Start Date: 13 March End Date:  24 May Competition URL: HuggingFace

Motivation

Automatic recognition of fungi species assists mycologists, citizen scientists, and nature enthusiasts identify wild species. Its availability supports the collection of valuable biodiversity data. In practice, species identification typically does not depend solely on the visual observation of the specimen but also on other information available to the observer - such as habitat, substrate, location and time. Thanks to rich metadata, precise annotations, and baselines available to all competitors, the challenge provides a benchmark for image recognition with the use of additional information. Moreover, the toxicity of a mushroom can be crucial for a mushroom picker's decision. We will explore the decision process within the competition beyond the commonly assumed 0/1 cost function.

Task Description

Given the set of real fungi species observations and corresponding metadata, the goal of the task is to create a classification model that returns a ranked list of predicted species for each observation (multiple photographs of the same individual + geographical location). 

The classification model must fit limits for memory footprint and a prediction time limit (120 minutes) within a given HuggingFace server instance (Nvidia T4 small 4vCPU, 15GB RAM, 16GB VRAM).

Note: Since the test set contains multiple out-of-the-scope classes. The solution has to handle such classes.