TAO BEVFusion Model Card

Model Overview

Description

BEVFusion model detects the people within an image and a Lidar with 3D Bounding Boxes, a category label for each object and confidence scores.

This model is ready for commercial use.

License/Terms of Use

License to use these models is covered by the NVIDIA Open Model License. By downloading the model, you accept the terms and conditions of these licenses.

References

Liu, Zhijian and Tang, Haotian and Amini, Alexander and Yang, Xingyu and Mao, Huizi and Rus, Daniela and Han, Song: BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation. In: ICRA. (2023)

Model Architecture

Architecture Type: Vision Transformer
\nNetwork Architecture:\nThis model is a transformer-based network archteicture and fuses the lidar and camear features in Bird's-Eye View Feature space to perform 3D object detection.\nSwin-Tiny (Image Backbone); SECOND (LiDAR Backbone) were used.

This model was trained using the BEVFusion entrypoint in TAO. The model uses three angles in 3D space (roll, pitch ,yaw) to represent the 3D boudning boxes, whereas only single angle (yaw) was supported in the original BEVFusion code base.\nThe training algorithm optimizes the network to minimize the Gaussian Focal Loss and L1 Loss.

Input

Input Types:

Image , Point Cloud

Input Formats:

Images: Red, Green, Blue (RGB)
Point Cloud: Points

Input Parameters:

Images - 1920 X 1080 X 3 for TAO3DSynthetic: Two Dimensional (2D)
Images - 370 X 1224 X for KittiPerson : Two Dimensional (2D)
Point Cloud - N X 4 : Three Dimensional (3D)

Other Properties Related to Input:

Calibration matrix: Lidar to Camera Prjoection Matrix, Camera Intrinsic Matrix
No specific minimum or maximum resolution restriction, No Alpha Channel needed

Output

Output Types: 3D Bounding Boxes, Category labels, Confidence scores
\nOutput Format: Images with 3D Bounding Boxes visualization
\nOutput Parameters: Nine Dimensional (9D), One-Dimensional (1D), One-Dimensional (1D)
\nOther Properties Related to Output:

3D bounding-box coordinates: (Cetner_x, Cetner_y, Center_z, Scale_x, Scale_y, Scale_z, Rotation_x, Rotation_y, Rotation_z).
No specific minimum or maximum resolution restriction, No Alpha Channel needed

Software Integration

Runtime Engines:

TAO 5.5.0

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Blackwell
NVIDIA Jetson
NVIDIA Hopper
NVIDIA Lovelace
NVIDIA Pascal
NVIDIA Turing
NVIDIA Volta

[Preferred/Supported] Operating Systems:

Linux

These models are designed for use with NVIDIA hardware and software. For hardware, the models are compatible with any NVIDIA GPU, including NVIDIA Jetson devices. For software, the models are specifically designed for the TAO Toolkit.

The primary application of these models is to estimate an object's 3D bounding box from a single camera and sinlge lidar sensor setup. They can identify the person objects given single rgb image with aligned single lidar point cloud.

Model Versions

bevfusion1.0**: pytorch pth file, inferencable on TAO Toolkit.

Training, Testing, and Evaluation Datasets

Training Dataset

A proprietary synthetic dataset (TAO3DSynthetic) that was generated with Omniverse and Issac-Sim was used during the training and testing. The training dataset has 465,373 images, point cloud pairs, and 1,510,704 objects for the person class.

nSpect: NSPECT-I6RW-ENUU

Data Collection Method by Dataset

Synthetic

Labeling Method by Dataset

Synthetic

Properties:
\nThe training dataset consists of image, point cloud data from Lidar, and calibration matrices from nine different indoor scenes.\nThe content was captured from a height of two feet with sensors that were located perpendicularly from the surface. Each scene has three different lighting conditions: Normal, Dark, and Very Dark.

\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\">

	Training Dataset
Scene Category	Number of Images
Warehouse	41526
Retail Store	79283
Hospital	57182
Office	145940
Factory	141442
Total	465373

Evaluation Dataset

Evaluation dataset for TAO3DSynthetic was also generated along with training dataset.\nEvaluation dataset contains three scenes (Warehouse, Retail Store and Hospital) from training data with unseen people in them and one new scene that is not included in the training dataset.

