PeopleSemSegNet AMR Model Card

Model Overview

The PeopleSemSegNet Autonomous Mobile Robot (AMR) model detects one or more “person” object within an image and returns a semantic segmentation mask for all people within an image. This model is optmized for Isaac Perceptor product based on the deployabel PeopleSemSegNet version from TAO, which is ready for commercial use.

References

Citations

Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: CVPR. (2016)
Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks, In: CVPR. (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: CVPR (2015)

Model Architecture

Architecture Type: Convolution Neural Network (CNN)
\nNetwork Architecture: UNet

UNet is a widely adopted network for performing semantic segmentation, which has applications in autonomous vehicles, industries, smart cities, etc. UNet is a fully convolutional network with an encoder that is comprised of convolutional layers and a decoder that is comprised of transposed convolutions or upsampling layers. It then predicts a class label for every pixel in the input image.

Input:

Input Type(s): Images
\nInput Format(s): Red, Green, Blue (RGB)
\nInput Parameters: 4D
\nOther Properties Related to Input: RGB Fixed Resolution: 960 X 544 X 3 (W x H x C) Channel Ordering of the Input: NHWC, where N = Batch Size, C = number of channels (3), H = Height of images (544), W = Width of the images (960) Input scale: 1/255.0 Mean subtraction: None; No minimum bit depth, alpha, or gamma.

Input Image:\n

Output:

Output Type(s): Label(s), Segmentation Mask, Confidence Scores
\nOutput Format: Label: Text String(s); Segmentation Mask, Confidence Scores: Floating Point
\nOther Properties Related to Output: Category Label(s): Bag, Face, Person, Segmentation Mask, Confidence Scores

Output image\n

Training

Data Collection Method by dataset: Automatic/Sensors
\nLabeling Method by dataset: Human

This model was trained using the DetectNet_v2 entrypoint in TAO. The training algorithm optimizes the network to minimize the localization and confidence loss for the objects. The training is carried out in two phases. In the first phase, the network is trained without regularization.

Training Data Properties

Internal, proprietary dataset with more than 5 million objects for person class. The training dataset consists of a mix of camera heights, crowd-density, and field-of view (FOV). Approximately half of the training data consisted of images captured in an indoor office environment. For this case, the camera is typically set up at approximately 10 feet height, 45-degree angle and has close field-of-view. This content was chosen to improve accuracy of the models for extended arms pose of people. We have also added approximately 45 thousand images with low-density scenes from a robot's point of view to improve the performance for use-cases where person object detection is needed at low heights.

\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\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\">

		Object
Environment	Images	Persons
5ft Indoor	108,692	1,060,960
fft Outdoor	206,912	166,8250
1Oft Indoor (Office close FOV)	413,270	4,577,870
1Oft Outdoor	18,321	178,817
20ft Indoor	104,972	1,079,550
20ft Outdoor	24,783	59,623
Robotics Subset	43076	160806
Total	920,026	8,785,876

Training Data Ground-truth Labeling Guidelines

The training dataset is created by labeling ground-truth bounding-boxes and categories by human labellers. Following guidelines were used while labelling the training data for NVIDIA PeopleSemSegNet AMR model. If you are looking to re-train with your own dataset, please follow the guideline below for highest accuracy.

PeopleSemSegNet AMR project labelling guidelines:

\n
All objects that fall under one of the three classes (person, face, bag) in the image and are larger than the smallest bounding-box limit for the corresponding class (height >= 10px OR width >= 10px @1920x1080) are labeled with the appropriate class label.
\n
\n
If a person is carrying an object please mark the bounding-box to include the carried object as long as it doesn’t affect the silhouette of the person. For example, exclude a rolling bag if they are pulling it behind them and are distinctly visible as separate object. But include a backpack, purse etc. that do not alter the silhouette of the pedestrian significantly.
\n
\n
Occlusion: For partially occluded objects that do not belong a person class and are visible approximately 60% or are marked as visible objects with bounding box around visible part of the object. These objects are marked as partially occluded. Objects under 60% visibility are not annotated.
\n
\n
Occlusion for person class: If an occluded person’s head and shoulders are visible and the visible height is approximately 20% or more, then these objects are marked by the bounding box around the visible part of the person object. If the head and shoulders are not visible please follow the Occlusion guidelines in item 3 above.
\n
\n
Truncation: For an object other than a person that is at the edge of the frame with visibility of 60% or more visible are marked with the truncation flag for the object.
\n
\n
Truncation for person class: If a truncated person’s head and shoulders are visible and the visible height is approximately 20% or more mark the bounding box around the visible part of the person object. If the head and shoulders are not visible please follow the Truncation guidelines in item 5 above.
\n
\n
Each frame is not required to have an object.
\n
\n
The segmentation masks were labeled using NVIDIA internal auto-labeling tool
\n

Performance

Evaluation Data Properties\nData Collection Method by dataset: Automatic/Sensors
\nLabeling Method by dataset: Human\n5000 proprietary images across a variety of environments from a robot's point of view.

