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Camel Farm Monitoring Framework

Overview

This repository contains the code for an automated framework designed for camel farm monitoring. The framework introduces two key contributions:

Unified Auto-Annotation Framework:
- Combines two models, GroundingDINO (GD), and Segment-Anything-Model (SAM), to automatically annotate raw datasets extracted from surveillance videos.
Fine-Tune Distillation Framework:
- Conducts fine-tuning of student models using the auto-annotated dataset.
- Involves transferring knowledge from a large teacher model to a student model, resembling a variant of Knowledge Distillation.
- Aims to be adaptable to specific use cases, enabling the transfer of knowledge from large models to small models, making it suitable for domain-specific applications.

Method Figure

Key Features

Automated Annotation: Utilizes GD and SAM models for automatic annotation of raw surveillance video datasets.
Fine-Tune Distillation: Conducts fine-tuning of student models for efficient real-time object detection.
Adaptability: Framework is designed to be adaptable to specific use cases, allowing knowledge transfer from large models to small models.

Dataset and Pre-trained Model

Raw Dataset: The framework leverages a raw dataset collected from Al-Marmoom Camel Farm in Dubai, UAE.
Pre-trained Model: GroundingDINO is used as the pre-trained teacher model while YOLOv8 as the student model for the Fine-Tune Distillation framework.

Deployable Model

The Fine-Tune Distillation framework produces a lightweight deployable model, YOLOv8, demonstrating high performance and computational efficiency for efficient real-time object detection.

Usage

To use this framework, follow the instructions provided in the corresponding directories for the Unified Auto-Annotation and Fine-Tune Distillation frameworks.

Citation

If you find this work useful for your research, please consider citing:

[Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm Surveillance] [Raza Imam, Muhammad Huzaifa, Nabil Mansour, Shaher Bano Mirza, Fouad Lamghar] [Link will be out soon]