Total runs:
15.9M
Run Growth:
700.8K
Growth Rate:
4.36%
Sponsored by
YouArt
nomic-ai / nomic-embed-vision-v1.5
Model's Last Updated: April 01 2025
image-feature-extraction
Introduction of nomic-embed-vision-v1.5
Model Details of nomic-embed-vision-v1.5
nomic-embed-vision-v1.5: Expanding the Latent Space
nomic-embed-vision-v1.5
is a high performing vision embedding model that shares the same embedding space as
nomic-embed-text-v1.5
.
All Nomic Embed Text models are now multimodal !
| Name | Imagenet 0-shot | Datacomp (Avg. 38) | MTEB |
|---|---|---|---|
nomic-embed-vision-v1.5
|
71.0 | 56.8 | 62.28 |
nomic-embed-vision-v1
|
70.7 | 56.7 | 62.39 |
| OpenAI CLIP ViT B/16 | 68.3 | 56.3 | 43.82 |
| Jina CLIP v1 | 59.1 | 52.2 | 60.1 |
Hosted Inference API
The easiest way to get started with Nomic Embed is through the Nomic Embedding API.
Generating embeddings with the
nomic
Python client is as easy as
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)
For more information, see the API reference
Data Visualization
Click the Nomic Atlas map below to visualize a 100,000 sample CC3M comparing the Vision and Text Embedding Space!
Training Details
We align our vision embedder to the text embedding by employing a technique similar to LiT but instead lock the text embedder!
For more details, see the Nomic Embed Vision Technical Report (soon to be released!) and corresponding blog post
Training code is released in the
contrastors
repository
Usage
Remember
nomic-embed-text
requires
prefixes and so, when using Nomic Embed in multimodal RAG scenarios (e.g. text to image retrieval),
you should use the
search_query:
prefix.
Transformers
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel, AutoImageProcessor
from PIL import Image
import requests
processor = AutoImageProcessor.from_pretrained("nomic-ai/nomic-embed-vision-v1.5")
vision_model = AutoModel.from_pretrained("nomic-ai/nomic-embed-vision-v1.5", trust_remote_code=True)
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(image, return_tensors="pt")
img_emb = vision_model(**inputs).last_hidden_state
img_embeddings = F.normalize(img_emb[:, 0], p=2, dim=1)
Additionally, you can perform multimodal retrieval!
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
sentences = ['search_query: What are cute animals to cuddle with?', 'search_query: What do cats look like?']
tokenizer = AutoTokenizer.from_pretrained('nomic-ai/nomic-embed-text-v1.5')
text_model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1.5', trust_remote_code=True)
text_model.eval()
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = text_model(**encoded_input)
text_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
text_embeddings = F.layer_norm(text_embeddings, normalized_shape=(text_embeddings.shape[1],))
text_embeddings = F.normalize(text_embeddings, p=2, dim=1)
print(torch.matmul(img_embeddings, text_embeddings.T))
Join the Nomic Community
- Nomic: https://nomic.ai
- Discord: https://discord.gg/myY5YDR8z8
- Twitter: https://twitter.com/nomic_ai
Runs of nomic-ai nomic-embed-vision-v1.5 on huggingface.co
77.5K
Total runs
0
