The objective of this project is to fine-tune and validate the accuracy of FaceIT, an AI-powered livestock facial recognition system. The technology is already developed with established algorithms, but this project will focus on optimizing its performance, improving recognition accuracy, and testing real-world application using a provided dataset of livestock images.

Students will work with existing AI models to train, test, and evaluate the system’s effectiveness, exploring possible refinements to enhance its capabilities. Teams are welcome to suggest new approaches for improvement. All intellectual property (IP) remains with PrüvIT Technologies Inc., and participants must sign a Non-Disclosure Agreement (NDA).

Tasks and Activities:

• Dataset Preparation & Preprocessing:
• Work with provided livestock image datasets, ensuring proper image organization, cleaning, and normalization for AI training.
• Apply data augmentation techniques (cropping, rotation, contrast adjustment) to improve model robustness.
• AI Model Training & Fine-Tuning:
• Optimize hyperparameters, feature extraction methods, and model architectures to improve facial recognition accuracy.
• Experiment with alternative training techniques, augmentation strategies, or deep learning approaches to enhance detection and identification rates.
• Model Evaluation & Accuracy Testing:
• Design structured test cases to assess recognition performance, false positive/negative rates, and model reliability under real-world conditions.
• Implement a benchmarking framework to compare different training methodologies and quantify model improvements.
• Reporting & Documentation:
• Deliver a technical report summarizing refinements, testing methodologies, and results.
• Provide recommendations for future optimization, including additional data needs or AI architecture improvements.
• Document all modifications to the FaceIT model and their impact on accuracy.

✔ Preprocessed dataset optimized for AI training

✔ Fine-tuned AI model with improved recognition accuracy

✔ Comprehensive test results and benchmarking metrics

✔ Final technical report outlining refinements, results, and recommendations

✔ Technical documentation detailing model updates and training procedures

This project offers students an opportunity to apply AI, computer vision, and deep learning techniques in a real-world livestock technology setting while contributing to a cutting-edge innovation in livestock management and disease tracking.