Enhancement of Cat Breeds Classification Model Based on Meta Loss Correction

In the area of image recognition, the quality of image label data has a significant impact on the performance of classification models. Therefore, manual annotation has been used as a means to label images. However, manual annotation is laborious and time-consuming and can introduce additional noise. To address these issues, this paper investigates an automatic algorithm for improving a cat breed classification model based on meta loss correction. The proposed algorithm leverages web crawling techniques to obtain unlabeled images of cats, filters them through object recognition, and selects only images containing cats. These images are then fed into the algorithm, which utilizes a pretrained initial model to generate pseudo-labels. These pseudo-labeled data are subsequently refined using a meta loss function, correcting the inaccuracies associated with the pseudo-labels. Finally, the labeled new data is merged with the original dataset, gradually increasing both the quantity and quality of the dataset. Experimental results demonstrate that as the merged dataset expands, the model’s error decreases gradually, and its performance improves.