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Field	Response
Participation considerations from adversely impacted groups protected classes in model design and testing:	None
Measures taken to mitigate against unwanted bias:	None

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C-RADIOv2 Overview

Description:

Cradiov2 model card, nSpect, subcards\nC-RADIOv2 specializes in visual feature extraction. For instance, C-RADIOv2 generates image embeddings that can be used by a downstream model to classify images.\nThe C-RADIOv2 models are available in multiple sizes:

Base (90M parameters).
Large (320M parameters).
Huge (653M parameters).
Gigantic (1.8B parameters).

C-RADIOv2 was trained for 1M steps (400k more steps than v1), using:\nInverse frequency sampling for data balancing\nPHI Standardization for teacher distribution balance\nThe model is ready for commercial and non-commercial use.

License/Terms of Use

License to use these models is covered by the NVIDIA Open Model License. By downloading the model, you accept the terms and conditions of these licenses.

Deployment Geography:

Global.

Use Case:

C-RADIOv2 is designed for developers and researchers to extract visual features at scale (via image embeddings) for image classification.

Release Date:

NGC 04/15/2025

References:

AM-RADIO: Agglomerative Vision Foundation Model -- Reduce All Domains Into One
\nPHI-S: Distribution Balancing for Label-Free Multi-Teacher Distillation
\nRADIOv2.5: Improved Baselines for Agglomerative Vision Foundation Models

Model Architecture:

Architecture Type: Neural Network\nNetwork Architecture: Vision Transformer

This model has 4 versions:

ViT-B: 90 million model parameters
ViT-L: 320 million model parameters
ViT-H: 653 million model parameters
ViT-G: 1.8 billion model parameters

Input:

Input Type: Image
\nInput Format: Red, Green, Blue (RGB)
\nInput Parameters: Two Dimensional (2D)
\nOther Properties Related to Input: Image resolutions up to 2048x2028 in increments of 16 pixels

Output:

Output Type: Embeddings
\nOutput Format: Tensor
\nOutput Parameters: Two Dimensional (2D)
\nOther Properties Related to Output: Downstream model required to leverage image features

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration:

Runtime Engine :\nTAO 6.0.0

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Blackwell
NVIDIA Jetson
NVIDIA Hopper
NVIDIA Lovelace
NVIDIA Pascal
NVIDIA Turing

Operating System(s):

Linux

Model Version(s):

C-RADIOv2-B (90M parameters).
C-RADIOv2-L (320M parameters).
C-RADIOv2-H (653M parameters).
C-RADIOv2-G (1.8B parameters).

Training Dataset:

Data Collection Method by dataset
\nAutomated

Labeling Method by dataset
\nNot Applicable (no labels are needed)

Properties
\n700 Million Image-Text pairs NV Internal Data

Methodology and KPI

Benchmark

The key performance indicator is accuracy, following the standard evaluation protocol for image classification. The KPIs for the evaluation data are reported below:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Model	Input Resolution	Accuracy on Set	Top-1 Accuracy	Top-5 Accuracy
C-RADIOv2-B	432	ImageNet validation	83.26%	96.79%
C-RADIOv2-L	432	ImageNet validation	86.13%	97.73%
C-RADIOv2-H	432	ImageNet validation	87.27%	98.12%

Inference:

Engine: PyTorch

Test Hardware: A100

Ethical Considerations

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 internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report security vulnerabilities or NVIDIA AI Concerns here.

