Overview
In this project, I focused on automating wildlife detection through NeoV, a custom fine-tune of YOLOv11 tailored for wildlife conservation. The goal was to create a model that could accurately detect African wildlife in real-time, streamlining data collection and monitoring efforts for conservationists. By improving detection efficiency and scalability, Neo aims to support the ongoing fight to protect endangered species and track wildlife behavior across diverse environments.
Key Features
Custom YOLOv11 Fine-tuning: Fine-tuned YOLOv11 to specialize in detecting various African wildlife species, ensuring high accuracy in diverse environmental conditions. Real-time Detection: Enabled real-time detection in both images and video streams, allowing conservationists to monitor wildlife behavior and poaching activities instantly. Scalable Monitoring: Designed the model to be scalable, capable of processing large datasets and adapting to different habitats, ensuring broad applicability across conservation projects. Automated Wildlife Detection: Automated the detection process, reducing the need for manual monitoring and allowing more efficient data collection, even in remote areas.
Technologies Used
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YOLOv11: For implementing the core object detection framework, fine-tuned for wildlife detection.
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Python: Used for training and deploying the model, leveraging deep learning frameworks for optimization and scalability.
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Ultralytics Hub: A low-code tool that is useful for finetuning YOLO models
Challenges and Learnings
One of the primary challenges was ensuring the model's accuracy across a wide variety of environments and lighting conditions. This involved gathering diverse datasets and fine-tuning the model to handle both static images and dynamic video input. Additionally, handling large datasets required optimizing the training pipeline to ensure efficiency while maintaining high detection accuracy. Real-time processing was also a key challenge, as I had to balance detection speed with model performance.