Classifies images and finds matches using CLIP's multimodal embeddings.
npx skills add https://github.com/devMoez/titan --skill clip-devmoez
Please help me install this Agent Skill. Skill: clip Source: https://github.com/devMoez/titan/tree/main/optional-skills/mlops/clip Command: npx skills add https://github.com/devMoez/titan --skill clip-devmoez
This Skill helps you understand images using natural-language text, so you can classify, search, and match visuals without collecting labeled training data.
Use the clip skill to run zero-shot classification by comparing your image against a list of label prompts.
High-intent search queries and answers about installing and using this skill.
Zero-shot image classification matches images to text prompts using CLIP embeddings, allowing you to assign labels to images without collecting labeled training data or fine-tuning models.
Cross-modal retrieval uses CLIP embeddings to find the best matching images for text queries, enabling you to search a folder of product photos using natural language descriptions like "red sneakers".
Yes, multimodal content moderation estimates categories like safe or unsafe by applying prompt-based labels to images using PyTorch-compatible inference with the CLIP model, requiring no fine-tuning.
Image-text similarity scoring works by preprocessing images and tokenizing text to produce normalized feature vectors, then calculating cosine similarity to match visuals to descriptive queries.
No, zero-shot classification using CLIP embeddings requires no fine-tuning, allowing you to directly compare your images against a custom list of label prompts using pre-trained models.
Cross-modal retrieval requires PyTorch, Transformers, and Pillow to handle image preprocessing and text tokenization, producing normalized feature vectors for cosine similarity calculations.
dawsonblockClassifies images and measures image-text similarity using CLIP without fine-tuning.
SignmanalClassify images and match image-text similarity using CLIP embeddings.
HolobiomicsLabLoad, fine-tune, and run inference with Hugging Face Transformers across NLP, vision, and audio tasks.
jonnabioClassify images and search by text using OpenAI's CLIP model.