Data Collection Method by Dataset

Synthetic

Labeling Method by Dataset

Synthetic

After pretraining is complete on TAO3DSynthetic, the BEVFusion model was fine-tuned on a public KITTI dataset with only pedestrian class to demonstrate the use-case. We do not release the model weights for Kitti fine-tuning and we are showing accuracy results only below for demonstration purpose.\nThe original Kitti provides only rotation_y in 3D bounding box labels. Thus, we zero-padded rotation x and z for KittiPerson dataset to be compatible with TAO3DSynthetic-BEVfusion.\nWe only used the images that contains pedestrian from original Kitti dataset. KittiPerson dataset consists of 955 training images with 2207 person objects and 824 validation images with 2280 person objects.

Methodology and KPI

The KPI for the evaluation data are reported in the following table.\nFor TAO3Dsynthetic-BEVFusion model, the evaluation data is also generated with Omniverse.\nFor KittiPerson-BEVFusion model, validation set from Kitti was used for evaluation. Both training from scrtach and finetuning from TAO3DSynthetic were evaluated for KittiPerson model. Two models were evaluated with the KITTI 3D Metric, which evaluates object detection performance using AP40 at IOU 0.5.\nNote that Kitti Pedestrian is more chanllenging data as it was captured in the real street scenes.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Model	TAO3DSynthetic-BEVFusion
From Scratch	79.35%

\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	KittiPerson-BEVFusion
From Scratch	38.383 %
From TAO3DSynthetic-Finetuned	53.8747 %

Inference:

Engine: Pytorch

Inference Method

BEVFusion inference will be run through tao_pytorch_backend.

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. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards [Insert Link to Model Card++ here]. Please report security vulnerabilities or NVIDIA AI Concerns here.

