Animal classification is the process of categorizing different species of animals based on their physical and biological characteristics. 

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Here, when we say physical biological charastetrisitcs, we mean:

1. Symmetry: radial like a starfish or bilateral like a butterfly?

2. Body plan: does it have a backbone? Is it covered in fur or scales?

3. Reproduction: does it lay eggs or is it some kind of internal fertilization?

4. Metabolism: is it a herbivore or carnivore? Or something else?

And many more. 

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By studying these characteristics, scientists can better understand the evolutionary relationships between different species and how they have adapted to different environments over time. They can aslo see the changes in the behavior and appearance of the animals by checking the data from different time periods. Based on this information, scientists make conclusions and provide recommendations for ecological improvements that can benefit both endangered and non-endangered species alike.

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So animal classification is something really important. And challenging. As it requires both: collection and processing of Information. For this very reason it is also very time consuming and costly: the animal kingdom is big and so is the volumes of the collected data. 

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Image annotation can help with animal classification by providing a way to analyze large amounts of visual data quickly and efficiently. 

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The procedure is straightforward: assign labels, such as bird, starfish, bear, or zebra. When necessary, add attributes like the presence of a backbone, radial or bilateral symmetry, or even the gender of the subject. Once completed, export the annotated dataset and apply the machine learning model to it. This will classify animals based on the provided labels, resulting in a quicker and more accurate animal classification procedure.To make the process of adding classification labels easier, ecologists use different tools and one of them is CVAT (Computer Vision Annotation Tool).

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Here is the short video describing the whole process step-by-step:

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