Map images and text into a shared embedding space for zero-shot classification and retrieval.
npx skills add https://github.com/heysuhas/hermes_cli --skill clip-heysuhas
Please help me install this Agent Skill. Skill: clip Source: https://github.com/heysuhas/hermes_cli/tree/main/optional-skills/mlops/clip Command: npx skills add https://github.com/heysuhas/hermes_cli --skill clip-heysuhas
This Skill bridges the gap between natural language and visual data, allowing you to search, classify, and moderate images without the need for expensive, specialized training datasets.
Use the clip skill to classify the provided image into categories like dog, cat, or bird by running the zero-shot classification workflow.
High-intent search queries and answers about installing and using this skill.
Zero-shot image classification is achieved by mapping visual and textual inputs into a shared embedding space, allowing you to categorize images into custom labels without fine-tuning. This Skill uses the CLIP model architecture to compute cosine similarity between image and text features.
Yes, semantic image search is supported by computing cross-modal retrieval. The Skill maps natural language queries and visual inputs into a shared embedding space to find and retrieve relevant images within large collections.
Yes, you need PyTorch and the transformers library to run cross-modal retrieval tasks. These dependencies, along with Pillow, are required to load the CLIP model architecture and process visual and textual inputs for embedding.
The best way to detect NSFW or graphic content is using zero-shot vision-language mapping for content moderation. This approach categorizes visual assets against custom text labels without requiring specialized training datasets to filter inappropriate material.
Cosine similarity works by measuring the distance between image and text features mapped into a shared embedding space. This cross-modal comparison allows the CLIP architecture to determine how closely visual content matches natural language descriptions.
HarriesConnects images and text in a shared embedding space using CLIP.
zerwizClassifies images and retrieves cross-modal matches using natural-language prompts and cosine similarity scoring without training data.
zepdbIndex and search text with Zeppelin vector store and BM25 full-text indexing.
zeronx798Classifies images and searches across datasets using natural language prompts.
