Fine-Grained Classification for Poisonous Fungi Identification with Transfer Learning

• URL: http://arxiv.org/abs/2407.07492v1
• Date: Wed, 10 Jul 2024 09:24:50 GMT
• Title: Fine-Grained Classification for Poisonous Fungi Identification with Transfer Learning
• Authors: Christopher Chiu, Maximilian Heil, Teresa Kim, Anthony Miyaguchi,
• Abstract summary: FungiCLEF 2024 addresses the fine-grained visual categorization (FGVC) of fungi species. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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