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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",[190,191,192,193,194],"resourceVersionsTotal",4,"hasMoreVersions","versionMetadata",{"_165":21,"_166":-5,"_48":167,"_168":-5,"_169":170,"_171":172,"_173":80,"_174":16,"_175":86,"_176":-5,"_177":30,"_178":-5,"_179":30,"_180":-5,"_181":30,"_182":-5,"_183":30,"_184":-5,"_185":30,"_186":-5,"_187":30,"_188":189},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/tao/models/cradiov2/-","version","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/tao/cradiov2:cradiov2-l","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/tao/models/cradiov2/cradiov2-l/version-history","accessCheckResult",{"_164":86},"hasRelatedCollections","context",{"_160":161,"_162":163},[],[],[154,155,156,157,158,159],{"_44":59,"_46":70,"_48":61,"_50":62,"_52":71,"_54":55},{"_44":59,"_46":68,"_48":61,"_50":62,"_52":69,"_54":55},{"_44":59,"_46":66,"_48":61,"_50":62,"_52":67,"_54":55},{"_44":59,"_46":60,"_48":61,"_50":62,"_52":63,"_54":55},{"_44":56,"_46":57,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55},"orgName","nvidia","teamName","tao","allowed","versionId","description","UPLOAD_COMPLETE","releaseType","createdDate",["D",1746822476528],"updatedDate",["D",1746822487710],"totalSizeInBytes","totalFileCount","isSigned","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_76":202,"_78":203,"_168":-5,"_175":86,"_177":30,"_188":201,"_179":30},{"_76":200,"_78":200,"_168":-5,"_175":86,"_177":30,"_188":201,"_179":30},{"_76":21,"_78":21,"_168":-5,"_175":86,"_177":30,"_188":199,"_179":30},{"_76":197,"_78":197,"_168":-5,"_175":86,"_177":30,"_188":198,"_179":30},{"_76":195,"_78":195,"_168":-5,"_175":86,"_177":30,"_188":196,"_179":30},"cradiov2-g",[],"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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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533],[372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388],{"_389":495,"_214":495,"_166":224,"_362":496},{"_389":490,"_214":490,"_166":224,"_362":491},{"_389":486,"_214":486,"_166":224,"_362":487},{"_389":481,"_214":481,"_166":224,"_362":482},{"_389":476,"_214":476,"_166":224,"_362":477},{"_389":464,"_214":464,"_166":224,"_362":465},{"_389":452,"_214":452,"_166":224,"_362":453},{"_389":447,"_214":447,"_166":224,"_362":448},{"_389":436,"_214":436,"_166":224,"_362":437},{"_389":431,"_214":431,"_166":224,"_362":432},{"_389":426,"_214":426,"_166":224,"_362":427},{"_389":421,"_214":421,"_166":224,"_362":422},{"_389":416,"_214":416,"_166":224,"_362":417},{"_389":411,"_214":411,"_166":224,"_362":412},{"_389":406,"_214":406,"_166":224,"_362":407},{"_389":399,"_214":399,"_166":224,"_362":400},{"_389":390,"_214":390,"_166":224,"_362":391},"hostName","amazon-adsystem.com",[392,393],{"_78":398,"_395":390,"_396":30,"_166":397},{"_78":394,"_395":390,"_396":30,"_166":397},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[401,402],{"_78":405,"_395":399,"_396":30,"_166":404},{"_78":403,"_395":399,"_396":30,"_166":404},"IDE","This domain is owned by Doubleclick (Google). The main business activity is: Doubleclick is Googles real time bidding advertising exchange","test_cookie","bing.com",[408],{"_78":409,"_395":406,"_396":30,"_166":410},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[413],{"_78":414,"_395":411,"_396":30,"_166":415},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","If you know anything about this host and its uses please add your contributions here. ","tiktok.com",[418],{"_78":419,"_395":416,"_396":30,"_166":420},"_ttp","TikTok is a social media platform owned by ByteDance, known for short-form video sharing. The service is used to create and share videos, and it utilizes cookies for various purposes including advertising and analytics.","t.co",[423],{"_78":424,"_395":421,"_396":30,"_166":425},"muc_ads","This domain is owned by Twitter. It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[428],{"_78":429,"_395":426,"_396":30,"_166":430},"sp","Testing","google.com",[433],{"_78":434,"_395":431,"_396":30,"_166":435},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","tags.srv.stackadapt.com",[438,439,440,441],{"_78":446,"_395":436,"_396":30,"_166":443},{"_78":445,"_395":436,"_396":30,"_166":443},{"_78":444,"_395":436,"_396":30,"_166":443},{"_78":442,"_395":436,"_396":30,"_166":443},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[449],{"_78":450,"_395":447,"_396":30,"_166":451},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. For this reason it is classified as a primarily tracking/targeting domain.","twitter.com",[454,455,456,457],{"_78":463,"_395":452,"_396":30,"_166":459},{"_78":461,"_395":452,"_396":30,"_166":462},{"_78":460,"_395":452,"_396":30,"_166":459},{"_78":458,"_395":452,"_396":30,"_166":459},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[466,467,468,469],{"_78":475,"_395":464,"_396":30,"_166":473},{"_78":474,"_395":464,"_396":30,"_166":473},{"_78":472,"_395":464,"_396":30,"_166":473},{"_78":470,"_395":464,"_396":30,"_166":471},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[478],{"_78":479,"_395":476,"_396":30,"_166":480},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[483],{"_78":484,"_395":481,"_396":30,"_166":485},"receive-cookie-deprecation","This domain is owned by im-apps.net which provides information and resources about various instant messaging (IM) applications to help users make informed decisions based on their communication needs.","influ2.com",[488],{"_78":489,"_395":486,"_396":30,"_166":430},"R","6sc.co",[492],{"_78":493,"_395":490,"_396":30,"_166":494},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","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.",[621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641],[586,587,588,589,590],{"_389":481,"_214":481,"_166":224,"_362":612},{"_389":447,"_214":447,"_166":224,"_362":607},{"_389":600,"_214":600,"_166":224,"_362":601},{"_389":596,"_214":596,"_166":224,"_362":597},{"_389":591,"_214":591,"_166":224,"_362":592},"nvcrm.my.site.com",[593],{"_78":594,"_395":591,"_396":30,"_166":595},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[598],{"_78":599,"_395":596,"_396":30,"_166":430},"edge","github.com",[602,603],{"_78":606,"_395":600,"_396":30,"_166":605},{"_78":604,"_395":600,"_396":30,"_166":605},"logged_in","This domain is owned by Github. 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