Methodology and KPI

The KPI for the evaluation data are reported in the table below. Model is evaluated based on Mean Intersection-Over-Union. Mean Intersection-Over-Union (MIOU) is a common evaluation metric for semantic image segmentation, which first computes the IOU for each semantic class and then computes the average over classes.

\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	PeopleSemSegNet AMR
Content	MIOU
Robot's point of view	87

How to use this model

These models need to be used with NVIDIA Hardware and Software. For Hardware, the models can run on any NVIDIA GPU including NVIDIA Jetson devices. These models can only be used with Train Adapt Optimize (TAO) Toolkit, DeepStream SDK or TensorRT.

The model is intended for training using TAO Toolkit with the user's own dataset or using it as it is. This can provide high fidelity models that are adapted to the use case. The Jupyter notebook available as a part of TAO container can be used to re-train.

Primary use case intended for the model is segmenting people in a color (RGB) image. The model can be used to segment people from photos and videos by using appropriate video or image decoding and pre-processing. Note this model performs semantic segmentation and not instance based segmentation.

Ethical Considerations

NVIDIA PeopleSemSeg AMR model detects faces. However, no additional information such as race, gender, and skin type about the faces is inferred.

Training and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies.

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.

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This model is optmized for Isaac Perceptor product based on the [deployabel PeopleSemSegNet version from TAO](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplesemsegnet_amr), which is ready for commercial use.\n\n### References\n#### Citations\n- Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: CVPR. (2016)\n- Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks, In: CVPR. (2014)\n- He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: CVPR (2015)\n\n#### Model Architecture\n**Architecture Type**: Convolution Neural Network (CNN) \\\n**Network Architecture**: UNet\n\nUNet is a widely adopted network for performing semantic segmentation, which has applications in autonomous vehicles, industries, smart cities, etc. UNet is a fully convolutional network with an encoder that is comprised of convolutional layers and a decoder that is comprised of transposed convolutions or upsampling layers. It then predicts a class label for every pixel in the input image.\n\n#### Input:\n**Input Type(s)**: Images \\\n**Input Format(s)**: Red, Green, Blue (RGB) \\\n**Input Parameters**: 4D \\\n**Other Properties Related to Input**: RGB Fixed Resolution: 960 X 544 X 3 (W x H x C) Channel Ordering of the Input: NHWC, where N = Batch Size, C = number of channels (3), H = Height of images (544), W = Width of the images (960) Input scale: 1/255.0 Mean subtraction: None; No minimum bit depth, alpha, or gamma.\n\nInput Image:\n[](https://raw.githubusercontent.com/vpraveen-nv/model_card_images/main/cv/purpose_built_models/segmentation_robotics/image.png)\n\n#### Output:\n**Output Type(s)**: Label(s), Segmentation Mask, Confidence Scores \\\n**Output Format**: Label: Text String(s); Segmentation Mask, Confidence Scores: Floating Point \\\n**Other Properties Related to Output**: Category Label(s): Bag, Face, Person, Segmentation Mask, Confidence Scores\n\nOutput image\n[](https://raw.githubusercontent.com/vpraveen-nv/model_card_images/main/cv/purpose_built_models/segmentation_robotics/image.png)\n\n### Training\n**Data Collection Method by dataset**: Automatic/Sensors \\\n**Labeling Method by dataset:** Human\n\nThis model was trained using the DetectNet_v2 entrypoint in TAO. The training algorithm optimizes the network to minimize the localization and confidence loss for the objects. The training is carried out in two phases. In the first phase, the network is trained without regularization.\n\n#### Training Data Properties\nInternal, proprietary dataset with more than 5 million objects for person class. The training dataset consists of a mix of camera heights, crowd-density, and field-of view (FOV). Approximately half of the training data consisted of images captured in an indoor office environment. For this case, the camera is typically set up at approximately 10 feet height, 45-degree angle and has close field-of-view. This content was chosen to improve accuracy of the models for extended arms pose of people. We have also added approximately 45 thousand images with low-density scenes from a robot's point of view to improve the performance for use-cases where person object detection is needed at low heights.