nomic-embed-vision-v1
| Property | Value |
|---|---|
| Parameter Count | 92.9M |
| License | CC-BY-NC-4.0 |
| Paper | LiT Paper |
| Tensor Type | F32 |
What is nomic-embed-vision-v1?
nomic-embed-vision-v1 is a powerful vision embedding model designed to share the same embedding space as nomic-embed-text-v1, enabling seamless multimodal capabilities. The model demonstrates exceptional performance, achieving 70.7% accuracy on ImageNet zero-shot classification and 62.39% on MTEB, outperforming competitors like OpenAI CLIP ViT B/16 and Jina CLIP v1.
Implementation Details
The model employs a technique similar to Learning-in-Transfer (LiT), with a unique approach of locking the text embedder during training. It's implemented using the Transformers library and supports both image feature extraction and multimodal retrieval tasks.
- Supports both image and text embedding in the same latent space
- Optimized for zero-shot classification tasks
- Includes built-in normalization and attention mechanisms
- Provides easy integration through the Nomic Python client
Core Capabilities
- High-performance image feature extraction
- Multimodal retrieval capabilities
- Zero-shot classification with 70.7% ImageNet accuracy
- Seamless integration with text embeddings
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to share the same embedding space with text models while maintaining high performance on vision tasks sets it apart. Its architecture allows for efficient multimodal applications without compromising on individual task performance.
Q: What are the recommended use cases?
The model excels in image-text retrieval, zero-shot classification, and general image feature extraction. It's particularly suitable for building multimodal search systems, content recommendation engines, and visual similarity applications.
