Deep learning is a machine learning method that uses multi-layer neural networks to learn data features and expressions. It has a wide range of applications and excellent performance in the field of image recognition. This article introduces how to use deep learning to achieve efficient image recognition, mainly including the following aspects:
- Preprocessing of image data, including operations such as cropping, scaling, rotating, flipping, and normalizing, to increase data diversity and robustness.
- Extraction of image features, including the use of convolutional neural network (CNN) and other models to automatically learn high-level semantic features from images for subsequent classification or detection tasks.
- The method of image classification, including using components such as fully connected layers, softmax layers, and cross-entropy loss functions to build an end-to-end image classification network to label objects or scenes in images.
- Evaluation of image recognition, including using indicators such as accuracy rate, recall rate, F1 value, and visualization tools such as confusion matrix to evaluate the performance and effect of image recognition models.