CLIP-ViT-L-14-DataComp.XL-s13B-b90K
| Property | Value |
|---|---|
| License | MIT |
| Research Paper | DataComp Paper |
| Training Data | DataComp-1B (1.4B samples) |
| ImageNet-1k Accuracy | 79.2% (zero-shot) |
What is CLIP-ViT-L-14-DataComp.XL-s13B-b90K?
This is an advanced implementation of the CLIP (Contrastive Language-Image Pre-training) model, specifically using a Vision Transformer Large/14 architecture. Trained on the massive DataComp-1B dataset, it represents a significant advancement in zero-shot image classification and multi-modal learning. The model was trained on stability.ai's infrastructure and demonstrates state-of-the-art performance in various image understanding tasks.
Implementation Details
The model utilizes the OpenCLIP framework and incorporates a ViT-L/14 architecture trained on carefully curated data from the DataComp project. It's designed for research applications and demonstrates exceptional zero-shot classification capabilities.
- Trained on 1.4 billion samples from DataComp-1B dataset
- Implements Vision Transformer Large/14 architecture
- Achieves 79.2% zero-shot accuracy on ImageNet-1k
- Extensively evaluated on 38 different datasets
Core Capabilities
- Zero-shot image classification
- Image and text retrieval
- Foundation for downstream task fine-tuning
- Image generation guidance and conditioning
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its training on the carefully curated DataComp-1B dataset and its impressive zero-shot classification performance. The combination of the ViT-L/14 architecture with advanced training methodologies makes it particularly effective for research applications.
Q: What are the recommended use cases?
The model is primarily intended for research purposes, particularly in zero-shot image classification and multi-modal learning research. It's not recommended for deployment in production environments without thorough testing and evaluation. Specific use cases include research in image classification, retrieval systems, and foundation model studies.