","resourceVersions",[254,255],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_228":204,"_90":229,"_44":230,"_231":-5,"_232":233,"_93":234,"_235":236,"_237":238,"_239":26,"_240":-5,"_241":26,"_242":-5,"_243":26,"_244":-5,"_245":26,"_246":-5,"_247":26,"_248":-5,"_249":26,"_250":-5,"_251":26,"_252":253},"version","bevfusion_1.0","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/tao/bevfusion:bevfusion_1.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/tao/models/bevfusion/bevfusion_1.0/version-history","accessCheckResult",{"_227":155},"hasRelatedCollections","context",{"_84":85,"_86":87},1783025332683,[],[],[220,221,222,223,224,225,226],{"_40":55,"_42":66,"_44":57,"_46":58,"_48":67,"_50":51},{"_40":55,"_42":64,"_44":57,"_46":58,"_48":65,"_50":51},{"_40":55,"_42":62,"_44":57,"_46":58,"_48":63,"_50":51},{"_40":55,"_42":60,"_44":57,"_46":58,"_48":61,"_50":51},{"_40":55,"_42":56,"_44":171,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":54,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51},"allowed","versionId","","UPLOAD_COMPLETE","releaseType","createdDate",["D",1724374975744],["D",1740840131915],"totalSizeInBytes",487094270,"totalFileCount",2,"isSigned","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_77":258,"_256":259,"_231":-5,"_239":26,"_241":26,"_252":257,"_243":26},{"_77":204,"_256":204,"_231":-5,"_239":26,"_241":26,"_252":257,"_243":26},"name",[],"-","Latest (bevfusion_1.0)","nvidia/tao/bevfusion","nvidia/tao/models/bevfusion","bevfusion","BEVFusion model to detect 3D objects from point cloud and RGB data.","# TAO BEVFusion Model Card\n## Model Overview \n\n## Description\n[BEVFusion](https://arxiv.org/pdf/2205.13542) model detects the people within an image and a Lidar with 3D Bounding Boxes, a category label for each object and confidence scores. \n\nThis model is ready for commercial use.\n\n### License/Terms of Use \n\nLicense to use these models is covered by the NVIDIA Open Model License. By downloading the model, you accept the terms and conditions of these [licenses](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\n## References\n\nLiu, Zhijian and Tang, Haotian and Amini, Alexander and Yang, Xingyu and Mao, Huizi and Rus, Daniela and Han, Song: BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation. In: ICRA. (2023)\n\n## Model Architecture \n\n**Architecture Type:** Vision Transformer
\n**Network Architecture:** \nThis model is a transformer-based network archteicture and fuses the lidar and camear features in Bird's-Eye View Feature space to perform 3D object detection. \nSwin-Tiny (Image Backbone); SECOND (LiDAR Backbone) were used.\n\nThis model was trained using the BEVFusion entrypoint in TAO. The model uses three angles in 3D space (roll, pitch ,yaw) to represent the 3D boudning boxes, whereas only single angle (yaw) was supported in the original BEVFusion code base.\nThe training algorithm optimizes the network to minimize the Gaussian Focal Loss and L1 Loss.\n\n### Input\n**Input Types:**
\n* Image , Point Cloud
\n\n**Input Formats:**
\n* Images: Red, Green, Blue (RGB)
\n* Point Cloud: Points
\n\n**Input Parameters:**
\n* Images - 1920 X 1080 X 3 for TAO3DSynthetic: Two Dimensional (2D)\n* Images - 370 X 1224 X for KittiPerson : Two Dimensional (2D)\n* Point Cloud - N X 4 : Three Dimensional (3D)\n\n**Other Properties Related to Input:** \n* Calibration matrix: Lidar to Camera Prjoection Matrix, Camera Intrinsic Matrix
\n* No specific minimum or maximum resolution restriction, No Alpha Channel needed
\n\n### Output\n**Output Types:** 3D Bounding Boxes, Category labels, Confidence scores
\n**Output Format:** Images with 3D Bounding Boxes visualization
\n**Output Parameters:** Nine Dimensional (9D), One-Dimensional (1D), One-Dimensional (1D)
\n**Other Properties Related to Output:**
\n* 3D bounding-box coordinates: (Cetner_x, Cetner_y, Center_z, Scale_x, Scale_y, Scale_z, Rotation_x, Rotation_y, Rotation_z).
\n* No specific minimum or maximum resolution restriction, No Alpha Channel needed
\n\n## Software Integration \n**Runtime Engines:** \n* TAO 5.5.0
\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Ampere
\n* NVIDIA Blackwell
\n* NVIDIA Jetson
\n* NVIDIA Hopper
\n* NVIDIA Lovelace
\n* NVIDIA Pascal
\n* NVIDIA Turing
\n* NVIDIA Volta
\n\n**[Preferred/Supported] Operating Systems:**
\n* Linux
\n\nThese models are designed for use with NVIDIA hardware and software. For hardware, the models are compatible with any NVIDIA GPU, including NVIDIA Jetson devices. For software, the models are specifically designed for the [TAO Toolkit](https://github.com/NVIDIA/tao_pytorch_backend).\n\nThe primary application of these models is to estimate an object's 3D bounding box from a single camera and sinlge lidar sensor setup. They can identify the person objects given single rgb image with aligned single lidar point cloud.\n\n## Model Versions\n\nbevfusion1.0**: pytorch pth file, inferencable on [TAO Toolkit](https://github.com/NVIDIA/tao_pytorch_backend).\n\n# Training, Testing, and Evaluation Datasets \n\n## Training Dataset \nA proprietary synthetic dataset (TAO3DSynthetic) that was generated with [Omniverse](https://www.nvidia.com/en-us/omniverse/) and [Issac-Sim](https://developer.nvidia.com/isaac-sim) was used during the training and testing. The training dataset has 465,373 images, point cloud pairs, and 1,510,704 objects for the person class. \n* nSpect: NSPECT-I6RW-ENUU\n\n**Data Collection Method by Dataset**
