Map images and text into a shared latent space for zero-shot classification.
npx skills add https://github.com/Rheasilvia/hermes-desktop --skill clip-rheasilvia
Please help me install this Agent Skill. Skill: clip Source: https://github.com/Rheasilvia/hermes-desktop/tree/main/optional-skills/mlops/clip Command: npx skills add https://github.com/Rheasilvia/hermes-desktop --skill clip-rheasilvia
This skill solves the challenge of categorizing images or searching through visual databases without the need for expensive, custom-trained classification models.
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 maps images and text into a shared latent space to categorize images using natural language labels. This computes cosine similarity between image and text embeddings, instantly classifying visual assets into custom categories without expensive training.
Semantic image search uses cross-modal retrieval to find images matching specific text descriptions. By mapping both images and text into a shared latent space, the system computes similarity scores to retrieve relevant visual assets from a database.
Zero-shot image classification requires PyTorch, transformers, and Pillow to run. These dependencies provide the foundational CLIP model architecture and image processing capabilities needed to compute cosine similarity between image and text embeddings.
Content moderation automatically detects and filters NSFW, violent, or graphic content in image datasets. By applying zero-shot classification with specific text labels, the system categorizes and flags inappropriate visual assets during batch processing.
The best way to categorize images using natural language is zero-shot classification. It leverages a vision-language model to map inputs into a shared latent space, computing cosine similarity between image and text embeddings to instantly classify visual assets.
Batch processing of visual assets for cross-modal retrieval is supported. The system computes embeddings for images and text, calculating cosine similarity across the batch to perform semantic image search or zero-shot classification at scale.
HarriesConnects images and text in a shared embedding space using CLIP.
biomaps-infraCompare mass spectra and identify compounds using the matchms Python library.
Supporter09Train BPE, WordPiece, or Unigram tokenizers with offset mapping for Transformers.
hung-phanDesign and implement convolutional neural networks for image tasks using PyTorch.
