EfficientNet edible fungus image recognition based on improved weight decay

Abstract

Objective: In order to solve the problem of edible fungus species identification, an EfficientNet edible fungus image classification model based on convolution neural network is proposed. Methods: Firstly, the edible fungus images were collected and the datasets were made according to different equipment and shooting environment, and then the model performance was improved through model training skills and network skills. A YWeight weight attenuation method was proposed to control the effective learning rate, and the generalization performance of the model was affected by controlling the cross-boundary. Results: This method makes EfficientNet-B0 obtain 79.82% （+0.85%） top-1 accuracy on the self built dataset YMushroom, and only 78.97% in the default training process. On the public dataset fungus, the accuracy of EfficientNet-B0 was 87.62% （+0.78%） and the original training accuracy was 86.84%. Conclusion: Experiments show that by adjusting the super parameters, the model finds a near optimal solution, and improves the performance of the edible fungus image classification model through weight attenuation, which provides a basis for the automatic management of edible fungus planting base in the future.

Keywords

• edible fungus
• image classification
• yweight method
• effective learning rate
• weight decay
• hyperparameter