\n\n| | | Object |\n| :--- | :--- | :--- |\n| Environment | Images | Persons |\n| 5ft Indoor | 108,692 | 1,060,960 |\n| fft Outdoor | 206,912 | 166,8250 |\n| 1Oft Indoor (Office close FOV) | 413,270 | 4,577,870 |\n| 1Oft Outdoor | 18,321 | 178,817 |\n| 20ft Indoor | 104,972 | 1,079,550 |\n| 20ft Outdoor | 24,783 | 59,623 |\n| Robotics Subset | 43076 | 160806 |\n| Total | 920,026 | 8,785,876 |\n\n### Training Data Ground-truth Labeling Guidelines\nThe training dataset is created by labeling ground-truth bounding-boxes and categories by human labellers. Following guidelines were used while labelling the training data for NVIDIA PeopleSemSegNet AMR model. If you are looking to re-train with your own dataset, please follow the guideline below for highest accuracy.\n\nPeopleSemSegNet AMR project labelling guidelines:\n\n1. All objects that fall under one of the three classes (person, face, bag) in the image and are larger than the smallest bounding-box limit for the corresponding class (height >= 10px OR width >= 10px @1920x1080) are labeled with the appropriate class label.\n\n2. If a person is carrying an object please mark the bounding-box to include the carried object as long as it doesn’t affect the silhouette of the person. For example, exclude a rolling bag if they are pulling it behind them and are distinctly visible as separate object. But include a backpack, purse etc. that do not alter the silhouette of the pedestrian significantly.\n\n3. Occlusion: For partially occluded objects that do not belong a person class and are visible approximately 60% or are marked as visible objects with bounding box around visible part of the object. These objects are marked as partially occluded. Objects under 60% visibility are not annotated.\n\n4. Occlusion for person class: If an occluded person’s head and shoulders are visible and the visible height is approximately 20% or more, then these objects are marked by the bounding box around the visible part of the person object. If the head and shoulders are not visible please follow the Occlusion guidelines in item 3 above.\n\n5. Truncation: For an object other than a person that is at the edge of the frame with visibility of 60% or more visible are marked with the truncation flag for the object.\n\n6. Truncation for person class: If a truncated person’s head and shoulders are visible and the visible height is approximately 20% or more mark the bounding box around the visible part of the person object. If the head and shoulders are not visible please follow the Truncation guidelines in item 5 above.\n\n7. Each frame is not required to have an object.\n\n8. The segmentation masks were labeled using NVIDIA internal auto-labeling tool\n\n### Performance\nEvaluation Data Properties\n**Data Collection Method by dataset**: Automatic/Sensors \\\n**Labeling Method by dataset:** Human\n5000 proprietary images across a variety of environments from a robot's point of view.\n\n##### Methodology and KPI\nThe KPI for the evaluation data are reported in the table below. Model is evaluated based on Mean Intersection-Over-Union. Mean Intersection-Over-Union (MIOU) is a common evaluation metric for semantic image segmentation, which first computes the IOU for each semantic class and then computes the average over classes.\n\n| Model | PeopleSemSegNet AMR |\n| :--- | :--- |\n| Content | MIOU |\n| Robot's point of view | 87 |\n\n#### How to use this model\nThese models need to be used with NVIDIA Hardware and Software. For Hardware, the models can run on any NVIDIA GPU including NVIDIA Jetson devices. These models can only be used with Train Adapt Optimize (TAO) Toolkit, DeepStream SDK or TensorRT.\n\nThe model is intended for training using TAO Toolkit with the user's own dataset or using it as it is. This can provide high fidelity models that are adapted to the use case. The Jupyter notebook available as a part of TAO container can be used to re-train.\n\nPrimary use case intended for the model is segmenting people in a color (RGB) image. The model can be used to segment people from photos and videos by using appropriate video or image decoding and pre-processing. Note this model performs semantic segmentation and not instance based segmentation.\n\n#### Ethical Considerations\nNVIDIA PeopleSemSeg AMR model detects faces. However, no additional information such as race, gender, and skin type about the faces is inferred.\n\nTraining and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies.