Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm
  Surveillance
Raza Imam Muhammad Huzaifa Nabil Mansour Shaher Bano Mirza Fouad Lamghari
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}