The data have been split into a test and training set. The training set contains 13,999 images of animals and the test set contains 5000 images. There are two types of animals in this dataset, zebras and elephants.

Data was retrieved from the Data Repository for the University of Minnesota, https://doi.org/10.13020/D6T11K, under a creative commons license, from a study titled: Camera Trap Images used in "Identifying Animal Species in Camera Trap Images using Deep Learning and Citizen Science". All images were downloaded from Zooniverse and have been resized to 330x330 pixels.

“The cameras were located in Tanzania, at various places in the Serengeti National Park. The cameras were triggered by a combination of infrared and motion sensors and took three images, after which the trigger was blocked for one minute.” For more information on the dataset, please visit https://www.snapshotserengeti.org/ and https://conservancy.umn.edu/handle/11299/199819.

Your task is to provide the probability that an image contains a zebra. For each unique image ID you should estimate the likelihood that the image contains a zebra, with an estimated probability value between 0 and 1.

Turn your model into a web app with this blog post by Johnowhitaker.