The EffShuff-Block model is designed to be lightweight while maintaining high classification performance.The input feature map is divided into two parts and processed differently, with one half undergoing a lightweight convolution, and the other half undergoing average pooling. The EffShuff-Transition component performs pooling by shuffling after performing lightweight convolution, resulting in a 57.9% reduction in computational cost compared to ShuffleNetv2, a well-known optimization for lightweight CNN models. In the experiments, the proposed EffShuff-Block model achieved 96.975% accuracy in age and gender classification, which is 5.83% higher than the state-of-the-art. The EffShuff-Dense-Block (Efficient shuffle dense block) model, which incorporates Dense Block to further emphasize low-level features, achieved 97.63% accuracy. Additionally, the results of the fine-grained image classification experiment demonstrate that the proposed EffShuff-Block and EffShuff-Dense-Block models have better classification performance with a smaller model size.
- The Audience dataset is available at https://talhassner.github.io/home/projects/Adience/Adience-data.html.
- The Butterfly & Moths dataset is available https://www.kaggle.com/datasets/gpiosenka/butterfly-images40-species.
- the NABirds dataset is available at https://dl.allaboutbirds.org/nabirds.
| Task | Training Set | Val_acc | Params | Flops |
|---|---|---|---|---|
classification |
The Audience dataset(Age) | 96.37 | 1.13M | 4.85G |
classification |
The Audience dataset(Gender) | 97.58 | 1.13M | 4.85G |
Fine-grained classification |
Butterfly & Moths dataset | 97.70 | 1.21M | 4.85G |
Fine-grained classification |
NABirds dataset | 88.26 | 1.64M | 9.70G |
- Age
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Gender
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Butterfly and moths
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NA Birds
