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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2605.15689 |
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| _version_ | 1866909046439673856 |
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| author | Gosal, Oswin Rios, Edwin Arkel Surya, Augusto Christian Mikael, Fernando Lai, Bo-Cheng Hu, Min-Chun |
| author_facet | Gosal, Oswin Rios, Edwin Arkel Surya, Augusto Christian Mikael, Fernando Lai, Bo-Cheng Hu, Min-Chun |
| contents | Fine-grained image recognition classifies subcategories such as bird species or car models. While state-of-the-art (SOTA) models are accurate, they are often too resource-intensive for deployment on constrained devices. Knowledge distillation addresses this by transferring knowledge from a large teacher model to a smaller student model. A key challenge is selecting the right teacher, as it heavily impacts student performance. This paper introduces a teacher selection metric, \textbf{Ratio 1-2}, based on teacher prediction ratios. Extensive analysis of over one thousand experiments across 3 students, 8 teachers, and 8 datasets under 4 training strategies demonstrates that our metric improves teacher selection by 18\% over previous methods, enabling small student models to achieve up to 17\% accuracy gains. Experiment codebase is available at: \href{https://github.com/arkel23/FGIR-KD-Teacher}{https://github.com/arkel23/FGIR-KD-Teacher}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15689 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | How to Choose Your Teacher for Fine Grained Image Recognition Gosal, Oswin Rios, Edwin Arkel Surya, Augusto Christian Mikael, Fernando Lai, Bo-Cheng Hu, Min-Chun Computer Vision and Pattern Recognition I.2; I.4 Fine-grained image recognition classifies subcategories such as bird species or car models. While state-of-the-art (SOTA) models are accurate, they are often too resource-intensive for deployment on constrained devices. Knowledge distillation addresses this by transferring knowledge from a large teacher model to a smaller student model. A key challenge is selecting the right teacher, as it heavily impacts student performance. This paper introduces a teacher selection metric, \textbf{Ratio 1-2}, based on teacher prediction ratios. Extensive analysis of over one thousand experiments across 3 students, 8 teachers, and 8 datasets under 4 training strategies demonstrates that our metric improves teacher selection by 18\% over previous methods, enabling small student models to achieve up to 17\% accuracy gains. Experiment codebase is available at: \href{https://github.com/arkel23/FGIR-KD-Teacher}{https://github.com/arkel23/FGIR-KD-Teacher}. |
| title | How to Choose Your Teacher for Fine Grained Image Recognition |
| topic | Computer Vision and Pattern Recognition I.2; I.4 |
| url | https://arxiv.org/abs/2605.15689 |