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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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Description:

Polyp Detection RT-DETR is a RT-DETR model that is designed to detect polyps in colonoscopy images.\nThis model is ready for commercial use.

License/Terms of Use:

GOVERNING TERMS: Use of this model is governed by the NVIDIA Open Model License. Additional Information: Apache License Version 2.0.\nYou are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.

Reference(s):

The model is trained on the REAL-Colon dataset [1], utilizing the RT-DETR v2 architecture [2] and the ResNet50 backbone [3]. The backbone is pretrained on the NVImageNet dataset.

\n
[1] Biffi, Carlo, et al. \"REAL-Colon: A dataset for developing real-world AI applications in colonoscopy.\" Scientific Data 11.1 (2024): 539.
\n

\n
[2] Lv, Wenyu, et al. \"Rt-detrv2: Improved baseline with bag-of-freebies for real-time detection transformer.\" arXiv preprint arXiv:2407.17140 (2024).
\n

\n
[3] He, Kaiming, et al. \"Deep residual learning for image recognition.\" Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
\n

Model Architecture:

Architecture Type: Convolution Neural Network (CNN), Transformer
\nNetwork Architecture: RT DETR V2 with ResNet50 backbone

Input:

Input Type(s): Image
\nInput Format(s): Red, Green, Blue (RGB)
\nInput Parameters: Two-Dimensional (2D)
\nOther Properties Related to Input: Image Range Needed (640 x 640 x 3), Pre-Processing Needed (value range [0, 255])

Output:

Output Type(s): A dictionary which contains two keys: \"pred_logits\" and \"pred_boxes\"
\nOutput Format: The value of key \"pred_logits\" is a tensor with shape [300, 1]. The value of the key \"pred_boxes\" is a tensor with shape [300, 4]
\nOutput Parameters: Two-Dimensional (2D)
\nOther Properties Related to Output: The values of the key \"pred_boxes\" are in range [0, 1], which represents normalized coordinates in format [center_x, center_y, width, height] relative to the input image size.

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(s):

[Holoscan SDK 2.9.0]

Supported Hardware Microarchitecture Compatibility:

[NVIDIA Ampere]
[NVIDIA Hopper]
[NVIDIA Lovelace]
[NVIDIA Volta]

[Preferred/Supported] Operating System(s):

[Linux]

Model Version(s):

rtdetrv2_timm_r50_nvimagenet_pretrained_neg_finetune_bhwc

Training, Testing, and Evaluation Datasets:

The total size: 60 recordings
\nTotal number of datasets: 1
\nDataset partition: Training 66%, testing 14%, validation 20%

Training Dataset:

Link: https://plus.figshare.com/articles/media/REAL-colon_dataset/22202866
\nData Collection Method by dataset

[Human]

Labeling Method by dataset

[Human]

Properties: 40 recordings of real-world colonoscopies.

Testing Dataset:

Link: https://plus.figshare.com/articles/media/REAL-colon_dataset/22202866
\nData Collection Method by dataset

[Human]

Labeling Method by dataset

[Human]

Properties : 12 recordings of real-world colonoscopies.

Evaluation Dataset:

Link: https://plus.figshare.com/articles/media/REAL-colon_dataset/22202866
\nBenchmark Score
\nmAP@0.5:0.95: 0.301

Data Collection Method by dataset

[Human]

Labeling Method by dataset

[Human]

Properties: 8 recordings of real-world colonoscopies.

Inference:

Acceleration Engine: Tensor(RT)
\nTest Hardware:

V100
A100
H100
RTX 6000 Ada

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.\nUsers are responsible for ensuring the model-generated segmentations are appropriately evaluated and comply with applicable safety regulations and ethical standards. Please make sure you have proper rights and permissions for all input images, particularly for personal health information.

For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.

Please report security vulnerabilities or NVIDIA AI Concerns here.

