Image preprocessing

In this exercise, you will use the flickr dataset, which has 30,000 images and associated captions, to perform preprocessing operations on images. This preprocessing is needed to make the image data suitable for inferencing with Hugging Face model tasks, such as text generation from images. In this case, you'll generate a text caption for this image:

Photo of 2 people with 1 playing the guitar

The dataset (dataset) has been loaded with the following structure:

Dataset({
    features: ['image', 'caption', 'sentids', 'split', 'img_id', 'filename'],
    num_rows: 10
})

The image-captioning model (model) has been loaded.

This exercise is part of the course

Multi-Modal Models with Hugging Face

Exercise instructions

Load the image from the element at index 5 of the dataset.
Load the image processor (BlipProcessor) of the pretrained model: Salesforce/blip-image-captioning-base.
Execute the processor on image, making sure to specify that PyTorch tensors (pt) are required.
Use the .generate() method to create a caption using the model.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Load the image from index 5 of the dataset
image = dataset[5]["____"]

# Load the image processor of the pretrained model
processor = ____("Salesforce/blip-image-captioning-base")

# Preprocess the image
inputs = ____(images=____, return_tensors="pt")

# Generate a caption using the model
output = ____(**inputs)
print(f'Generated caption: {processor.decode(output[0])}')
print(f'Original caption: {dataset[5]["caption"][0]}')

Edit and Run Code

This exercise is part of the course

Multi-Modal Models with Hugging Face

IntermediateSkill Level

5.0+

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Navigate the Hugging Face model hub, transform raw text, audio, and visual data into AI-friendly formats. Learn how to find the latest most popular models for tasks such as text generation and harness the power of pre-built pipelines.

Exercise 1: Hugging Face model navigation Exercise 2: How many models!?Exercise 3: Finding the most popular text-to-image model Exercise 4: Loading and preprocessing data Exercise 5: Text tokenizing Exercise 6: Image preprocessing

Current Exercise

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