Research and Application of Fine-Grained Image Classification Based on Small Collar Dataset

With the rapid development of apparel e-commerce, the variety of apparel is increasing, and it becomes more and more important to classify the apparel according to its collar design. Traditional image processing methods have been difficult to cope with the increasingly complex image backgrounds. To solve this problem, an EMRes-50 classification algorithm is proposed to solve the problem of garment collar image classification, which is designed based on the ECA-ResNet50 model combined with the MC-Loss loss function method. Applying the improved algorithm to the Coller-6 dataset, and the classification accuracy obtained was 73.6%. To further verify the effectiveness of the algorithm, it was applied to the DeepFashion-6 dataset, and the classification accuracy obtained was 86.09%. The experimental results show that the improved model has higher accuracy than the existing CNN model, and the model has better feature extraction ability, which is helpful to solve the problem of the difficulty of fine-grained collar classification and promote the further development of clothing product image classification.

Automated summary

With the rapid development of apparel e-commerce, the classification of apparel based on its collar design has become increasingly important. Traditional image processing methods struggle with complex image backgrounds. To address the issue, an EMRes-50 classification algorithm is proposed, which combines the ECA-ResNet50 model with the MC-Loss loss function method. The algorithm achieved high classification accuracy when applied to the Coller-6 and DeepFashion-6 datasets. Experimental results demonstrate that the improved model outperforms existing CNN models in terms of accuracy and feature extraction ability.