ResNET50-Based Model Combination Optimization for Anime Image Recognition

The global anime market continues to grow, character recognition is particularly important in the derivatives market. And the demand for anime content on various platforms continues to increase. However, relatively few studies have been conducted on anime character image recognition, and the current research focuses on image recognition using a single ResNet50 model, with few studies attempting to incorporate other models to improve recognition accuracy. This project aims to construct a Pokémon image recognition system using deep learning techniques to improve classification efficiency and accuracy. The project uses the Pokémon image dataset on kaggle and performs data preprocessing, including data balancing, de-noising, normalization and data enhancement. The project constructed a classification prediction model combining ResNet50 and SVM respectively, which achieved 97.25% accuracy, 97.11% recall and 97.15% F1 score on the test set, indicating that the model can effectively recognize different kinds of Pokémon images. This project verifies the effectiveness of the ResNet50 model in image recognition tasks and provides a useful reference for subsequent research on image recognition.