","resourceVersions",[139,140,141,142,143],"resourceVersionsTotal",4,"hasMoreVersions","versionMetadata",{"_111":77,"_112":-5,"_37":113,"_114":-5,"_115":116,"_117":118,"_119":120,"_121":122,"_123":110,"_124":-5,"_125":-5,"_126":20,"_127":-5,"_128":20,"_129":-5,"_130":20,"_131":-5,"_132":20,"_133":-5,"_134":20,"_135":-5,"_136":20,"_137":138},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/tao/models/cradiov2/-","version","cradiov2-l","resolvedVersion","imageReference","registry","targetPath","cliDownloadCommand","ngc registry model download-version nvidia/tao/cradiov2:cradiov2-l","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/tao/models/cradiov2/cradiov2-l/version-history","accessCheckResult",{"_109":110},"hasRelatedCollections","showNimMetadataTab","context",{"_105":106,"_107":108},1793021865522,[],[],[97,98,99,100,101,102],{"_33":47,"_35":52,"_37":53,"_39":54,"_41":55,"_43":44},{"_33":47,"_35":51,"_37":53,"_39":54,"_41":55,"_43":44},{"_33":47,"_35":50,"_37":53,"_39":54,"_41":104,"_43":44},{"_33":47,"_35":48,"_37":53,"_39":54,"_41":103,"_43":44},{"_33":45,"_35":46,"_37":38,"_39":40,"_41":42,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_43":44},48,50,"orgName","nvidia","teamName","tao","allowed",true,"versionId","description","UPLOAD_COMPLETE","releaseType","createdDate",["D",1746822476528],"updatedDate",["D",1746822487710],"totalSizeInBytes",1279619576,"totalFileCount",1,"isSigned","digest","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_144":153,"_146":154,"_114":-5,"_123":110,"_126":20,"_137":152,"_128":20},{"_144":151,"_146":151,"_114":-5,"_123":110,"_126":20,"_137":152,"_128":20},{"_144":77,"_146":77,"_114":-5,"_123":110,"_126":20,"_137":150,"_128":20},{"_144":148,"_146":148,"_114":-5,"_123":110,"_126":20,"_137":149,"_128":20},{"_144":145,"_146":145,"_114":-5,"_123":110,"_126":20,"_137":147,"_128":20},"id","cradiov2-g","name",[],"cradiov2-h",[],[],"cradiov2-b",[],"-","Latest (cradiov2-b)","nvidia/tao/cradiov2","key","nvidia/tao/models/cradiov2","resourceType","models","resourceId","cradiov2","shortDescription","TAO Commercial Pretrained C-RADIOv2 Model","# C-RADIOv2 Overview\n\n## Description:\n\nCradiov2 model card, nSpect, subcards\nC-RADIOv2 specializes in visual feature extraction. For instance, C-RADIOv2 generates image embeddings that can be used by a downstream model to classify images.\nThe C-RADIOv2 models are available in multiple sizes:\n* Base (90M parameters).\n* Large (320M parameters).\n* Huge (653M parameters).\n* Gigantic (1.8B parameters).\n\nC-RADIOv2 was trained for 1M steps (400k more steps than v1), using:\nInverse frequency sampling for data balancing \n[PHI Standardization](https://arxiv.org/abs/2410.01680) for teacher distribution balance\nThe model is ready for commercial and non-commercial use.\n\n### License/Terms of Use \nLicense to use these models is covered by the NVIDIA Open Model License. By downloading the model, you accept the terms and conditions of these [licenses](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\n### Deployment Geography:\nGlobal.\n\n### Use Case: \nC-RADIOv2 is designed for developers and researchers to extract visual features at scale (via image embeddings) for image classification.\n\n### Release Date:
\nNGC 04/15/2025
\n\n## References:
\n[AM-RADIO: Agglomerative Vision Foundation Model -- Reduce All Domains Into One](https://arxiv.org/abs/2312.06709)
\n[PHI-S: Distribution Balancing for Label-Free Multi-Teacher Distillation](https://arxiv.org/abs/2410.01680)
\n[RADIOv2.5: Improved Baselines for Agglomerative Vision Foundation Models](https://arxiv.org/abs/2412.07679)
\n\n## Model Architecture: \n**Architecture Type:** Neural Network \n**Network Architecture:** Vision Transformer\n\n**This model has 4 versions:**\n- **ViT-B**: 90 million model parameters \n- **ViT-L**: 320 million model parameters \n- **ViT-H**: 653 million model parameters \n- **ViT-G**: 1.8 billion model parameters\n\n\n## Input:\n**Input Type:** Image
\n**Input Format:** Red, Green, Blue (RGB)
\n**Input Parameters:** Two Dimensional (2D)
\n**Other Properties Related to Input:** Image resolutions up to 2048x2028 in increments of 16 pixels
\n\n\n\n## Output: \n**Output Type:** Embeddings
\n**Output Format:** Tensor
\n**Output Parameters:** Two Dimensional (2D)
