---
title: "nvclip"
publisher: "nvidia"
type: "endpoint"
updated: "2025-06-13T14:24:45.242Z"
description: "NV-CLIP is a multimodal embeddings model for image and text."
canonical: "https://build.nvidia.com/nvidia/nvclip"
---

# NV-CLIP (Commercial Foundation Model)

## Model Overview
NV-CLIP is a multimodal embeddings model for image and text. Trained on 700M proprietary images, NV-CLIP is the NVIDIA commercial version of OpenAI’s CLIP (Contrastive Language-Image Pre-Training) model. NV-CLIP can be applied to various areas such as multimodal search, zero-shot image classification, and downstream computer vision tasks such as object detection and more.

## Getting Started with NV-CLIP NIM microservice

Deploying and integrating NV-CLIP NIM microservice is straightforward and based on industry standard APIs. See the NV-CLIP NIM microservice documentation to get started.

## Applications

- Multimodal search: Enable accurate image and text search to quickly search database of images and videos.

- Zero-shot and few-shot inference: Classify images without re-training or fine-tuning.

- Downstream vision tasks: Use the embeddings to enable downstream complex vision AI tasks such as segmentation, detection, VLMs and more.

## References:

- Radford, Alec, et al. "Learning transferable visual models from natural language supervision." International conference on machine learning. PMLR, 2021.

## Model Architecture: 
**Architecture Type:** Transformer-Based  <br>

NV-CLIP as a backbone can be used towards various downstream tasks such as classification, detection, segmentation and text based image retrieval.

## Input:
**Input Type(s):** Images, Texts <br>
**Input Format(s):** List of Red, Green, Blue (RGB) Images or Strings <br>
**Other Properties Related to Input:**  <br>

Channel Ordering of the Input: NCHW, where N = Batch Size, C = number of channels (3), H = Height of images (224), W = Width of the images (224)

## Output:
**Output Type(s):** Float tensor <br>
**Output Format:** 3D Tensor <br>
**Other Properties Related to Output:** <br> 
The output of this model is an embedding of an input image or text of size 1024 for ViT-H variant.

**Supported Operating System(s):** <br>
* Linux <br>

## Model Version(s): 
- **nv_clip_224_vit_h_trainable_v1.0** - NV-CLIP ViT-H with 224 resolution is foundation model and is trainable.

## Using this Model <a class="anchor" name="how_to_use_this_model"></a>

These models need to be used with NVIDIA hardware and software. These models can only be used with NV-CLIP NIM microservice.

The primary use case for these models is getting feature embeddings from images and text. These embeddings can then be used for curation, clustering, zero-shot or few-shot downstream tasks such as classification. These embeddings can also be used towards text and image-based image

## Training Dataset:

**Data Collection Method by dataset:** <br>
* Automated <br>

**Labeling Method by dataset:** <br>
* Automated <br>

**Properties:**  <br>

| Dataset | No. of Images |
|--|--|
|NV Internal Data| 700M | 

## Evaluation Dataset:

**Link:** [https://www.image-net.org/](https://www.image-net.org/)

**Data Collection Method by dataset:** <br>
* Unknown <br>

**Labeling Method by dataset:** <br>
* Unknown <br>

**Properties:**  <br>
50,000 validation images from [ImageNet dataset](https://www.image-net.org/download.php) 

#### Methodology and KPI

The performance of zero shot accuracy of NV-CLIP on ImageNet validation dataset.

| model                   | top-1 Accuracy |
| ----------------------- | -------------- |
| ViT-H-224              | 0.7786         |

## Ethical Considerations:
### Bias, Safety & Security, and Privacy

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards [here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/nvclip_vit/bias). Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).

### Special Training Data Considerations

The model was trained on publicly available data, which may contain toxic language and societal biases. Therefore, the model may amplify those biases, such as associating certain genders with specific social stereotypes.

#### Governing Terms 
The NIM container is governed by the NVIDIA AI Enterprise Software License Agreement | NVIDIA; and the use of this model is governed by the ai-foundation-models-community-license.pdf (nvidia.com).

**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**

## Bias

| Field | Response |
| -- | -- |
|Participation considerations from adversely impacted groups [(protected classes)](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None of the Above |
| Measures taken to mitigate against unwanted bias: | Not Applicable |

## Explainability

| Field              |Response  |
|:-------------------|:---------|
| Intended Application & Domain:|  Generating image embedding that is aligned with text for zero-shot classification. |
| Model Type:                   | Embedding Generation |
| Intended User:                | This model is intended for developers building search engines, classification, detection/ segmentation models. |
| Output:                       | Embedding (An array of float numbers, providing a dense vector representation for the input text or image)                        |
| Describe how the model works: | NV-CLIP consists of a vision and text encoder. The model maps both images and text into a unified embedding space.  |
| Verified to have met prescribed NVIDIA quality standards:| Yes|
| Performance Metrics:          | ImageNet zero-shot accuracy |
| Potential Known Risks:        | Unknown |
| Licensing:                    | [NVIDIA AI Foundation Models Community License](https://docs.nvidia.com/ai-foundation-models-community-license.pdf) |
| Technical Limitations:        | It may not perform well in classifying images of aircraft, geolocation features, and faces. |

## Privacy

| Field | Response |
| -- | -- |
| Generatable or reverse engineerable personally-identifiable information (PII)? | None |
| Protected classes used to create this model? | Not Applicable (No PII) |
| Was consent obtained for any PII used? | Not Applicable (No PII) |
| How often is dataset reviewed? | 	Before Release |
| Is a mechanism in place to honor data subject right of access or deletion of personal data? | No |
| If PII collected for the development of the model, was it collected directly by NVIDIA? |Not Applicable |
| If PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects?	| Not Applicable |
| If PII collected for the development of this AI model, was it minimized to only what was required? | Not Applicable |
| Is there provenance for all datasets used in training? | Yes |
| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |
| Is data compliant with data subject requests for data correction or removal, if such a request was made? | Yes |
| Applicable NVIDIA Privacy Policy	| [https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/) |

## Safety & Security

| Field | Response |
| -- | -- |
| Model Application(s): | Image Search |
| Describe the life-critical application (if present). | None: Not within Operational Design Domain |
| Use Case Restrictions: | Abide by [https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/"](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/)  |
| Describe access restrictions (if any): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development.  |

## Prototype

```bash
curl -X POST https://integrate.api.nvidia.com/v1/embeddings \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $NVIDIA_API_KEY" \
-d '{
"input": ["Image of a dog",
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"],
"model": "nvidia/nvclip",
"encoding_format": "float"
}'
```

```python
from openai import OpenAI

client = OpenAI(
api_key="$NVIDIA_API_KEY",
base_url="https://integrate.api.nvidia.com/v1"
)

response = client.embeddings.create(
input=[ "Image of a dog",
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"],
model="nvidia/nvclip",
encoding_format="float"
)

print(response.data[0].embedding)
```

```javascript
import OpenAI from 'openai';

const openai = new OpenAI({
apiKey: '$NVIDIA_API_KEY',
baseURL: 'https://integrate.api.nvidia.com/v1/',
})

async function main() {
const response = await openai.embeddings.create({
input: ["The quick brown fox jumped over the lazy dog",
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"
],
model: "nvidia/nvclip",
encoding_format: "float"
})

const embedding = response.data[0].embedding;
process.stdout.write(`${embedding}\n`);
}

main();
```