VQA met Vision Language Transformers (ViLTs)

Tijd om zelf aan de slag te gaan met multi-modale generatie, te beginnen met Visual Question-Answering (VQA). Je gebruikt het model dandelin/vilt-b32-finetuned-vqa om de kleur van het verkeerslicht in de volgende afbeelding te bepalen:

Picture of a traffic light showing red

De preprocessor (processor), het model (model) en de afbeelding (image) zijn alvast voor je geladen.

Deze oefening maakt deel uit van de cursus

Multi-modale modellen met Hugging Face

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Oefeninstructies

Verwerk de text-prompt en image.
Genereer de antwoordtokens met het model en sla ze op in outputs.
Zoek de ID van het antwoord met de hoogste zekerheid via de output-logits.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

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])

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Multi-modale modellen met Hugging Face

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

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: Preprocessing different modalities Exercise 5: Text tokenizing Exercise 6: Image preprocessing Exercise 7: Audio preprocessing Exercise 8: Pipeline tasks and evaluations Exercise 9: Pipeline caption generation Exercise 10: Passing keyword arguments Exercise 11: Model evaluation on a custom dataset

Learn to master individual modalities with state-of-the-art models. Dive into computer vision for image classification and segmentation, explore speech recognition and text-to-speech synthesis, and learn effective fine-tuning techniques. Build practical skills with pre-trained models from Hugging Face's transformers library.

Exercise 1: Computer vision Exercise 2: Image classification Exercise 3: Object detection Exercise 4: Image background removal Exercise 5: Fine-tuning computer vision models Exercise 6: CV fine-tuning: dataset prep Exercise 7: CV fine-tuning: model classes Exercise 8: CV fine-tuning: trainer configuration Exercise 9: Speech recognition and audio generation Exercise 10: Automatic speech recognition Exercise 11: Creating speech embeddings Exercise 12: Audio denoising Exercise 13: Fine-tuning text-to-speech models Exercise 14: Fine-tuning a text-to-speech model Exercise 15: Generating new speech

Learn to fuse visual, textual, and audio information for richer AI applications. Master techniques like CLIP for zero-shot classification, build sentiment analyzers that see and read, and create emotion detectors that combine facial expressions with voice. Take your AI models beyond single-modality thinking.

Exercise 1: Zero-shot image classification Exercise 2: Zero-shot learning with CLIP Exercise 3: Automated caption quality assessment Exercise 4: Multi-modal sentiment analysis Exercise 5: Prompting Vision Language Models (VLMs)Exercise 6: Multi-modal sentiment classification with Qwen Exercise 7: Zero-shot video classification Exercise 8: Video audio splitting Exercise 9: Video sentiment analysis with CLIP CLAP

Transform ideas into reality! Master cutting-edge AI techniques to generate and manipulate visual content using text prompts. Create stunning images, edit photos intelligently, and build powerful question-answering systems for images and documents. Turn your creative vision into digital reality with multi-modal AI.

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

Huidige oefening

Exercise 3: Document-VQA met LayoutLM Exercise 4: Afbeeldingen bewerken met diffusiemodellen Exercise 5: Aangepaste bewerkingen op afbeeldingen Exercise 6: Image inpainting Exercise 7: Video genereren Exercise 8: Bouw een video!Exercise 9: Prestaties van videogeneratie beoordelen Exercise 10: Gefeliciteerd!