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\nPolyp Detection RT-DETR is a RT-DETR model that is designed to detect polyps in colonoscopy images.\nThis model is ready for commercial use.\n \n### License/Terms of Use:
\nGOVERNING TERMS: Use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: [Apache License Version 2.0](https://github.com/lyuwenyu/RT-DETR/blob/main/LICENSE).\nYou are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.
\n \n## Reference(s):\nThe model is trained on the [REAL-Colon dataset](https://github.com/cosmoimd/real-colon-dataset) [1], utilizing the [RT-DETR v2](https://github.com/lyuwenyu/RT-DETR/tree/main/rtdetrv2_pytorch) architecture [2] and the [ResNet50](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/resnet.py) backbone [3]. The backbone is pretrained on the NVImageNet dataset.\n \n> [1] Biffi, Carlo, et al. \"REAL-Colon: A dataset for developing real-world AI applications in colonoscopy.\" Scientific Data 11.1 (2024): 539.\n \n> [2] Lv, Wenyu, et al. \"Rt-detrv2: Improved baseline with bag-of-freebies for real-time detection transformer.\" arXiv preprint arXiv:2407.17140 (2024).\n \n> [3] He, Kaiming, et al. \"Deep residual learning for image recognition.\" Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
\n \n## Model Architecture:
\n**Architecture Type:** Convolution Neural Network (CNN), Transformer
\n**Network Architecture:** RT DETR V2 with ResNet50 backbone
\n\n## Input:
\n**Input Type(s):** Image
\n**Input Format(s):** Red, Green, Blue (RGB)
\n**Input Parameters:** Two-Dimensional (2D)
\n**Other Properties Related to Input:** Image Range Needed (640 x 640 x 3), Pre-Processing Needed (value range [0, 255])
\n \n## Output:
\n**Output Type(s):** A dictionary which contains two keys: \"pred_logits\" and \"pred_boxes\"
\n**Output Format:** The value of key \"pred_logits\" is a tensor with shape [300, 1]. The value of the key \"pred_boxes\" is a tensor with shape [300, 4]
\n**Output Parameters:** Two-Dimensional (2D)
\n**Other Properties Related to Output:** The values of the key \"pred_boxes\" are in range [0, 1], which represents normalized coordinates in format [center_x, center_y, width, height] relative to the input image size.
\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(s):**
\n* [Holoscan SDK 2.9.0]
\n \n**Supported Hardware Microarchitecture Compatibility:**
\n* [NVIDIA Ampere]
\n* [NVIDIA Hopper]
\n* [NVIDIA Lovelace]
\n* [NVIDIA Volta]
\n \n**[Preferred/Supported] Operating System(s):**
\n* [Linux]
\n\n## Model Version(s):\nrtdetrv2_timm_r50_nvimagenet_pretrained_neg_finetune_bhwc
\n \n# Training, Testing, and Evaluation Datasets:
\n \n**The total size:** 60 recordings
\n**Total number of datasets:** 1
\n**Dataset partition:** Training 66%, testing 14%, validation 20%
\n\n## Training Dataset: \n**Link:** https://plus.figshare.com/articles/media/REAL-colon_dataset/22202866
\n**Data Collection Method by dataset**
\n* [Human]
\n\n**Labeling Method by dataset**
\n* [Human]
\n\n**Properties:** 40 recordings of real-world colonoscopies.
\n\n ## Testing Dataset:\n**Link:** https://plus.figshare.com/articles/media/REAL-colon_dataset/22202866
\n**Data Collection Method by dataset**
\n* [Human]
\n\n**Labeling Method by dataset**
\n* [Human]
\n\n**Properties :** 12 recordings of real-world colonoscopies.
\n\n## Evaluation Dataset:\n**Link:** https://plus.figshare.com/articles/media/REAL-colon_dataset/22202866
\n**Benchmark Score**
\n mAP@0.5:0.95: 0.301\n\n**Data Collection Method by dataset**
\n* [Human]
\n\n**Labeling Method by dataset**
\n* [Human]
\n\n**Properties:** 8 recordings of real-world colonoscopies.
\n \n## Inference:\n**Acceleration Engine:** Tensor(RT)
\n**Test Hardware:**
\n* V100
\n* A100
\n* H100
\n* RTX 6000 Ada
\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. \nUsers are responsible for ensuring the model-generated segmentations are appropriately evaluated and comply with applicable safety regulations and ethical standards. Please make sure you have proper rights and permissions for all input images, particularly for personal health information.
