Welcome to the exciting world of wildlife management and species classification! The TrapperAI model leverages ultra-modern machine learning techniques to detect and classify 18 different European mammal species. With unparalleled accuracy and efficiency, let’s dive into how you can implement this powerful assistant in your projects.

🐾 Overview of TrapperAI

The TrapperAI model, built on the fine-tuned YOLOv8-m model, showcases astonishing performance with a 95% F1-score and a 93% mAP50-95. It quickly processes impressive datasets, including 401,458 camera trap images gathered from multiple European countries.

This model can efficiently detect the following species:

• Bird
• Cat
• Chamois
• Dog
• Eurasian Lynx
• Eurasian Red Squirrel
• European Badger
• European Mouflon
• Fallow Deer
• Gray Wolf
• Hare
• Marten
• Moose
• Red Deer
• Red Fox
• Reindeer
• Roe Deer
• Wild Boar

The recommended image resolution for processing is 1024px, and the model can efficiently process approximately 30,000 images in just one hour on an NVIDIA GPU with more than 11 GB of vRAM.

📥 Installation Steps

To get TrapperAI up and running, follow these simple steps:

• First, create a virtual environment:

bash
$ python3 -m venv env
$ source env/bin/activate
$ pip install ultralytics dill ipython  # IPython is optional

🚀 Usage Guide

Here’s how to utilize the TrapperAI model once installed:

ipython
In [1]: from ultralytics import YOLO
In [2]: model = YOLO('TrapperAI-v02.2024-YOLOv8-m.pt')
In [3]: results = model.predict('fox36-Vulpes-vulpes.jpg')
In [4]: len(results)  # how many animals were detected
Out[4]: 1
In [5]: results[0].show()  # open image viewer with detection results
In [6]: results[0].boxes.conf  # confidence score
Out[6]: tensor([0.9558], device=cuda:0)
In [7]: results[0].boxes.cls  # index value for detected species
Out[7]: tensor([14.], device=cuda:0)  # Red Fox

If your image contains more than one object, you will need to loop through the results to obtain the confidence score and species index value for each detected entity.

🏢 Who is Using TRAPPER?

A myriad of prestigious institutions are employing the TrapperAI model, including:

• Mammal Research Institute Polish Academy of Sciences
• Karkonosze National Park
• Swedish University of Agricultural Sciences
• University of Freiburg Wildlife Ecology and Management
• Bavarian Forest National Park
• And many more!

💡 Troubleshooting Tips

While using the TrapperAI model, you may encounter issues that can be resolved easily. Here are some ideas:

• If you find that your images are not being processed accurately, ensure that the images are of the specified resolution (1024px).
• Check for potential errors in your installing process—make sure all necessary libraries have been successfully installed.
• Running out of GPU memory? Consider reducing the batch size or selecting a less complex model.

For further troubleshooting, for updates, or to collaborate on AI development projects, stay connected with **fxis.ai**.

📜 Conclusion

With the help of TrapperAI, you have a powerful tool at your fingertips for detecting and classifying European mammal species. At **fxis.ai**, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.