Small data? Tabular vs conventional models in crowd classification

In the field of crowd analysis, especially in high-traffic contexts such as pilgrimage, classifying the state of crowds becomes a critical challenge. The scarcity of labeled data—due to the costly and complex annotation process—drives the search for methods that can operate with few examples. This is where two families of models come into play: tabular foundation models, designed to make predictions from a few cases without the need for specific training, and conventional machine learning models (such as decision trees, gradient boosting, or lightweight neural networks) that require hyperparameter tuning but offer greater maturity and control. The decision between one or the other is not trivial and depends heavily on the available label budget. When this is very limited, foundation models excel at their ability to generalize with just a handful of examples; however, as the labeled set grows, well-tuned conventional models match and even surpass in accuracy, especially in tasks where the geometric structure of the data is relevant. Additionally, computational efficiency introduces another dimension: conventional models demand a costly tuning process in terms of time and resources, while foundation models skip that step but reprocess the entire context with each prediction, which can scale poorly. For a company developing custom smart surveillance applications, understanding this balance is key. At Q2BSTUDIO, we integrate artificial intelligence into custom software solutions that optimize crowd management, relying on AWS and Azure cloud services to scale processing and on business intelligence services such as Power BI to visualize patterns in real time. Our AI agents can dynamically adapt to data volume, choosing between foundation or conventional models based on the label budget. Cybersecurity is also a priority when handling sensitive video streams. Thus, the strategic choice is not purely technical but business-oriented: invest in costly labeling or leverage models that require less data. Ultimately, there is no universal solution; the decision map depends on label scarcity and computational resources, and our experience in AI for businesses allows us to guide organizations toward the most efficient architecture for each case.