\n\nFor more detailed information on ethical considerations for this model, please see the Model Card++ [Explainability](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/holoscan/models/polyp_detection_rt_detr_model/explainability), [Bias](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/holoscan/models/polyp_detection_rt_detr_model/bias), [Safety & Security](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/holoscan/models/polyp_detection_rt_detr_model/safety-and-security), and [Privacy](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/holoscan/models/polyp_detection_rt_detr_model/privacy) Subcards.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","Polyp Detection RT DETR Model",["D",1749728201191],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/clara-holoscan/models/polyp_detection_rt_detr_model","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","| Field | Response |\n|-----------------------------------------------------------------------------------------------------|------------------|\n| Participation considerations from adversely impacted groups [(protected classes)](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None |\n| Measures taken to mitigate against unwanted bias: | None |","explainability","| Field | Response |\n|--------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|\n| Intended Domain: | Polyp Detection in Colonoscopy Images |\n| Model Type: | Object Detection |\n| Intended Users: | Intended for users that use models for polyp detection tasks. |\n| Output: | A dictionary which contains two keys: \"pred_logits\" and \"pred_boxes\". |\n| Describe how the model works: | The model processes a colonoscopy image to output predicted boxes around detected polyps and their associated logits. |\n| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable |\n| Technical Limitations & Mitigation: | Limitation: This model's detection performance may not be 100% accurate, particularly in instances with variable lighting conditions, motion blur from camera movement and different video resolutions. Mitigation: The model can be finetuned to adapt to local imaging conditions for better performance. |\n| Verified to have met prescribed NVIDIA quality standards: | Yes |\n| Performance Metrics: | Mean Average Precision |\n| Potential Known Risks: | The model may result in missed polyps (false negatives) or incorrect detections (false positive) in clinical scenarios. This model is only intended to support diagnostic purposes and should serve only as a supplementary screening aid to assist healthcare professionals in identifying areas of interest during colonoscopy procedures. |\n| Licensing: | GOVERNING TERMS: Use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: [Apache License Version 2.0](https://github.com/lyuwenyu/RT-DETR/blob/main/LICENSE). |","privacy","| Field | Response |\n|------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------|\n| Generatable or reverse engineerable personal data? | None |\n| Personal data used to create this model? | None |\n| How often is dataset reviewed? | Before Release |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | Not Applicable |\n| Applicable Privacy Policy | [https://www.nvidia.com/en-us/about-nvidia/privacy-policy/](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) |","safetyAndSecurity","| Field | Response |\n|-----------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------|\n| Model Application(s): | Object Detection for Polyp in Colonoscopy Images |\n| Model Application Field(s): | Medical Devices |\n| Describe the life critical impact (if present). | The model is only intended to support diagnostic purposes. |\n| Use Case Restrictions: | Abide by [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: [Apache License Version 2.0](https://github.com/lyuwenyu/RT-DETR/blob/main/LICENSE). |\n|Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |","featureLabels",[],"displayLabels",[187,188,189,190,191,192,193],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_152":220,"_195":221,"_197":222,"_199":200},{"_152":217,"_195":218,"_197":219,"_199":200},{"_152":213,"_195":214,"_197":215,"_199":200},{"_152":209,"_195":210,"_197":211,"_199":200},{"_152":205,"_195":206,"_197":207,"_199":200},{"_152":203,"_195":203,"_197":204,"_199":200},{"_152":194,"_195":196,"_197":198,"_199":200},"Object Detection","value","uscs_object_detection","filter",{"_201":202,"_195":196},"category","label","field","application","NSPECT-YMVI-7R6R",{"_201":200,"_195":203},"Inference","soln_inference",{"_201":208,"_195":206},"technology","Holoscan","pltfm_clara_holoscan",{"_201":212,"_195":210},"platform","Healthcare","indus_healthcare",{"_201":216,"_195":214},"industry","Computer