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Reducing Training Data Using Pre-Trained Foundation Models: A Case Study on Traffic Sign Segmentation Using the

Sofia Henninger¹, Maximilian Kellner^1,2, Benedikt Rombach¹

¹Fraunhofer Institute for Physical Measurement Techniques IPM, 79110 Freiburg, Germany.

Journal of Imaging

|September 27, 2024

Summary

This summary is machine-generated.

The Segment Anything Model (SAM) shows promise for reducing training data needs in semantic segmentation tasks. A SAM-derived model maintained accuracy even with 95% less data, though SAM itself didn't outperform leading models.

Keywords:

Mask R-CNN segment anything model semantic segmentation traffic signs training data reduction

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Pre-trained foundation models offer adaptable solutions for specialized tasks.
The Segment Anything Model (SAM) has shown significant capabilities in semantic segmentation.
Data collection for model training is often resource-intensive.

Purpose of the Study:

To evaluate if foundation models like SAM can decrease the requirement for extensive training data.
To assess SAM's performance in traffic sign segmentation compared to established models.
To analyze the impact of reduced training data on model accuracy.

Main Methods:

Utilized five distinct datasets for semantic segmentation tasks.
Focused on traffic sign segmentation for comparative analysis.
Compared the performance of SAM against Mask R-CNN, a leading semantic segmentation model.

Main Results:

SAM did not outperform Mask R-CNN in traffic sign segmentation, irrespective of training data volume.
A knowledge-distilled student architecture derived from SAM demonstrated comparable accuracy.
The SAM-derived model maintained performance even when trained on data reduced by 95%.

Conclusions:

Foundation models like SAM may not universally surpass state-of-the-art models on all tasks.
Knowledge distillation from SAM can create efficient models requiring significantly less training data.
This approach holds potential for reducing data collection costs in computer vision applications.