Encode images and text into embeddings for zero-shot classification, search, and moderation.
npx skills add https://github.com/graniet/kheish --skill clip-graniet
Please help me install this Agent Skill. Skill: clip Source: https://github.com/graniet/kheish/tree/main/skills/mlops/models/clip Command: npx skills add https://github.com/graniet/kheish --skill clip-graniet
This skill eliminates the need for large labeled image datasets and custom model training to perform common image-related tasks, reducing the time and resources required to build image understanding workflows.
Use the CLIP skill to check if the image 'user_uploaded_photo.jpg' contains violent or NSFW content for content moderation purposes.
High-intent search queries and answers about installing and using this skill.
Zero-shot image classification categorizes images into any set of labels without custom model training by encoding images and text into normalized embedding vectors for cosine similarity computation. This eliminates the need for large labeled image datasets.
Cross-modal retrieval enables semantic image search using natural language text queries. It encodes both images and text into normalized embedding vectors, allowing you to find images in a library based on text descriptions without manual tagging.
Yes, computing image-text similarity scores requires PyTorch, the CLIP library, Pillow, and Transformers to encode images and text into normalized embedding vectors for cosine similarity computation.
Content moderation for NSFW or violent content applies zero-shot image understanding to automatically detect policy violations in user-uploaded images. It uses cross-modal similarity scoring to evaluate unstructured image datasets against policy labels.
Zero-shot image classification works for unstructured image datasets by applying cross-modal retrieval and image-text similarity scoring. It processes images without task-specific model training, reducing the time and resources required to build image understanding workflows.
ovachieverEncode images and texts to compute cosine similarity for zero-shot classification and cross-modal retrieval.
afel6Compute image and text embeddings and rank matches by cosine similarity.
Z43LMatch images to natural-language descriptions for classification and retrieval.
ovachieverLoad, infer, and fine-tune pre-trained transformer models across modalities.