Vision","soln_computer_vision",{"_201":208,"_195":218},"AI","soln_ai",{"_201":208,"_195":221},"appConfig",{"_1401":1402,"_257":1403},"ngcApps",[1354],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1335":1336,"_1337":1338,"_1339":1340,"_1341":1342,"_1343":235,"_77":262,"_1344":1345,"_1346":44,"_1347":88,"_1348":88,"_1349":1350,"_1351":88,"_1352":1353},"amplitude",{"_1332":1333,"_934":-7,"_1334":20},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1326":1327,"_1328":1329,"_1330":1331},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","https://api.ngc.nvidia.com","ngcHelmOrigin","https://helm.ngc.nvidia.com","origin","https://catalog.ngc.nvidia.com","user","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1209":111,"_1210":1211,"_1212":1213,"_1214":1215},"announcements",[],"consentState",{"_300":1199,"_1200":88,"_1201":20,"_1202":20,"_1203":1204},"consentConfig",{"_282":283,"_284":285,"_286":287,"_288":289,"_290":291,"_292":293,"_294":295,"_296":297,"_298":299,"_300":301},"showContactAdmin",1783956362610,[],[],[276,277,278,279,280],{"_33":48,"_35":53,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":52,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":49,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":45,"_35":46,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":281,"_43":44},4,"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. 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,[451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485],[324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340],{"_341":447,"_152":447,"_110":111,"_314":448},{"_341":442,"_152":442,"_110":111,"_314":443},{"_341":438,"_152":438,"_110":111,"_314":439},{"_341":433,"_152":433,"_110":111,"_314":434},{"_341":428,"_152":428,"_110":111,"_314":429},{"_341":416,"_152":416,"_110":111,"_314":417},{"_341":404,"_152":404,"_110":111,"_314":405},{"_341":399,"_152":399,"_110":111,"_314":400},{"_341":388,"_152":388,"_110":111,"_314":389},{"_341":383,"_152":383,"_110":111,"_314":384},{"_341":378,"_152":378,"_110":111,"_314":379},{"_341":373,"_152":373,"_110":111,"_314":374},{"_341":368,"_152":368,"_110":111,"_314":369},{"_341":363,"_152":363,"_110":111,"_314":364},{"_341":358,"_152":358,"_110":111,"_314":359},{"_341":351,"_152":351,"_110":111,"_314":352},{"_341":342,"_152":342,"_110":111,"_314":343},"hostName","amazon-adsystem.com",[344,345],{"_139":350,"_347":342,"_348":20,"_110":349},{"_139":346,"_347":342,"_348":20,"_110":349},"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",[353,354],{"_139":357,"_347":351,"_348":20,"_110":356},{"_139":355,"_347":351,"_348":20,"_110":356},"IDE","This domain is owned by Doubleclick (Google). 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It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[380],{"_139":381,"_347":378,"_348":20,"_110":382},"sp","Testing","google.com",[385],{"_139":386,"_347":383,"_348":20,"_110":387},"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",[390,391,392,393],{"_139":398,"_347":388,"_348":20,"_110":395},{"_139":397,"_347":388,"_348":20,"_110":395},{"_139":396,"_347":388,"_348":20,"_110":395},{"_139":394,"_347":388,"_348":20,"_110":395},"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",[401],{"_139":402,"_347":399,"_348":20,"_110":403},"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",[406,407,408,409],{"_139":415,"_347":404,"_348":20,"_110":411},{"_139":413,"_347":404,"_348":20,"_110":414},{"_139":412,"_347":404,"_348":20,"_110":411},{"_139":410,"_347":404,"_348":20,"_110":411},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. 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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",[430],{"_139":431,"_347":428,"_348":20,"_110":432},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[435],{"_139":436,"_347":433,"_348":20,"_110":437},"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",[440],{"_139":441,"_347":438,"_348":20,"_110":382},"R","6sc.co",[444],{"_139":445,"_347":442,"_348":20,"_110":446},"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.",[573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593],[538,539,540,541,542],{"_341":433,"_152":433,"_110":111,"_314":564},{"_341":399,"_152":399,"_110":111,"_314":559},{"_341":552,"_152":552,"_110":111,"_314":553},{"_341":548,"_152":548,"_110":111,"_314":549},{"_341":543,"_152":543,"_110":111,"_314":544},"nvcrm.my.site.com",[545],{"_139":546,"_347":543,"_348":20,"_110":547},"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",[550],{"_139":551,"_347":548,"_348":20,"_110":382},"edge","github.com",[554,555],{"_139":558,"_347":552,"_348":20,"_110":557},{"_139":556,"_347":552,"_348":20,"_110":557},"logged_in","This domain is owned by Github. 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The data is aggregated and analyzed; therefore, it remains anonymous upon use.\n","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","_biz_nA","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. 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 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