\n\nNVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.","displayName","PeopleSemSeg AMR",["D",1733260447085],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/isaac/models/optimized-peoplesemseg-amr","rawImageUrl","https://media.githubusercontent.com/media/NVIDIA-ISAAC-ROS/.github/main/resources/isaac_ngc/icons/m48-robot-256px-grn.png","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: | Gender, Skin Color |\n| Measures taken to mitigate against unwanted bias: | Developed customized dataset that included video from persons of different skin colors in different geographic settings. Unwanted bias is mitigated by the collection of data in different urban areas at different times of the day during typical working hours and outside of typical working times to get a diversity of races and skin tones. |","explainability","| Field | Response |\n|---------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| Intended Application(s) & Domain(s): | Detecting bags, faces, and persons. |\n| Model Type: | Object Segmentation |\n| Intended Users: | Developers working in robotics applications to help autonomous machines navigate floor space. |\n| Output: | Pixel Mask(s), Category Label(s), Confidence Score(s) |\n| Describe how the model works: | Detects and labels bags, faces, and persons within an image and returns maksed image, labels for each category. |\n| Technical Limitations: | Model accuracy may be reduced in low-light, low contrast lighting conditions, with warped images, objects positioning relatively close to or far away from the camera frame (smaller than 20x20 pixels), and with objects occluded (less than 40% of the object is visible) or truncated. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Accuracy, Intersection over Union(IoU), Precision, Recall |\n| Potential Known Risks: | Not recommended for life-critical use cases |\n| Licensing: | [https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/) |","privacy","| Field | Response |\n|-----------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| Generatable or reverse engineerable personally-identifiable information (PII)? | None |\n| Protected classes used to create this model? | Developed customized dataset that included video from persons of different skin colors in different geographic settings. Unwanted bias is mitigated by the collection of data in different urban areas at different times of the day during typical working hours and outside of typical working times to get a diversity of races and skin tones. |\n| Was consent obtained for any PII used? | Yes |\n| How often is dataset reviewed? | Before Release |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | Yes |\n| If PII collected for the development of the model, was it collected directly by NVIDIA? | Yes, PII was collected by NVIDIA. |\n| If PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Yes |\n| If PII collected for the development of this AI model, was it minimized to only what was required? | Yes |\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? | Yes |\n| Applicable NVIDIA 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 Segmentation |\n| Describe the life-critical application (if present). | Potential for life-critical impact in detecting the presence of people in floor space. |\n| Use Case Restrictions: | Should not be used in \"Critical Applications;\" (reference)[https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/] |\n| Describe access restrictions (if any): | 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",[245,246,247,248,249,250,251,252,253,254],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_210":290,"_256":291,"_258":292,"_260":261},{"_210":287,"_256":288,"_258":289,"_260":261},{"_210":283,"_256":284,"_258":285,"_260":261},{"_210":281,"_256":281,"_258":282,"_260":261},{"_210":278,"_256":279,"_258":280,"_260":261},{"_210":274,"_256":275,"_258":276,"_260":261},{"_210":271,"_256":271,"_258":272,"_260":261},{"_210":268,"_256":269,"_258":270,"_260":261},{"_210":264,"_256":265,"_258":266,"_260":261},{"_210":255,"_256":257,"_258":259,"_260":261},"Vision AI","value","soln_vision_ai","filter",{"_262":263,"_256":257},"category","label","field","technology","Triton Inference Server","pltfm_triton_inference_server",{"_262":267,"_256":265},"platform","TensorRT","pltfm_tensorrt",{"_262":267,"_256":269},"TAO Toolkit",{"_262":273,"_256":271},"framework","Robotics","indus_robotics",{"_262":277,"_256":275},"industry","NVIDIA AI","soln_nvidia_ai",{"_262":263,"_256":279},"NSPECT-9QC1-GSKB",{"_262":261,"_256":281},"Image Segmentation","uscs_image_segmentation",{"_262":286,"_256":284},"application","Computer