ImageNet NV-DINOV2

Model Overview

Image Classification is a popular computer vision technique in which an image is classified into one of the designated classes based on the image features.

Model Architecture

NV-Dinov2 is a visual foundational model trained on NVIDIA proprietary large scale dataset. Dinov2 is a self-supervised learning method that uses a combination of two SSL techniques : DINO and iBOT. These models could greatly simplify the use of images in any system by producing all purpose visual features, i.e., features that work across image distributions and tasks without finetuning. Trained on large curated datasets, our model has learnt robust fine-grained representation useful for localization and classification tasks. This model can be used as a foundation model for a variety of downstream tasks with few labeled examples. For more details on the method please refer: Dinov2.

The model in this model card uses the NVDinov2 pre-trained ViT-L backbone and fine-tunes a Linear Regression Classifier on ImageNet dataset.

Training Data

NV-Dinov2 was pre-trained on NVIDIA proprietary collected data that are of commercial license. The model with linear probe head was fine-tuned on ImageNet dataset ImageNet1K dataset.

Performance

Evaluation Data

The evaluation data is ImagetNet-1k.ImageNet1K dataset.

Methodology and KPI

The performance is measure in top-1 accuracy on Imagenet.

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

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

model	top-1 accuracy
ImageNet validation	79.9

Real-time Inference Performance

The inference is run on the provided unpruned model at FP16 precision. The inference performance is run using trtexec. The end-to-end performance with streaming video data might slightly vary depending on other bottlenecks in the hardware and software.

NVDinoV2 (224x224 resolution)

\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\">

Platform	BS	FPS
Orin NX 16GB	16	31.55
AGX Orin 64GB	16	81.41
A2	16	72.7
T4	4	110.3
A30	16	461.0
L4	4	275.0
L40	8	579.0
A100	32	1031.0
H100	64	2500.6

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 DeepStream SDK.

The model can be used to classify objects from photos and videos by using appropriate video or image decoding and pre-processing. The model is a binary classifier which predicts whether a component is present or missing.

The models are intended for easy edge deployment using DeepStream SDK. DeepStream provides the facilities to create efficient video analytic pipelines to capture, decode, and pre-process the data before running inference.

Input

Input RGB image dimensions: B X 3 X 224 X 224 (B C H W) - B X 3 X 518 X 518 (B C H W)

Channel Ordering of the Input: NCHW, where N = Batch Size, C = number of channels (3), H = Height of images (224), W = Width of the images (224)

Output

The output is a 1000 x 1 output where each value is the confidence score of respective class.

Input image

Instructions to deploy these models with DeepStream

To create an end-to-end video analytics application, deploy this model with DeepStream SDK. DeepStream SDK is a streaming analytics toolkit to accelerate deployment of AI-based video analytics applications. The model can be integrated directly into deepstream by following the instructions mentioned below.

To deploy these models with DeepStream 6.1, please follow the instructions below:

Download and install DeepStream SDK. The installation instructions for DeepStream are provided in\nDeepStream development guide.

/opt/nvidia/deepstream is the default DeepStream installation directory. This path will be different if you are installing in a different directory.

Two extra files are required which are provided in NVIDIA-AI-IOT.

\n
A label file: containing the names of the classes that the model is trained to classify against. The order in which the classes are listed must match the order in which the model predicts the output. Here is a sample file for the Imagenet Classification model:
\n
\n
A DeepStream configuration file: Here are the key parameters in pgie_nvdinov2_classification_tao_config.txt and labels file imagenet_labels_file.txt.
\n

gpu-id=0\nnet-scale-factor=1.0\noffsets=123.675;116.28;103.53\nmodel-color-format=0\nlabelfile-path=imagenet_labels_file.txt\nonnx-file=epoch_10_onnx.onnx\nbatch-size=1\nnetwork-mode=2\ninterval=0\ngie-unique-id=1\nnetwork-type=1\nscaling-filter=1\nscaling-compute-hw=1\nclassifier-threshold=0.5\n

Run ds-tao-classifier:

Classification

ds-tao-classifier  -c pgie_nvdinov2_classification_tao_config.txt -i file:///path/to/img.jpg\n

Documentation to deploy with DeepStream is provided in \"Deploying to DeepStream\" chapter of TAO User Guide.

