  In this study, we propose an automated framework for camel farm monitoring,
introducing two key contributions: the Unified Auto-Annotation framework and
the Fine-Tune Distillation framework. The Unified Auto-Annotation approach
combines two models, GroundingDINO (GD), and Segment-Anything-Model (SAM), to
automatically annotate raw datasets extracted from surveillance videos.
Building upon this foundation, the Fine-Tune Distillation framework conducts
fine-tuning of student models using the auto-annotated dataset. This process
involves transferring knowledge from a large teacher model to a student model,
resembling a variant of Knowledge Distillation. The Fine-Tune Distillation
framework aims to be adaptable to specific use cases, enabling the transfer of
knowledge from the large models to the small models, making it suitable for
domain-specific applications. By leveraging our raw dataset collected from
Al-Marmoom Camel Farm in Dubai, UAE, and a pre-trained teacher model,
GroundingDINO, the Fine-Tune Distillation framework produces a lightweight
deployable model, YOLOv8. This framework demonstrates high performance and
computational efficiency, facilitating efficient real-time object detection.
Our code is available at
\href{https://github.com/Razaimam45/Fine-Tune-Distillation}{https://github.com/Razaimam45/Fine-Tune-Distillation}
