MetaCLIP H14 FullCC2.5B
| Property | Value |
|---|---|
| Parameter Count | 986M parameters |
| License | CC-BY-NC-4.0 |
| Paper | Demystifying CLIP Data |
| Tensor Type | F32 |
| Tags | Zero-Shot Image Classification, Vision, MetaCLIP |
What is metaclip-h14-fullcc2.5b?
MetaCLIP H14 is a sophisticated vision-language model developed by Facebook Research, trained on 2.5 billion data points from CommonCrawl. This model represents a significant effort to reverse-engineer and demystify OpenAI's CLIP data preparation pipeline, offering a powerful alternative for multimodal understanding.
Implementation Details
The model utilizes a transformer-based architecture with a patch resolution of 14, specifically designed for processing both image and text inputs in a shared embedding space. Built using PyTorch and supporting Safetensors, it enables efficient processing and inference capabilities.
- Huge-sized architecture with 986M parameters
- Trained on CommonCrawl dataset with 2.5B samples
- Implements patch-based image processing (14x14)
- Supports zero-shot classification capabilities
Core Capabilities
- Zero-shot image classification
- Text-based image retrieval
- Image-based text retrieval
- Cross-modal embedding generation
Frequently Asked Questions
Q: What makes this model unique?
This model is unique in its approach to revealing CLIP's data curation methodology, offering a transparent alternative to OpenAI's closed-source implementation while maintaining high performance on vision-language tasks.
Q: What are the recommended use cases?
The model excels in applications requiring image-text alignment, including zero-shot image classification, content retrieval systems, and multimodal search applications. It's particularly useful when you need to work with images and text in a unified embedding space.
