Pipeline caption generation

In this exercise, you will use the same flickr dataset as previously, which has 30,000 images and associated captions. Now you will generate a caption for the following image using a pipeline instead of needing to import the model and preprocessing class specific to the model.

Photo of a man standing on a ladder cleaning a window

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

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

The pipeline module (pipeline) has been loaded.

This exercise is part of the course

Multi-Modal Models with Hugging Face

Exercise instructions

Load the image-to-text pipeline with Salesforce/blip-image-captioning-base pretrained model.
Use the pipeline to generate a caption for the image at index 3.

Hands-on interactive exercise

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

# Load the image-to-text pipeline
pipe = ____

# Use the pipeline to generate a caption with the image of datapoint 3
pred = ____

print(pred)

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 Exercise 7: Audio preprocessing Exercise 8: Pipeline tasks and evaluations Exercise 9: Pipeline caption generation

Current Exercise

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Exercise 1: Image-text similarity Exercise 2: Zero-shot learning with CLIP Exercise 3: Automated caption quality assessment Exercise 4: Multimodal sentiment analysis Exercise 5: Vision Language models: multi-modal setup Exercise 6: Vision Language models: multi-modal sentiment Exercise 7: Audio-visual emotion analysis Exercise 8: Video audio splitting Exercise 9: CLIP CLAP: Multi-modal emotion classification

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