CLIP ViT-Large-Patch14-336
| Property | Value |
|---|---|
| Author | OpenAI |
| Downloads | 19.7M+ |
| Framework Support | PyTorch, TensorFlow |
| Training Precision | float32 |
What is clip-vit-large-patch14-336?
This is an advanced implementation of OpenAI's CLIP (Contrastive Language-Image Pre-training) model, specifically using a Vision Transformer (ViT) architecture with large configuration. The model processes images with 336x336 pixel resolution using patch sizes of 14x14, enabling sophisticated zero-shot image classification capabilities.
Implementation Details
The model utilizes a transformer-based architecture optimized for processing visual information in conjunction with textual data. It's built on the PyTorch framework while also supporting TensorFlow, making it versatile for different development environments.
- Leverages large-scale vision transformer architecture
- Supports 336x336 pixel input images
- Uses 14x14 patch size for image tokenization
- Implements zero-shot classification capabilities
Core Capabilities
- Zero-shot image classification
- Multi-modal learning (text and image)
- Transfer learning applications
- High-resolution image processing
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its larger architecture and higher resolution input capability (336x336) compared to standard CLIP models, potentially offering better performance on detailed image analysis tasks.
Q: What are the recommended use cases?
The model is particularly well-suited for zero-shot image classification tasks, visual-semantic understanding, and applications requiring high-resolution image analysis without specific training data.
