Lightweight Distillation of SAM 3 and DINOv3 for Edge-Deployable Individual-Level Livestock Monitoring and Longitudinal Visual Analytics
arXiv:2604.27128v2 Announce Type: replace-cross Abstract: Foundation-model pipelines for individual-level livestock monitoring -- combining open-vocabulary detection, promptable video segmentation, and self-supervised visual embeddings -- have raised the accuracy ceiling of precision livestock farming (PLF), but their GPU memory budgets exceed the envelope of commodity edge accelerators. To close this gap, the 446M-parameter Perception Encoder (PE-ViT-L+) backbone of SAM 3 is distilled into a 40.66M-parameter multi-scale student through three mechanisms: a Feature Pyramid Network student encod
The proliferation of foundation models combined with the imperative for real-time edge processing in sectors like agriculture is driving demand for efficient distillation techniques.
This development enables the deployment of advanced AI capabilities, previously restricted to powerful GPUs, onto more accessible and cost-effective edge devices, broadening their application across various industries.
Previously GPU-bound foundation model pipelines can now be run on edge accelerators, making sophisticated individual-level monitoring and analytics economically viable for applications like precision livestock farming.
- · Precision agriculture technology providers
- · Edge AI hardware manufacturers
- · Livestock farmers
- · AI model distillation specialists
- · Providers of solely cloud-based AI monitoring solutions
- · Developers focused exclusively on large, untransformed foundation models
Increased adoption of individual-level monitoring systems in agriculture due to reduced hardware costs and improved accessibility.
Expansion of AI applications into other constraint-heavy edge environments beyond agriculture, such as remote infrastructure inspection or embedded medical devices.
Accelerated development of domain-specific, highly optimized AI models for niche industrial applications, fostering new AI service markets.
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Read at arXiv cs.AI