Classificazione di immagini

In questo esercizio userai lo stesso insieme di dati di flickr di prima, con 30.000 immagini e didascalie associate. Ora classificherai l’immagine usando un modello preaddestrato.

Photo of 2 people, 1 is playing the guitar

L’immagine di esempio (image) e il modulo della pipeline (pipeline) sono già stati caricati.

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Modelli multi-modali con Hugging Face

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Istruzioni dell'esercizio

Carica la pipeline image-classification con il modello preaddestrato google/mobilenet_v2_1.0_224.
Usa la pipeline per prevedere la classe dell’immagine di esempio.
Stampa la prima label (a probabilità più alta).

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Load the image-classification pipeline
pipe = ____

# Use the pipeline to predict the class of the sample image
pred = ____

# Print the first (highest probability) label
print("Predicted class:", ____)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Modelli multi-modali con Hugging Face

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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: 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: Classificazione di immagini

Esercizio in corso

Exercise 3: Rilevamento di oggetti Exercise 4: Rimozione dello sfondo dell'immagine Exercise 5: Fine-tuning dei modelli di computer vision Exercise 6: Fine-tuning CV: preparazione del dataset Exercise 7: Fine-tuning CV: classi del modello Exercise 8: Fine-tuning CV: configurazione del trainer Exercise 9: Riconoscimento vocale e generazione audio Exercise 10: Riconoscimento vocale automatico Exercise 11: Creare speech embedding Exercise 12: Rimozione del rumore audio Exercise 13: Messa a punto dei modelli text-to-speech Exercise 14: Fine-tuning di un modello text-to-speech Exercise 15: Generare nuova voce

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 with Vision Language Transformers (ViLTs)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!