CNN and ViT Efficiency Study on Tiny ImageNet and DermaMNIST Datasets
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866915795156598784 |
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| author | Amangeldi, Aidar Taigonyrov, Angsar Jawad, Muhammad Huzaifa Mbonu, Chinedu Emmanuel |
| author_facet | Amangeldi, Aidar Taigonyrov, Angsar Jawad, Muhammad Huzaifa Mbonu, Chinedu Emmanuel |
| contents | This study evaluates the trade-offs between convolutional and transformer-based architectures on both medical and general-purpose image classification benchmarks. We use ResNet-18 as our baseline and introduce a fine-tuning strategy applied to four Vision Transformer variants (Tiny, Small, Base, Large) on DermatologyMNIST and TinyImageNet. Our goal is to reduce inference latency and model complexity with acceptable accuracy degradation. Through systematic hyperparameter variations, we demonstrate that appropriately fine-tuned Vision Transformers can match or exceed the baseline's performance, achieve faster inference, and operate with fewer parameters, highlighting their viability for deployment in resource-constrained environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08259 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CNN and ViT Efficiency Study on Tiny ImageNet and DermaMNIST Datasets Amangeldi, Aidar Taigonyrov, Angsar Jawad, Muhammad Huzaifa Mbonu, Chinedu Emmanuel Computer Vision and Pattern Recognition This study evaluates the trade-offs between convolutional and transformer-based architectures on both medical and general-purpose image classification benchmarks. We use ResNet-18 as our baseline and introduce a fine-tuning strategy applied to four Vision Transformer variants (Tiny, Small, Base, Large) on DermatologyMNIST and TinyImageNet. Our goal is to reduce inference latency and model complexity with acceptable accuracy degradation. Through systematic hyperparameter variations, we demonstrate that appropriately fine-tuned Vision Transformers can match or exceed the baseline's performance, achieve faster inference, and operate with fewer parameters, highlighting their viability for deployment in resource-constrained environments. |
| title | CNN and ViT Efficiency Study on Tiny ImageNet and DermaMNIST Datasets |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.08259 |