
Reviewing outputs is easy on Labellerr as compare to Labelbox.
Labellerr has the feature of Advance filters based on the file activity status such as no. of files annotated, reviewed, client reviewed, rejected, skipped etc.
It also has an extensive filter based on Dataset levels, Range of dates, Cumulative score and Remarks given.
Labellerr has developed "Smart feedback loop" that could be customized for a different use cases with very less effort and then it would cover the iterative nature of model training fully autonomous.
Custom ML model building has been a huge challenge for the ML community, as it requires a good amount of capital and time, to begin with. Customization is a smarter way is to bring automation to your continuous training data pipeline.