\n* Synthetic
\n\n**Labeling Method by Dataset**
\n* Synthetic
\n\n**Properties:**
\nThe training dataset consists of image, point cloud data from Lidar, and calibration matrices from nine different indoor scenes. \nThe content was captured from a height of two feet with sensors that were located perpendicularly from the surface. Each scene has three different lighting conditions: Normal, Dark, and Very Dark. \n\n| | Training Dataset |\n| -- | -- |\n| Scene Category | Number of Images |\n| Warehouse | 41526 |\n| Retail Store | 79283 |\n| Hospital | 57182 |\n| Office | 145940 | \n| Factory | 141442 | \n| Total | 465373 | \n\n## Evaluation Dataset \n\nEvaluation dataset for TAO3DSynthetic was also generated along with training dataset.\nEvaluation dataset contains three scenes (Warehouse, Retail Store and Hospital) from training data with unseen people in them and one new scene that is not included in the training dataset.\n\n**Data Collection Method by Dataset**
\n* Synthetic
\n\n**Labeling Method by Dataset**
\n* Synthetic
\n\nAfter pretraining is complete on TAO3DSynthetic, the BEVFusion model was fine-tuned on a public [KITTI dataset](https://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d) with only pedestrian class to demonstrate the use-case. We do not release the model weights for Kitti fine-tuning and we are showing accuracy results only below for demonstration purpose.\nThe original Kitti provides only rotation_y in 3D bounding box labels. Thus, we zero-padded rotation x and z for KittiPerson dataset to be compatible with TAO3DSynthetic-BEVfusion. \nWe only used the images that contains pedestrian from original Kitti dataset. KittiPerson dataset consists of 955 training images with 2207 person objects and 824 validation images with 2280 person objects.\n\n### Methodology and KPI\n\nThe KPI for the evaluation data are reported in the following table. \nFor TAO3Dsynthetic-BEVFusion model, the evaluation data is also generated with Omniverse. \nFor KittiPerson-BEVFusion model, validation set from Kitti was used for evaluation. Both training from scrtach and finetuning from TAO3DSynthetic were evaluated for KittiPerson model. Two models were evaluated with the KITTI 3D Metric, which evaluates object detection performance using AP40 at IOU 0.5. \nNote that Kitti Pedestrian is more chanllenging data as it was captured in the real street scenes.\n\n| Model| TAO3DSynthetic-BEVFusion | \n|------|------|\n|From Scratch| 79.35% |\n\n| Model| KittiPerson-BEVFusion | \n|------|------|\n|From Scratch| 38.383 % |\n|From TAO3DSynthetic-Finetuned| 53.8747 % |\n\n## Inference:\n**Engine:** Pytorch
\n\n## Inference Method\n\nBEVFusion inference will be run through [tao_pytorch_backend](https://github.com/NVIDIA/tao_pytorch_backend/tree/main/).\n\n### Ethical Considerations \n\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. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards [Insert Link to Model Card++ here]. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","BEVFusion for 3D Object Detection",["D",1732745394928],"/api/image/nvidia/tao/models/bevfusion","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","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: | No measures taken to mitigate against unwanted bias. |\n| Measures taken to mitigate against unwanted bias: | None of the Above. |","explainability","| Field | Response |\n| -- | -- |\n| Intended Application(s) & Domain(s): | Object Detection (People). |\n| Model Type: | 3D Object Detection. |\n| Intended Users: | This model is intended for developers working in industrial, robotics, and smart space applications. |\n| Output: | 3-D Bounding Box(es), Category Label(s), Confidence Score(s) |\n| Describe how the model works: | Detects one or more objects from fused LIDAR point cloud features and camera RGB features. |\n| Technical Limitations: | This model may not work well for detecting partially or fully occluded persons. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Mean Average Precision (mAP) |\n| Licensing: | [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) |","privacy","| Field | Response |\n| -- | -- |\n| Generatable or reverse engineerable personally-identifiable information ? | None |\n| Protected classes used to create this model? | Not Applicable (No personal data) |\n| Was consent obtained for any personal data used? | Not Applicable (No personal data) |\n| How often is dataset reviewed? | \tBefore Release |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable |\n| If personal data collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable |\n| If personal data collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects?