nileshkhetrapal / yassqueendb Goto Github PK

Implement methods to calculate evaluation metrics (e.g., accuracy, precision, recall, F1 score) to measure the performance of the image classification model.

Create a new method predict_image_class that accepts an input image, preprocesses it, generates its embedding using the image encoder, and predicts the class label using the trained classifier.

Choose a pre-trained image encoder, such as ResNet, MobileNet, or EfficientNet. Load the pre-trained model using TensorFlow Hub or Keras applications.

Add a function to preprocess images, such as resizing, normalization, and data augmentation. You can use libraries like OpenCV, Pillow, or TensorFlow's tf.image module for this purpose.

Update the example code and documentation to showcase the newly added image classification functionality.

For multiclass classification, you'll need labeled training data. Create a new method train_image_classifier that accepts training images and their corresponding labels. Use these embeddings and labels to train a classifier (e.g., logistic regression, SVM, or a simple neural network) using libraries like scikit-learn or TensorFlow.

Update the generate_embedding method to handle image data. Add a new parameter data_type to differentiate between text and image data. When data_type is "image", use the pre-trained image encoder to generate embeddings.

Modify the add_node method to accept image data. When adding image nodes, call the generate_embedding method with data_type="image" to generate image embeddings.

Modify the GraphDatabase class to handle image data. Create a new method called load_image_encoder that loads the pre-trained image model.