Limitations

This model was fine-tuned on the ImageNet1K dataset with 1000 object categories. Hence, the model may not perform well on different data distributions, so we recommend conducting further fine tuning on the target domain to get higher accuracy.

References

Using TAO Pre-trained Models

Get TAO Container
Get other Purpose-built models from NGC model registry:\n
- PeopleNet
- TrafficCamNet
- FaceDetectIR
- VehicleMakeNet
- VehicleTypeNet
\n

Technical blogs

Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - Part 1 | Part 2
Learn how to train real-time License plate detection and recognition app with TAO and DeepStream.
Model accuracy is extremely important, learn how you can achieve state of the art accuracy for classification and object detection models using TAO
Learn how to train Instance segmentation model using MaskRCNN with TAO
Read the technical tutorial on how PeopleNet model can be trained with custom data using Transfer Learning Toolkit
Learn how to train and deploy real-time intelligent video analytics apps and services using DeepStream SDK

License

This work is licensed under the Creative Commons Attribution NonCommercial ShareAlike 4.0 License (CC-BY-NC-SA-4.0). To view a copy of this license, please visit this link, or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

Ethical Considerations

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 developers to ensure that it meets the requirements for the relevant industry and use case, that the necessary instructions 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.

","resourceVersions",[190,191,192,193],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_164":21,"_165":166,"_48":167,"_168":-5,"_169":170,"_171":172,"_173":78,"_174":16,"_175":84,"_176":-5,"_177":30,"_178":-5,"_179":30,"_180":-5,"_181":30,"_182":-5,"_183":30,"_184":-5,"_185":30,"_186":-5,"_187":30,"_188":189},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/tao/models/imagenet_nv_dinov2/-","version","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/tao/imagenet_nv_dinov2:deployable_v1.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/tao/models/imagenet_nv_dinov2/deployable_v1.0/version-history","accessCheckResult",{"_163":84},"hasRelatedCollections","context",{"_159":160,"_161":162},1787526335532,[],[],[152,153,154,155,156,157,158],{"_44":58,"_46":68,"_48":60,"_50":61,"_52":69,"_54":55},{"_44":58,"_46":66,"_48":60,"_50":61,"_52":67,"_54":55},{"_44":58,"_46":65,"_48":60,"_50":61,"_52":64,"_54":55},{"_44":58,"_46":63,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":59,"_48":60,"_50":61,"_52":62,"_54":55},{"_44":56,"_46":57,"_48":49,"_50":51,"_52":53,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55},"orgName","nvidia","teamName","tao","allowed","versionId","description","","UPLOAD_COMPLETE","releaseType","createdDate",["D",1754419450225],"updatedDate",["D",1754419460884],"totalSizeInBytes","totalFileCount","isSigned","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_74":199,"_76":200,"_168":-5,"_175":84,"_177":30,"_188":197,"_179":30},{"_74":21,"_76":21,"_168":-5,"_175":84,"_177":30,"_188":198,"_179":30},{"_74":196,"_76":196,"_168":-5,"_175":84,"_177":30,"_188":197,"_179":30},{"_74":194,"_76":194,"_168":-5,"_175":84,"_177":30,"_188":195,"_179":30},"trainable_v1.1",[],"deployable_v1.1",[],[],"-","Latest (deployable_v1.1)","nvidia/tao/imagenet_nv_dinov2","key","nvidia/tao/models/imagenet_nv_dinov2","resourceType","models","resourceId","imagenet_nv_dinov2","shortDescription","ImageNet NV-DINOV2","## ImageNet NV-DINOV2\n\n### Model Overview \n\nImage Classification is a popular computer vision technique in which an image is classified into one of the designated classes based on the image features.\n\n### Model Architecture \n\nNV-Dinov2 is a visual foundational model trained on NVIDIA proprietary large scale dataset. [Dinov2](https://arxiv.org/pdf/2304.07193.pdf) is a self-supervised learning method that uses a combination of two SSL techniques : DINO and iBOT. These models could greatly simplify the use of images in any system by producing all purpose visual features, i.e., features that work across image distributions and tasks without finetuning. Trained on large curated datasets, our model has learnt robust fine-grained representation useful for localization and classification tasks. This model can be used as a foundation model for a variety of downstream tasks with few labeled examples. For more details on the method please refer: [Dinov2](https://arxiv.org/pdf/2304.07193.pdf).