Evaluación automática de la calidad de los pies de foto

Has clasificado correctamente la imagen del vestido, pero ¿qué tal era la descripción original?

Maa Fab wrap with a Trendy design dress with Vibrant color for an elegant touch of Fabric completely Soft and Comfortable wear with amazing design of Solid Boat ? Neck Flared Dress to make a perfect addition to your wardrobe collection.

Ahora usarás el modelo CLIP para cuantificar lo precisa que es esta descripción utilizando el CLIP score. El pie de foto (description), la imagen (image), la clase ToTensor y la función clip_score() de torchmetrics ya están cargados.

Este ejercicio forma parte del curso

Modelos multimodales con Hugging Face

Instrucciones del ejercicio

Convierte la imagen en un tensor de PyTorch con intensidades en el rango 0-255.
Usa la función clip_score() para evaluar la calidad del pie de foto comparando image_tensor y description con el modelo openai/clip-vit-base-patch32.

Ejercicio interactivo práctico

Prueba este ejercicio y completa el código de muestra.

# Convert the image to a PyTorch tensor
image_tensor = ____()(image)*____

# Use the clip_score function to assess the quality of the caption
score = ____(____, ____, "____")

print(f"CLIP score: {score}")

Editar y ejecutar código

Este ejercicio forma parte del curso

Modelos multimodales con Hugging Face

IntermedioNivel de habilidad

4.8+

Comienza el curso 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: Clasificación de imágenes sin entrenamiento previo Exercise 2: Aprendizaje sin disparos con CLIP Exercise 3: Evaluación automática de la calidad de los pies de foto

Ejercicio actual

Exercise 4: Análisis multimodal del sentimiento Exercise 5: Modelos de lenguaje visual (VLM) con indicaciones Exercise 6: Clasificación multimodal del sentimiento con Qwen Exercise 7: Clasificación de vídeos sin entrenamiento previo Exercise 8: División de audio y vídeo Exercise 9: Análisis de sentimiento en vídeo con 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!