What is the method for image processing in TensorFlow?

In TensorFlow, image processing methods typically involve the following steps:

1. Load image data: Use functions in the tf.io module to load image data, common functions include tf.io.read_file() and tf.io.decode_image().
2. Data preprocessing: preprocessing of image data, such as adjusting image size, normalization, augmentation, can be implemented using functions in the tf.image module, such as tf.image.resize(), tf.image.per_image_standardization(), etc.
3. Build a data pipeline: Use tf.data.Dataset to construct a data pipeline, transforming the data into a format that can be inputted into the model.
4. Utilize pre-trained models for feature extraction or fine-tuning: You can use pre-trained models for feature extraction or fine-tuning, such as those found in the tf.keras.applications module.
5. Training and evaluating the model: using the constructed model to train and evaluate image data.
6. Prediction: Use the trained model to make predictions on new image data.