VQA with Vision Language Transformers (ViLTs)

Time to have a go with multi-modal generation, starting with Visual Question-Answering (VQA). You will use the dandelin/vilt-b32-finetuned-vqa model to determine the color of the traffic light in the following image:

Picture of a traffic light showing red

The preprocessor (processor), model (model), and image (image) have been loaded for you.

This exercise is part of the course

Multi-Modal Models with Hugging Face

Exercise instructions

Preprocess the text prompt and image.
Generate the answer tokens from the model and assign to outputs.
Find the ID of the answer with the highest confidence using the output logits.

Hands-on interactive exercise

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

text = "What color is the traffic light?"

# Preprocess the text prompt and image
encoding = ____(____, ____, return_tensors="pt")

# Generate the answer tokens
outputs = ____

# Find the ID of the answer with the highest confidence
idx = outputs.logits.____
print("Predicted answer:", model.config.id2label[idx])

Edit and Run Code

This exercise is part of the course

Multi-Modal Models with Hugging Face

IntermediateSkill Level

4.9+

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Exercise 1: Visual question-answering (VQA)Exercise 2: VQA with Vision Language Transformers (ViLTs)

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Exercise 3: Document VQA with LayoutLM Exercise 4: Image editing with diffusion models Exercise 5: Custom image editing Exercise 6: Image inpainting Exercise 7: Video generation Exercise 8: Build a video!Exercise 9: Assessing video generation performance Exercise 10: Congratulations!