Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm Surveillance
<|reference_start|>Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm Surveillance: 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}<|reference_end|>
@article{imam2024domain,
    title={Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm
  Surveillance},
    author={Raza Imam, Muhammad Huzaifa, Nabil Mansour, Shaher Bano Mirza, Fouad
  Lamghari},
    journal={arXiv preprint arXiv:2402.07059},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07059},
    primaryClass={cs.CV}
}