Embed images and text prompts into a shared space for zero-shot similarity matching.
npx skills add https://github.com/ZardLi1115/zedclaw --skill clip-zardli1115
Please help me install this Agent Skill. Skill: clip Source: https://github.com/ZardLi1115/zedclaw/tree/main/optional-skills/mlops/clip Command: npx skills add https://github.com/ZardLi1115/zedclaw --skill clip-zardli1115
This Skill solves the problem of identifying, comparing, and searching images using natural-language labels without requiring task-specific training data.
Use the clip skill to classify the image file "photo.jpg" against labels like a dog, a cat, a bird, and a car by running a zero-shot similarity comparison between image and tokenized text prompts.
High-intent search queries and answers about installing and using this skill.
Zero-shot image classification matches images to natural language text labels by encoding both into a shared representation space and calculating cosine similarity scores to select top-matching categories.
Semantic image search retrieves relevant images for a text query by embedding both text prompts and images into a shared space, then comparing their similarity scores to rank results.
Zero-shot image-text matching uses transformers and torch to encode images and text prompts, producing cosine-like similarity scores between encoded features to rank image-caption matches for cross-modal retrieval.
Lightweight content moderation categorizes images by measuring similarity against natural-language text labels, using zero-shot classification to rank image-text matches without requiring task-specific training data.
Computing image-text similarity scores requires installing CLIP-compatible libraries, primarily leveraging transformers, torch, and pillow to process image files and tokenize text prompts for encoding.
Zero-shot classification is not suitable when precise task-specific accuracy is required, as it relies on general image-text matching without dedicated training data to produce similarity-based predictions.
gztcodeClassify images and match them to text using OpenAI's CLIP model.
eddieluengCompute image and text embeddings for zero-shot classification and retrieval.
ovachieverEncode images and texts to compute cosine similarity for zero-shot classification and cross-modal retrieval.
kaminocorpLoad OpenAI CLIP models to perform zero-shot image classification and image-text similarity.
