Classify images zero-shot and retrieve cross-modal matches via cosine similarity.
npx skills add https://github.com/automatedigital/spark --skill clip-automatedigital
Please help me install this Agent Skill. Skill: clip Source: https://github.com/automatedigital/spark/tree/main/skills/mlops/models/clip Command: npx skills add https://github.com/automatedigital/spark --skill clip-automatedigital
This Skill eliminates the need for large labeled image datasets and custom model training to perform image understanding tasks, saving significant time and resources for vision-language workflows.
Use the clip skill to classify the image 'user_uploaded_photo.jpg' into the categories 'safe for work', 'violent content', and 'graphic content' to check for policy violations.
High-intent search queries and answers about installing and using this skill.
Zero-shot image classification categorizes images into any custom set of labels without task-specific model fine-tuning or large labeled image datasets. You simply provide the image and your predefined categories to generate predictions.
Yes, cross-modal retrieval enables semantic image search by computing cosine similarity between visual and textual modalities. You can search for images using text queries or find matching text descriptions for input images.
Content moderation teams can automatically flag unsafe, violent, or graphic images by classifying them against predefined safety categories using zero-shot image classification, eliminating the need for custom classifier development.
No, cross-modal retrieval and zero-shot image classification do not require large labeled image datasets. The Skill generates normalized image and text embeddings to compute similarity without task-specific model fine-tuning.
Yes, the Skill generates normalized image and text embeddings that can be integrated with vector databases for scalable image indexing and retrieval across visual and textual content.
Zero-shot image classification applies to batch image processing for individual and enterprise vision-language workflows, supporting use cases like image deduplication, visual question answering, and content moderation.
overviewlabsCompute CLIP embeddings for zero-shot image classification and cross-modal retrieval.
ovachieverEncode images and texts to compute cosine similarity for zero-shot classification and cross-modal retrieval.
MLAI-AUS-IncSearch community members by expertise using vector embeddings and cosine similarity.
InverterNetworkFine-tune large language models with TRL for SFT, DPO, and GRPO workflows.
