Project ResNet 50

The video tutorial explains how to use pre-trained models in Keras, focusing on the Resnet 50 model. It covers loading the model, preparing and preprocessing images, and making predictions. The tutorial encourages viewers to try using Resnet 50 with their own images to see its performance.

What is the primary advantage of using the Resnet 50 model over other models available in Keras?

• It is faster to train.

• It uses less memory while maintaining high accuracy.

• It supports more image formats.

• It is easier to implement.

Which library is used to load the Resnet 50 model in the tutorial?

• Keras

• Scikit-learn

• TensorFlow

• PyTorch

What is the required size for images to be compatible with the Resnet 50 model?

• 224x224 pixels

• 512x512 pixels

• 256x256 pixels

• 128x128 pixels

Why is it necessary to preprocess images before making predictions with Resnet 50?

• To enhance image quality

• To reduce the file size

• To match the input format expected by the model

• To convert the image to grayscale

What function is used to load an image in the tutorial?

• image.load()

• load_image()

• image.load_function()

• image.load_img()