\n**Other Properties Related to Output:** Downstream model required to leverage image features
\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n\n## Software Integration:\n**Runtime Engine** : \nTAO 6.0.0\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Ampere
\n* NVIDIA Blackwell
\n* NVIDIA Jetson
\n* NVIDIA Hopper
\n* NVIDIA Lovelace
\n* NVIDIA Pascal
\n* NVIDIA Turing
\n\n**Operating System(s):**
\n* Linux\n\n## Model Version(s):
\n* C-RADIOv2-B (90M parameters).\n* C-RADIOv2-L (320M parameters).\n* C-RADIOv2-H (653M parameters).\n* C-RADIOv2-G (1.8B parameters).\n \n# Training Dataset: \n\n## Training Dataset: \n\n**Data Collection Method by dataset**
\nAutomated\n\n**Labeling Method by dataset**
\nNot Applicable (no labels are needed)\n\n**Properties**
\n700 Million Image-Text pairs NV Internal Data\n\n## Methodology and KPI\n\nBenchmark\n\nThe key performance indicator is accuracy, following the standard evaluation protocol for image classification. The KPIs for the evaluation data are reported below:\n\n| Model | Input Resolution | Accuracy on Set | Top-1 Accuracy | Top-5 Accuracy |\n|-------------------|-------------------|----------------------|----------------|----------------|\n| C-RADIOv2-B | 432 | ImageNet validation | 83.26% | 96.79% |\n| C-RADIOv2-L | 432 | ImageNet validation | 86.13% | 97.73% |\n| C-RADIOv2-H | 432 | ImageNet validation | 87.27% | 98.12% |\n\n\n## Inference:\n**Engine:** PyTorch
\n\n\n**Test Hardware:** A100
\n\n## Ethical Considerations\nNVIDIA 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 internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName",["D",1746822498413],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/cradiov2","rawImageUrl","","canGuestDownload","latestVersionSizeInBytes",392981640,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","Field | Response\n:---------------------------------------------------------------------------------------------------|:---------------\nParticipation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None\nMeasures taken to mitigate against unwanted bias: | None","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Domain: | Visual Feature Extraction\nModel Type: | Vision Transformer\nIntended Users: | Developers of downstream vision applications\nOutput: | Image embeddings\nDescribe how the model works: | The model takes an image as input, processes the image through multiple transformer blocks, and outputs summary and patch embeddings.\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable\nTechnical Limitations & Mitigation: | This model generates image embeddings that can be used by a downstream model to, for example, classify images. The downstream model must be trained to leverage the visual embeddings.\nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | Image classification accuracy\nPotential Known Risks: | This model is only tested on input resolutions ranging from 256 to 2048, in increments of 16 pixels. Additionally, the generated embeddings might fail to disambiguate differences that appear evident to humans (e.g. two images showing different breeds of dogs might in fact produce very similar embeddings). Domain-specific evaluation is required for the target application.\nLicensing: | [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) |","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personal data? | None\nPersonal data used to create this model? | None\nHow often is dataset reviewed? | Before Release\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes\nIs data compliant with data subject requests for data correction or removal, if such a request was made? | Yes\nApplicable Privacy Poicy | https://www.nvidia.com/en-us/about-nvidia/privacy-policy/","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nModel Application(s): | Generation of visual embeddings\nDescribe the life critical impact (if present). | Not Applicable\nUse Case Restrictions: | Abide by [NVIDIA Software and Model Evaluation License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-and-model-evaluation-license/)\nModel and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. 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This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","This cookie is associated with Akamai. 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