In today’s blog, we will dive into the fascinating world of multimodal AI and show you how to utilize the nomic-embed-vision-v1.5 model for image feature extraction. With the capability to link images and text seamlessly, this model opens up a myriad of opportunities for AI applications.

Understanding the Nomic Embed Vision v1.5

Imagine your brain’s ability to connect various pieces of knowledge when drawing pictures in your mind; the nomic-embed-vision-v1.5 does something similar but for images and text. This model operates in a shared embedding space with nomic-embed-text-v1.5, meaning that it can comprehend both visual and textual contexts while generating embeddings. Just as a painter uses different colors to create a masterpiece, this model utilizes multiple data inputs to produce meaningful embeddings.

Step-by-Step Guide

Let’s get started with extracting features from images using the Nomic Embed model.

1. Install the Required Library

• Ensure you have Python installed on your machine.
• Install the Nomic library via pip:

pip install nomic

2. Generate Image Embeddings

To generate embeddings for your images, you can use the following code snippet:

from nomic import embed
import numpy as np

output = embed.image(
    images=[
        "image_path_1.jpeg",
        "image_path_2.png",
    ],
    model='nomic-embed-vision-v1.5',
)

print(output['usage'])
embeddings = np.array(output['embeddings'])
print(embeddings.shape)

This code will output the usage statistics and the shape of the embeddings generated.

3. Visualize Your Data

For a deeper understanding of your embeddings, click the following link to visualize a comparison between the Vision and Text Embedding Space:

Nomic Atlas Map

Troubleshooting

If you encounter any issues while implementing Nomic Embed, here are some troubleshooting tips:

• Ensure that all required libraries are properly installed.
• Check your image paths to confirm they are correct and accessible.
• Inspect any error messages closely; they can guide you to the root cause of the issue.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

With Nomic Embed Vision v1.5, you hold the painting brush to create remarkable connections between images and text. By harnessing the power of this model, you can pave new paths in AI development and applications.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Exploring Further

Lastly, for those interested in delving deeper into the training techniques used with the Nomic Embed Vision model, you may refer to the Nomic Embed Vision Technical Report. Understanding the mechanics behind the model can greatly enhance your experience in using it!