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SAM 3 enables zero-annotation training for efficient YOLOv8 farming models

By PulseAugur Editorial · [3 sources] ·

Researchers have developed a novel method for training efficient YOLOv8 object detection models for precision pig farming by leveraging the Segment Anything Model 3 (SAM 3). This approach uses SAM 3 as an automated annotator to generate pseudo-labels, eliminating the need for manual data labeling. The resulting SAM 3-trained YOLOv8m model achieves a mean Average Precision (mAP) of 79.4% and significantly reduces inference latency, making it suitable for real-time edge deployment in smart agriculture. AI

IMPACT Enables scalable edge computing solutions for smart agriculture by reducing annotation costs and improving inference speed.

RANK_REASON The cluster describes a research paper detailing a new method for training AI models.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

SAM 3 enables zero-annotation training for efficient YOLOv8 farming models

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The cluster describes a research paper detailing a new method for training AI models.
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paper, model release
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158 days old
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COVERAGE [3]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    SAM3-Assisted Training of Lightweight YOLO Models for Precision Pig Farming

    Deep learning-based object detection has revolutionized Precision Livestock Farming (PLF), yet a critical barrier remains: high-performance Foundation Models (such as SAM 3) are too computationally intensive for edge deployment, while lightweight models (like YOLO) require prohib…

  2. arXiv cs.CV TIER_1 English(EN) · Marcos Vinicius Mendes Faria, Thiago Borges Pereira, Isabella C. F. S. Condotta, Thiago Meireles Paix\~ao, Francisco de Assis Boldt ·

    SAM3-Assisted Training of Lightweight YOLO Models for Precision Pig Farming

    arXiv:2605.25860v1 Announce Type: new Abstract: Deep learning-based object detection has revolutionized Precision Livestock Farming (PLF), yet a critical barrier remains: high-performance Foundation Models (such as SAM 3) are too computationally intensive for edge deployment, whi…

  3. arXiv cs.CV TIER_1 English(EN) · Francisco de Assis Boldt ·

    SAM3-Assisted Training of Lightweight YOLO Models for Precision Pig Farming

    Deep learning-based object detection has revolutionized Precision Livestock Farming (PLF), yet a critical barrier remains: high-performance Foundation Models (such as SAM 3) are too computationally intensive for edge deployment, while lightweight models (like YOLO) require prohib…

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