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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",[135,136,137,138,139],"resourceVersionsTotal",4,"hasMoreVersions","versionMetadata",{"_108":78,"_109":-5,"_37":110,"_111":-5,"_112":113,"_114":115,"_116":117,"_118":119,"_120":107,"_121":-5,"_122":20,"_123":-5,"_124":20,"_125":-5,"_126":20,"_127":-5,"_128":20,"_129":-5,"_130":20,"_131":-5,"_132":20,"_133":134},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/tao/models/cradiov2/-","version","cradiov2-g","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/tao/cradiov2:cradiov2-g","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/tao/models/cradiov2/cradiov2-g/version-history","accessCheckResult",{"_106":107},"hasRelatedCollections","context",{"_102":103,"_104":105},1789346163025,[],[],[94,95,96,97,98,99],{"_33":48,"_35":53,"_37":54,"_39":55,"_41":56,"_43":44},{"_33":48,"_35":52,"_37":54,"_39":55,"_41":101,"_43":44},{"_33":48,"_35":51,"_37":54,"_39":55,"_41":100,"_43":44},{"_33":48,"_35":49,"_37":54,"_39":55,"_41":100,"_43":44},{"_33":45,"_35":46,"_37":38,"_39":40,"_41":47,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_43":44},49,48,"orgName","nvidia","teamName","tao","allowed",true,"versionId","description","UPLOAD_COMPLETE","releaseType","createdDate",["D",1746822471920],"updatedDate",["D",1746822485000],"totalSizeInBytes",4638631332,"totalFileCount",1,"isSigned","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_140":149,"_141":150,"_111":-5,"_120":107,"_122":20,"_133":148,"_124":20},{"_140":147,"_141":147,"_111":-5,"_120":107,"_122":20,"_133":148,"_124":20},{"_140":145,"_141":145,"_111":-5,"_120":107,"_122":20,"_133":146,"_124":20},{"_140":143,"_141":143,"_111":-5,"_120":107,"_122":20,"_133":144,"_124":20},{"_140":78,"_141":78,"_111":-5,"_120":107,"_122":20,"_133":142,"_124":20},"id","name",[],"cradiov2-h",[],"cradiov2-l",[],"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 tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554],[501,502,503,504,505],{"_340":415,"_161":415,"_109":171,"_314":525},{"_340":389,"_161":389,"_109":171,"_314":520},{"_340":515,"_161":515,"_109":171,"_314":516},{"_340":510,"_161":510,"_109":171,"_314":511},{"_340":506,"_161":506,"_109":171,"_314":507},"nvidia.partners",[508],{"_141":509,"_346":506,"_347":20,"_109":383},"edge","app.hushly.com",[512],{"_141":513,"_346":510,"_347":20,"_109":514},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[517],{"_141":518,"_346":515,"_347":20,"_109":519},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641],[593,594,595,596],{"_340":608,"_161":608,"_109":171,"_314":609},{"_340":603,"_161":603,"_109":171,"_314":604},{"_340":402,"_161":402,"_109":171,"_314":600},{"_340":515,"_161":515,"_109":171,"_314":597},[598],{"_141":599,"_346":515,"_347":107,"_109":519},"_gh_sess",[601],{"_141":602,"_346":402,"_347":107,"_109":409},"YSC","bizible.com",[605],{"_141":606,"_346":603,"_347":20,"_109":607},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[610],{"_141":611,"_346":608,"_347":20,"_109":612},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_141":683,"_346":461,"_347":20,"_109":684},{"_141":668,"_346":682,"_347":107,"_109":669},{"_141":651,"_346":464,"_347":20,"_109":576},{"_141":651,"_346":564,"_347":20,"_109":576},{"_141":680,"_346":461,"_347":20,"_109":681},{"_141":668,"_346":467,"_347":107,"_109":669},{"_141":678,"_346":461,"_347":20,"_109":679},{"_141":668,"_346":464,"_347":107,"_109":669},{"_141":676,"_346":563,"_347":20,"_109":677},{"_141":668,"_346":585,"_347":107,"_109":669},{"_141":648,"_346":464,"_347":20,"_109":650},{"_141":674,"_346":461,"_347":107,"_109":675},{"_141":672,"_346":461,"_347":20,"_109":673},{"_141":670,"_346":461,"_347":107,"_109":671},{"_141":668,"_346":559,"_347":107,"_109":669},{"_141":666,"_346":461,"_347":20,"_109":667},{"_141":664,"_346":461,"_347":20,"_109":665},{"_141":662,"_346":461,"_347":107,"_109":663},{"_141":660,"_346":568,"_347":20,"_109":661},{"_141":648,"_346":559,"_347":20,"_109":650},{"_141":658,"_346":461,"_347":20,"_109":659},{"_141":656,"_346":461,"_347":107,"_109":657},{"_141":654,"_346":461,"_347":20,"_109":655},{"_141":652,"_346":461,"_347":20,"_109":653},{"_141":651,"_346":461,"_347":20,"_109":576},{"_141":648,"_346":649,"_347":20,"_109":650},{"_141":646,"_346":461,"_347":20,"_109":647},{"_141":644,"_346":461,"_347":107,"_109":645},{"_141":642,"_346":461,"_347":20,"_109":643},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. 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