\t| Not Applicable |\n| If personal data collected for the development of this AI model, was it minimized to only what was required? | Not Applicable |\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\t| [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/) |","safetyAndSecurity","| Field | Response |\n| -- | -- |\n| Model Application(s): | Object Detection (Robotics, Retail) |\n| Describe the life-critical application (if present). | None |\n| Use Case Restrictions: | Abide by [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/) |\n| Describe access restrictions (if any): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. |",[],[282,283,284,285,286,287],[],[],[],{"_91":144,"_128":145,"_129":298,"_131":132},{"_91":296,"_128":296,"_129":297,"_131":132},{"_91":294,"_128":294,"_129":295,"_131":132},{"_91":292,"_128":292,"_129":293,"_131":132},{"_91":92,"_128":92,"_129":291,"_131":132},{"_91":288,"_128":288,"_129":289,"_131":132},"TAO Toolkit",{"_133":290,"_128":288},"framework",{"_133":132,"_128":92},"NSPECT-FY5H-V6CO",{"_133":132,"_128":292},"Deep Learning",{"_133":132,"_128":294},"CV",{"_133":132,"_128":296},{"_133":137,"_128":145},{"_176":1468,"_178":1469},"ngcApps",[1421],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1403":1404,"_1405":1406,"_1407":1408,"_1409":1410,"_1411":310,"_203":334,"_1412":1413,"_1401":51,"_1414":155,"_1415":155,"_1416":1417,"_1418":155,"_1419":1420},"amplitude",{"_1399":1400,"_1401":51,"_1007":-7,"_1402":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1393":1394,"_1395":1396,"_1397":1398},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1282":229,"_1283":1284,"_1285":1286,"_1287":1288},"announcements",[],"consentState",{"_373":1272,"_1273":26,"_1274":26,"_1275":155,"_1276":1277},"consentConfig",{"_355":356,"_357":358,"_359":360,"_361":362,"_363":364,"_365":366,"_367":368,"_369":370,"_371":372,"_373":374},"showContactAdmin",1783025332618,[],[],[348,349,350,351,352,353],{"_40":55,"_42":66,"_44":171,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":171,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":62,"_44":171,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":171,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":354,"_50":51},{"_40":41,"_42":43,"_44":171,"_46":-5,"_48":-5,"_50":51},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,[524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558],[397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413],{"_414":520,"_91":520,"_90":229,"_387":521},{"_414":515,"_91":515,"_90":229,"_387":516},{"_414":511,"_91":511,"_90":229,"_387":512},{"_414":506,"_91":506,"_90":229,"_387":507},{"_414":501,"_91":501,"_90":229,"_387":502},{"_414":489,"_91":489,"_90":229,"_387":490},{"_414":477,"_91":477,"_90":229,"_387":478},{"_414":472,"_91":472,"_90":229,"_387":473},{"_414":461,"_91":461,"_90":229,"_387":462},{"_414":456,"_91":456,"_90":229,"_387":457},{"_414":451,"_91":451,"_90":229,"_387":452},{"_414":446,"_91":446,"_90":229,"_387":447},{"_414":441,"_91":441,"_90":229,"_387":442},{"_414":436,"_91":436,"_90":229,"_387":437},{"_414":431,"_91":431,"_90":229,"_387":432},{"_414":424,"_91":424,"_90":229,"_387":425},{"_414":415,"_91":415,"_90":229,"_387":416},"hostName","amazon-adsystem.com",[417,418],{"_256":423,"_420":415,"_421":26,"_90":422},{"_256":419,"_420":415,"_421":26,"_90":422},"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",[426,427],{"_256":430,"_420":424,"_421":26,"_90":429},{"_256":428,"_420":424,"_421":26,"_90":429},"IDE","This domain is owned by Doubleclick (Google). 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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",[463,464,465,466],{"_256":471,"_420":461,"_421":26,"_90":468},{"_256":470,"_420":461,"_421":26,"_90":468},{"_256":469,"_420":461,"_421":26,"_90":468},{"_256":467,"_420":461,"_421":26,"_90":468},"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",[474],{"_256":475,"_420":472,"_421":26,"_90":476},"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",[479,480,481,482],{"_256":488,"_420":477,"_421":26,"_90":484},{"_256":486,"_420":477,"_421":26,"_90":487},{"_256":485,"_420":477,"_421":26,"_90":484},{"_256":483,"_420":477,"_421":26,"_90":484},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[491,492,493,494],{"_256":500,"_420":489,"_421":26,"_90":498},{"_256":499,"_420":489,"_421":26,"_90":498},{"_256":497,"_420":489,"_421":26,"_90":498},{"_256":495,"_420":489,"_421":26,"_90":496},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[503],{"_256":504,"_420":501,"_421":26,"_90":505},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[508],{"_256":509,"_420":506,"_421":26,"_90":510},"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",[513],{"_256":514,"_420":511,"_421":26,"_90":455},"R","6sc.co",[517],{"_256":518,"_420":515,"_421":26,"_90":519},"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.",[646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666],[611,612,613,614,615],{"_414":506,"_91":506,"_90":229,"_387":637},{"_414":472,"_91":472,"_90":229,"_387":632},{"_414":625,"_91":625,"_90":229,"_387":626},{"_414":621,"_91":621,"_90":229,"_387":622},{"_414":616,"_91":616,"_90":229,"_387":617},"nvcrm.my.site.com",[618],{"_256":619,"_420":616,"_421":26,"_90":620},"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",[623],{"_256":624,"_420":621,"_421":26,"_90":455},"edge","github.com",[627,628],{"_256":631,"_420":625,"_421":26,"_90":630},{"_256":629,"_420":625,"_421":26,"_90":630},"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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