Vision","soln_computer_vision",{"_262":263,"_256":288},"AI","soln_ai",{"_262":263,"_256":291},{"_121":1271,"_123":1272},"ngcApps",[1224],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1205":1206,"_1207":1208,"_1209":1210,"_1211":1212,"_1213":304,"_152":328,"_1214":1215,"_1216":59,"_1217":100,"_1218":100,"_1219":1220,"_1221":100,"_1222":1223},"amplitude",{"_1202":1203,"_840":-7,"_1204":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1196":1197,"_1198":1199,"_1200":1201},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1080":408,"_1081":1082,"_1083":1084,"_1085":1086},"announcements",[],"consentState",{"_365":1070,"_1071":100,"_1072":34,"_1073":34,"_1074":1075},"consentConfig",{"_347":348,"_349":350,"_351":352,"_353":354,"_355":356,"_357":358,"_359":360,"_361":362,"_363":364,"_365":366},"showContactAdmin",1785750132180,[],[],[342,343,344,345,346],{"_48":63,"_50":74,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":116,"_54":-5,"_56":-5,"_58":59},"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,[505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526],[389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405],{"_406":500,"_210":500,"_182":408,"_379":501},{"_406":496,"_210":496,"_182":408,"_379":497},{"_406":491,"_210":491,"_182":408,"_379":492},{"_406":487,"_210":487,"_182":408,"_379":488},{"_406":482,"_210":482,"_182":408,"_379":483},{"_406":477,"_210":477,"_182":408,"_379":478},{"_406":469,"_210":469,"_182":408,"_379":470},{"_406":461,"_210":461,"_182":408,"_379":462},{"_406":456,"_210":456,"_182":408,"_379":457},{"_406":451,"_210":451,"_182":408,"_379":452},{"_406":446,"_210":446,"_182":408,"_379":447},{"_406":441,"_210":441,"_182":408,"_379":442},{"_406":436,"_210":436,"_182":408,"_379":437},{"_406":429,"_210":429,"_182":408,"_379":430},{"_406":424,"_210":424,"_182":408,"_379":425},{"_406":417,"_210":417,"_182":408,"_379":418},{"_406":407,"_210":407,"_182":408,"_379":409},"hostName","amazon-adsystem.com","",[410,411],{"_78":416,"_413":407,"_414":34,"_182":415},{"_78":412,"_413":407,"_414":34,"_182":415},"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",[419,420],{"_78":423,"_413":417,"_414":34,"_182":422},{"_78":421,"_413":417,"_414":34,"_182":422},"IDE","This domain is owned by Doubleclick (Google). 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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. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.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.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","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. 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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.",[597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621],[568,569,570,571,572],{"_406":482,"_210":482,"_182":408,"_379":592},{"_406":456,"_210":456,"_182":408,"_379":587},{"_406":582,"_210":582,"_182":408,"_379":583},{"_406":577,"_210":577,"_182":408,"_379":578},{"_406":573,"_210":573,"_182":408,"_379":574},"nvidia.partners",[575],{"_78":576,"_413":573,"_414":34,"_182":450},"edge","app.hushly.com",[579],{"_78":580,"_413":577,"_414":34,"_182":581},"*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",[584],{"_78":585,"_413":582,"_414":34,"_182":586},"_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.",[680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708],[660,661,662,663],{"_406":675,"_210":675,"_182":408,"_379":676},{"_406":670,"_210":670,"_182":408,"_379":671},{"_406":469,"_210":469,"_182":408,"_379":667},{"_406":582,"_210":582,"_182":408,"_379":664},[665],{"_78":666,"_413":582,"_414":100,"_182":586},"_gh_sess",[668],{"_78":669,"_413":469,"_414":100,"_182":476},"YSC","bizible.com",[672],{"_78":673,"_413":670,"_414":34,"_182":674},"_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",[677],{"_78":678,"_413":675,"_414":34,"_182":679},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_78":750,"_413":528,"_414":34,"_182":751},{"_78":735,"_413":749,"_414":100,"_182":736},{"_78":718,"_413":531,"_414":34,"_182":643},{"_78":718,"_413":631,"_414":34,"_182":643},{"_78":747,"_413":528,"_414":34,"_182":748},{"_78":735,"_413":534,"_414":100,"_182":736},{"_78":745,"_413":528,"_414":34,"_182":746},{"_78":735,"_413":531,"_414":100,"_182":736},{"_78":743,"_413":630,"_414":34,"_182":744},{"_78":735,"_413":652,"_414":100,"_182":736},{"_78":715,"_413":531,"_414":34,"_182":717},{"_78":741,"_413":528,"_414":100,"_182":742},{"_78":739,"_413":528,"_414":34,"_182":740},{"_78":737,"_413":528,"_414":100,"_182":738},{"_78":735,"_413":626,"_414":100,"_182":736},{"_78":733,"_413":528,"_414":34,"_182":734},{"_78":731,"_413":528,"_414":34,"_182":732},{"_78":729,"_413":528,"_414":100,"_182":730},{"_78":727,"_413":635,"_414":34,"_182":728},{"_78":715,"_413":626,"_414":34,"_182":717},{"_78":725,"_413":528,"_414":34,"_182":726},{"_78":723,"_413":528,"_414":100,"_182":724},{"_78":721,"_413":528,"_414":34,"_182":722},{"_78":719,"_413":528,"_414":34,"_182":720},{"_78":718,"_413":528,"_414":34,"_182":643},{"_78":715,"_413":716,"_414":34,"_182":717},{"_78":713,"_413":528,"_414":34,"_182":714},{"_78":711,"_413":528,"_414":100,"_182":712},{"_78":709,"_413":528,"_414":34,"_182":710},"_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. It allows us to engage with a user that has previously visited our website.","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.","_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.","_session_id","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. 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