Fastening AI with Labellerr - From human-in-the loop to Bulk Predictions.

Imagine rallying a workforce of annotators to annotate some 1000s of images. Will this help the AI scientist achieve his end goal - faster deployment of models whose predictions are correct?

Probably not. This is because annotators differ in their perceptions about labelling a particular object. I think communication between stakeholders can help resolve this concern. Still, to and fro communication between all stakeholders will increase the time to market for AI scientists, affecting their performance metrics.

To ease this problem, automation was introduced into data annotation platforms, Labellerr included. Automation like SAM fastened the time to annotate individual objects. Still, the average time, say 10 seconds, to annotate an individual object was not short enough to give the 'right' data within the window period allotted to the AI scientist.

Active Learning was introduced to shorten the time to label all the instances on a single image file. This helped in reducing the time to annotate say 10 instances of a single object on one image. Still, annotation through active learning may go wrong because of wrong data selection at the initial stages. The time gained through active learning may be compensated by time lost during Quality Analysis.

Bulk prediction, i.e. annotating images in bulk without going through the process where images and objects are dissected individually to identify the right labels, helps save a lot of attention from the side of AI scientists. Also, imagine being able to use models/annotators concurrently to annotate images in bulk. Also, imagine the capability to visualise annotation from one model against another model. This will reduce the time to do QA ( quality analysis ).

Files at different stages of annotation inside Labellerr need different degrees of treatment concerning Quality analysis, labelling iterations etc. Models are made available for conducting these tasks at the place where all these files are listed.

Files are identified for two basic tasks - conducting Quality Analysis or first time annotation ( if the files are raw ).

Auto-label functionality inside Labellerr brings together public models as well as models created inside Labellerr ( using Active Learning job creation ). These models are presented to the AI scientist along with the metrics. This helps compare different models right at the point of use.

Model runs when conducted across images in bulk reduce the time that a user may have spent in

1. opening a single image file
2. analysing an image file
3. analysing the labels that have to be put around objects.
4. conducting a small qa at the level of each file, annotation instance etc.

Also, the predictions from the models used by the AI scientist are presented through a confusion matrix which helps him compare outcomes across models/annotators etc.

The functionality to create sample runs before annotation of the whole lot gives freedom to the AI scientist ; freedom to choose a model that is most appropriate for his end use.

The transition from human-in-the-loop to active learning and bulk predictions is more than scaling data annotation and QA efforts.

It empowers AI scientists to choose the most suitable annotations for their model training experiments, enabling faster, iterative training. By minimizing data preparation intervals, these advancements lead to quicker deployment of accurate models.