Food Image Classification Based on CBAM-Inception V3 Transfer Learning

Abstract

To improve the accuracy of automatic recognition and classification of food images, a classification model CBAM- InceptionV3 is proposed, which embeds the Convolutional Block Attention Module. The specific method is to split the Inception V3 model with ImageNet pre-trained weight parameters into blocks, embed CBAM modules after each Inception block, and reassemble them into a new model, embedding a total of 11 CBAM modules. This new model is used for transfer learning of Food-101 food image dataset padded and scaled to 299 pixels in both length and width, with the highest accuracy of 82.01%. Compared with the original Inception V3 model, the CBAM module can effectively improve the model's feature extraction and classification capabilities. At the same time, transfer learning can significantly improve the accuracy rate and shorten the training time compared with the training from scratch. Compared with several other mainstream convolutional neural network models, the results show that this new model has higher recognition accuracy and can provide strong support for food image classification and recognition.

Keywords

• food image classification
• channel attention; spatial attention
• cbam
• inceptionv3
• transfer learning