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. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","research.nvidia.com","_twpid","_gat_gtag_UA_141699104_1","_gat_gtag_UA_53775284_6","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.","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.",4,[627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657],[593,594,595,596,597,598],{"_396":478,"_207":478,"_166":167,"_368":622},{"_396":453,"_207":453,"_166":167,"_368":617},{"_396":612,"_207":612,"_166":167,"_368":613},{"_396":607,"_207":607,"_166":167,"_368":608},{"_396":603,"_207":603,"_166":167,"_368":604},{"_396":599,"_207":599,"_166":167,"_368":600},"nvcrm.my.site.com",[601],{"_75":602,"_402":599,"_403":30,"_166":167},"BrowserId","nvidia.partners",[605],{"_75":606,"_402":603,"_403":30,"_166":439},"edge","app.hushly.com",[609],{"_75":610,"_402":607,"_403":30,"_166":611},"*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",[614],{"_75":615,"_402":612,"_403":30,"_166":616},"_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.",[727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770],[699,700,701,702,703],{"_396":722,"_207":722,"_166":167,"_368":723},{"_396":717,"_207":717,"_166":167,"_368":718},{"_396":466,"_207":466,"_166":167,"_368":714},{"_396":707,"_207":707,"_166":167,"_368":708},{"_396":612,"_207":612,"_166":167,"_368":704},[705],{"_75":706,"_402":612,"_403":83,"_166":616},"_gh_sess","statcounter.com",[709,710],{"_75":713,"_402":707,"_403":30,"_166":712},{"_75":711,"_402":707,"_403":30,"_166":712},"is_unique","This domain is owned by StatCounter.\nThe main business activity is: Real time visitor analytics","is_visitor_unique",[715],{"_75":716,"_402":466,"_403":83,"_166":472},"YSC","bizible.com",[719],{"_75":720,"_402":717,"_403":30,"_166":721},"_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",[724],{"_75":725,"_402":722,"_403":30,"_166":726},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_75":832,"_402":540,"_403":30,"_166":833},{"_75":830,"_402":540,"_403":83,"_166":831},{"_75":827,"_402":828,"_403":30,"_166":829},{"_75":805,"_402":818,"_403":83,"_166":806},{"_75":825,"_402":540,"_403":30,"_166":826},{"_75":779,"_402":543,"_403":30,"_166":675},{"_75":779,"_402":576,"_403":30,"_166":675},{"_75":823,"_402":540,"_403":30,"_166":824},{"_75":807,"_402":662,"_403":83,"_166":808},{"_75":805,"_402":546,"_403":83,"_166":806},{"_75":821,"_402":540,"_403":30,"_166":822},{"_75":819,"_402":540,"_403":30,"_166":820},{"_75":813,"_402":540,"_403":30,"_166":167},{"_75":804,"_402":818,"_403":30,"_166":447},{"_75":805,"_402":543,"_403":83,"_166":806},{"_75":816,"_402":576,"_403":30,"_166":817},{"_75":814,"_402":667,"_403":30,"_166":815},{"_75":805,"_402":691,"_403":83,"_166":806},{"_75":813,"_402":662,"_403":30,"_166":167},{"_75":777,"_402":543,"_403":30,"_166":778},{"_75":804,"_402":543,"_403":30,"_166":447},{"_75":811,"_402":540,"_403":83,"_166":812},{"_75":809,"_402":540,"_403":30,"_166":810},{"_75":807,"_402":540,"_403":83,"_166":808},{"_75":805,"_402":662,"_403":83,"_166":806},{"_75":804,"_402":662,"_403":30,"_166":447},{"_75":802,"_402":540,"_403":30,"_166":803},{"_75":800,"_402":540,"_403":30,"_166":801},{"_75":798,"_402":540,"_403":83,"_166":799},{"_75":795,"_402":796,"_403":30,"_166":797},{"_75":777,"_402":662,"_403":30,"_166":778},{"_75":793,"_402":540,"_403":83,"_166":794},{"_75":791,"_402":540,"_403":30,"_166":792},{"_75":790,"_402":662,"_403":83,"_166":785},{"_75":788,"_402":540,"_403":83,"_166":789},{"_75":786,"_402":555,"_403":30,"_166":787},{"_75":784,"_402":662,"_403":83,"_166":785},{"_75":782,"_402":540,"_403":30,"_166":783},{"_75":780,"_402":540,"_403":30,"_166":781},{"_75":779,"_402":540,"_403":30,"_166":675},{"_75":777,"_402":555,"_403":30,"_166":778},{"_75":775,"_402":540,"_403":30,"_166":776},{"_75":773,"_402":540,"_403":83,"_166":774},{"_75":771,"_402":540,"_403":30,"_166":772},"_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","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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The cookie is used for measuring a visitor’s scroll activity to see how much of a page they view before moving on to another page.","_an_uid","This cookie is likely used for analytics purposes to track user behavior and site usage, which may include unique user identification for tracking.","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","s_ips","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","s_plt","This cookie is used to measure the time taken for pages to load, allowing website owners to identify slow loading pages and improve user experience.","_session_id","resources.nvidia.com","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. This ensures that behavior in subsequent visits to the same site will be attributed to the same user ID.","__sec_*","_hjHasCachedUserAttributes","This cookie name is associated with Hotjar which is used to know whether the data set in _hjUserAttributes Local Storage item is up to date or not.","s_ppv","This cookie is associated with Adobe Analytics site tracking and is used to measure a user's scroll activity to see how much of a page they view before moving to another page.","_gid","This cookie name is associated with Google Universal Analytics. This appears to be a new cookie and as of Spring 2017 no information is available from Google. It appears to store and update a unique value for each page visited.","_hjTLDTest","When the Hotjar script executes we try to determine the most generic cookie path we should use, instead of the page hostname. This is done so that cookies can be shared across subdomains (where applicable). 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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","omniverse.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. 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. 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.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","This cookie is managed by AWS and is used for load balancing.Used 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.","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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It is a temporary cookie used to determine if the browser accepts cookies.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","bm_ss","This cookie is used to help protect websites from automated bot traffic.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","c_code","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. 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