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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2410.06879 |
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| _version_ | 1866916429324877824 |
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| author | Pydimarry, Sai Abhinav Khairnar, Shekhar Madhav Palacios, Sofia Garces Sankaranarayanan, Ganesh Hoagland, Darian Nepomnayshy, Dmitry Nguyen, Huu Phong |
| author_facet | Pydimarry, Sai Abhinav Khairnar, Shekhar Madhav Palacios, Sofia Garces Sankaranarayanan, Ganesh Hoagland, Darian Nepomnayshy, Dmitry Nguyen, Huu Phong |
| contents | In the field of pattern recognition, achieving high accuracy is essential. While training a model to recognize different complex images, it is vital to fine-tune the model to achieve the highest accuracy possible. One strategy for fine-tuning a model involves changing its activation function. Most pre-trained models use ReLU as their default activation function, but switching to a different activation function like Hard-Swish could be beneficial. This study evaluates the performance of models using ReLU, Swish and Hard-Swish activation functions across diverse image datasets. Our results show a 2.06% increase in accuracy for models on the CIFAR-10 dataset and a 0.30% increase in accuracy for models on the ATLAS dataset. Modifying the activation functions in architecture of pre-trained models lead to improved overall accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_06879 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating Model Performance with Hard-Swish Activation Function Adjustments Pydimarry, Sai Abhinav Khairnar, Shekhar Madhav Palacios, Sofia Garces Sankaranarayanan, Ganesh Hoagland, Darian Nepomnayshy, Dmitry Nguyen, Huu Phong Computer Vision and Pattern Recognition In the field of pattern recognition, achieving high accuracy is essential. While training a model to recognize different complex images, it is vital to fine-tune the model to achieve the highest accuracy possible. One strategy for fine-tuning a model involves changing its activation function. Most pre-trained models use ReLU as their default activation function, but switching to a different activation function like Hard-Swish could be beneficial. This study evaluates the performance of models using ReLU, Swish and Hard-Swish activation functions across diverse image datasets. Our results show a 2.06% increase in accuracy for models on the CIFAR-10 dataset and a 0.30% increase in accuracy for models on the ATLAS dataset. Modifying the activation functions in architecture of pre-trained models lead to improved overall accuracy. |
| title | Evaluating Model Performance with Hard-Swish Activation Function Adjustments |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.06879 |