Classify images and match image-text pairs using OpenAI's CLIP model.
npx skills add https://github.com/hochoa13/Asistente-Harlest --skill clip-hochoa13
Please help me install this Agent Skill. Skill: clip Source: https://github.com/hochoa13/Asistente-Harlest/tree/main/skills/mlops/models/clip Command: npx skills add https://github.com/hochoa13/Asistente-Harlest --skill clip-hochoa13
This Skill bridges the gap between visual and textual information, enabling AI to understand and categorize images based on natural language descriptions without requiring specific training data for each new task.
Use the clip skill to classify the attached image 'photo.jpg' against the labels 'a dog', 'a cat', and 'a bird'.
High-intent search queries and answers about installing and using this skill.
Zero-shot image classification allows categorizing images using text prompts without task-specific fine-tuning. This Skill leverages OpenAI's CLIP model to match images against custom text labels, enabling classification into categories the model has never seen during training.
Yes, image-text similarity matching is supported using transformers and torch. This Skill uses the CLIP vision-language model to measure how well an image matches a given natural language description, requiring the pillow library to process the input image data.
Semantic image search finds images semantically related to a text query. By using the CLIP model, this Skill compares text descriptions against your image library, returning matches based on visual-textual understanding without requiring manual tagging or metadata labels.
Zero-shot image classification works for content moderation by identifying potentially inappropriate or harmful visual content. You can define moderation rules as text prompts, and the CLIP model will evaluate images against those descriptions to flag unsuitable material.
No task-specific training data is needed to classify images into custom categories. This Skill uses a zero-shot approach with the CLIP model, allowing you to define categories dynamically through natural language text prompts instead of collecting labeled datasets.
ovachieverEncode images and texts to compute cosine similarity for zero-shot classification and cross-modal retrieval.
kennyolofsson23-netizenGuide property-based testing of serialization, parsing, and smart contracts across languages.
ihatesea69Generate sentence, text, and image embeddings using pre-trained transformer models.
ihatesea69Classify images and match image-text pairs using OpenAI's CLIP model.
