For several years, animal detection in the wildlife has been an area of great interest among biologists. They often study the behaviour of the animals to predict their actions. Since there are a large number of different animals, manually identifying them can be a daunting task. So, an algorithm that can classify animals based on their images can help researchers monitor them more efficiently. Also, animal detection and classification can help prevent animal-vehicle accidents, trace animal facility, prevent theft, and ensure the security of animals in zoos.
The application of deep learning is rapidly growing in the field of computer vision and is helping in building powerful classification and identification models. We can leverage this power of deep learning to build models that can classify and differentiate between different species of animals as well.
In this dataset, we provide 19,000 images of 30 different species of animals. In the next 90 days, the model will predict the probability of every animal class. The animal class with the highest probability will signify that the image belongs to that animal class.