Fine-Grained Classification for Poisonous Fungi Identification with Transfer Learning
This work addresses the problem of identifying poisonous fungi for applications like safety and biodiversity, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the fine-grained visual categorization of poisonous fungi species using ensemble classifier heads on pre-computed image embeddings from self-supervised vision models, achieving a best Track 3 score of 0.345, accuracy of 78.4%, and macro-F1 of 0.577 on the private test set.
FungiCLEF 2024 addresses the fine-grained visual categorization (FGVC) of fungi species, with a focus on identifying poisonous species. This task is challenging due to the size and class imbalance of the dataset, subtle inter-class variations, and significant intra-class variability amongst samples. In this paper, we document our approach in tackling this challenge through the use of ensemble classifier heads on pre-computed image embeddings. Our team (DS@GT) demonstrate that state-of-the-art self-supervised vision models can be utilized as robust feature extractors for downstream application of computer vision tasks without the need for task-specific fine-tuning on the vision backbone. Our approach achieved the best Track 3 score (0.345), accuracy (78.4%) and macro-F1 (0.577) on the private test set in post competition evaluation. Our code is available at https://github.com/dsgt-kaggle-clef/fungiclef-2024.