24-hour runs
-953
3-day runs
-3.0K
7-day runs
13.0K
30-day runs
More Information About nomic-embed-vision-v1.5 huggingface.co Model
Url of nomic-embed-vision-v1.5
nomic-embed-vision-v1.5 huggingface.co Url
Provider of nomic-embed-vision-v1.5 huggingface.co
Other API from nomic-ai
Updated:April 07 2026
Total runs:
3.5M
Run Growth:
-1.1M
Growth Rate:
-31.40%
Total runs:
2.1M
Run Growth:
925.1K
Growth Rate:
44.28%
Total runs:
409.2K
Run Growth:
154.9K
Growth Rate:
37.85%
Total runs:
199.2K
Run Growth:
-176.6K
Growth Rate:
-88.66%
Total runs:
164.1K
Run Growth:
30.3K
Growth Rate:
19.50%
Total runs:
50.6K
Run Growth:
40.6K
Growth Rate:
80.22%
Total runs:
7.2K
Run Growth:
1.1K
Growth Rate:
16.00%
Total runs:
2.7K
Run Growth:
-181
Growth Rate:
-6.65%
Total runs:
2.7K
Run Growth:
-306
Growth Rate:
-11.33%
Total runs:
2.4K
Run Growth:
2.2K
Growth Rate:
93.38%
Total runs:
2.4K
Run Growth:
1.1K
Growth Rate:
46.71%
Total runs:
1.5K
Run Growth:
686
Growth Rate:
44.43%
Total runs:
1.1K
Run Growth:
-309
Growth Rate:
-28.40%
Total runs:
1.1K
Run Growth:
-381
Growth Rate:
-35.74%
Total runs:
607
Run Growth:
-49
Growth Rate:
-8.02%
Total runs:
164
Run Growth:
81
Growth Rate:
49.09%
Total runs:
141
Run Growth:
6
Growth Rate:
4.26%
Total runs:
119
Run Growth:
-90
Growth Rate:
-78.26%
Total runs:
50
Run Growth:
7
Growth Rate:
14.00%
Total runs:
50
Run Growth:
6
Growth Rate:
12.00%
Total runs:
48
Run Growth:
3
Growth Rate:
6.25%
Total runs:
48
Run Growth:
4
Growth Rate:
8.33%
Total runs:
48
Run Growth:
7
Growth Rate:
14.58%
Total runs:
47
Run Growth:
2
Growth Rate:
4.26%
Total runs:
47
Run Growth:
5
Growth Rate:
10.64%
Total runs:
47
Run Growth:
1
Growth Rate:
2.13%
Total runs:
46
Run Growth:
2
Growth Rate:
4.35%
Total runs:
46
Run Growth:
3
Growth Rate:
6.52%
Total runs:
46
Run Growth:
3
Growth Rate:
6.38%
Total runs:
46
Run Growth:
4
Growth Rate:
8.70%
Total runs:
46
Run Growth:
5
Growth Rate:
10.87%
Total runs:
46
Run Growth:
5
Growth Rate:
10.64%
Total runs:
46
Run Growth:
5
Growth Rate:
10.87%
Total runs:
45
Run Growth:
-2
Growth Rate:
-4.44%
Total runs:
45
Run Growth:
0
Growth Rate:
0.00%
Total runs:
45
Run Growth:
0
Growth Rate:
0.00%
Total runs:
44
Run Growth:
1
Growth Rate:
2.27%
Total runs:
44
Run Growth:
2
Growth Rate:
4.55%
Total runs:
44
Run Growth:
-2
Growth Rate:
-4.55%
Total runs:
44
Run Growth:
2
Growth Rate:
4.44%
Total runs:
43
Run Growth:
-1
Growth Rate:
-2.33%
Total runs:
42
Run Growth:
-3
Growth Rate:
-7.14%
Total runs:
30
Run Growth:
-55
Growth Rate:
-196.43%
Total runs:
27
Run Growth:
-11
Growth Rate:
-40.74%
Total runs:
24
Run Growth:
3
Growth Rate:
12.50%
Total runs:
24
Run Growth:
6
Growth Rate:
25.00%
Total runs:
22
Run Growth:
2
Growth Rate:
9.09%
Total runs:
17
Run Growth:
-3
Growth Rate:
-17.65%
Total runs:
17
Run Growth:
1
Growth Rate:
5.88%
Total runs:
16
Run Growth:
-1
Growth Rate:
-6.25%
Total runs:
12
Run Growth:
-10
Growth Rate:
-83.33%
Total runs:
11
Run Growth:
-5
Growth Rate:
-45.45%
Total runs:
10
Run Growth:
-4
Growth Rate:
-40.00%
Total runs:
8
Run Growth:
-9
Growth Rate:
-128.57%
Total runs:
0
Run Growth:
-2
Growth Rate:
0.00%