\n\nThe model in this model card uses the NVDinov2 pre-trained ViT-L backbone and fine-tunes a Linear Regression Classifier on ImageNet dataset.\n\n#### Training Data \n\nNV-Dinov2 was pre-trained on NVIDIA proprietary collected data that are of commercial license. The model with linear probe head was fine-tuned on ImageNet dataset [ImageNet1K dataset](https://www.image-net.org/challenges/LSVRC/index.php).\n\n### Performance\n\n#### Evaluation Data \n\nThe evaluation data is ImagetNet-1k.[ImageNet1K dataset](https://www.image-net.org/challenges/LSVRC/index.php).\n\n##### Methodology and KPI\n\nThe performance is measure in top-1 accuracy on Imagenet.\n\n| model | top-1 accuracy |\n|-------------------------------|----------------|\n| ImageNet validation | 79.9 |\n\n#### Real-time Inference Performance \n\nThe inference is run on the provided unpruned model at FP16 precision. The inference performance is run using [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/main/samples/trtexec). The end-to-end performance with streaming video data might slightly vary depending on other bottlenecks in the hardware and software.\n\nNVDinoV2 (224x224 resolution)\n\n| Platform | BS | FPS |\n|------------------|-----|--------|\n| Orin NX 16GB | 16 | 31.55 |\n| AGX Orin 64GB | 16 | 81.41 |\n| A2 | 16 | 72.7 |\n| T4 | 4 | 110.3 |\n| A30 | 16 | 461.0 |\n| L4 | 4 | 275.0 |\n| L40 | 8 | 579.0 |\n| A100 | 32 | 1031.0 |\n| H100 | 64 | 2500.6 |\n\n### How to use this model \n\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 [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk).\n\nThe model can be used to classify objects from photos and videos by using appropriate video or image decoding and pre-processing. The model is a binary classifier which predicts whether a component is present or missing.\n\nThe models are intended for easy edge deployment using DeepStream SDK. DeepStream provides the facilities to create efficient video analytic pipelines to capture, decode, and pre-process the data before running inference.\n\n#### Input\n\n* Input RGB image dimensions: B X 3 X 224 X 224 (B C H W) - B X 3 X 518 X 518 (B C H W)\n\nChannel Ordering of the Input: NCHW, where N = Batch Size, C = number of channels (3), H = Height of images (224), W = Width of the images (224)\n\n#### Output\n\nThe output is a 1000 x 1 output where each value is the confidence score of respective class.\n\n##### Input image\n\n#### Instructions to deploy these models with DeepStream\n\nTo create an end-to-end video analytics application, deploy this model with [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk). DeepStream SDK is a streaming analytics toolkit to accelerate deployment of AI-based video analytics applications. The model can be integrated directly into deepstream by following the instructions mentioned below.\n\nTo deploy these models with [DeepStream 6.1](https://developer.nvidia.com/deepstream-sdk), please follow the instructions below:\n\n[Download](https://developer.nvidia.com/deepstream-sdk) and install DeepStream SDK. The installation instructions for DeepStream are provided in\n[DeepStream development guide](https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html).\n\n`/opt/nvidia/deepstream` is the default DeepStream installation directory. This path will be different if you are installing in a different directory.\n\nTwo extra files are required which are provided in [NVIDIA-AI-IOT](https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps/tree/master/configs/nvinfer/multi_task_tao).\n\n1. A label file: containing the names of the classes that the model is trained to classify against. The order in which the classes are listed must match the order in which the model predicts the output. Here is a sample file for the Imagenet Classification model:\n\n2. A DeepStream configuration file: Here are the key parameters in `pgie_nvdinov2_classification_tao_config.txt` and labels file `imagenet_labels_file.txt`.\n\n```sh\ngpu-id=0\nnet-scale-factor=1.0\noffsets=123.675;116.28;103.53\nmodel-color-format=0\nlabelfile-path=imagenet_labels_file.txt\nonnx-file=epoch_10_onnx.onnx\nbatch-size=1\nnetwork-mode=2\ninterval=0\ngie-unique-id=1\nnetwork-type=1\nscaling-filter=1\nscaling-compute-hw=1\nclassifier-threshold=0.5\n```\n\nRun `ds-tao-classifier`:\n\n##### Classification\n\n```sh\nds-tao-classifier -c pgie_nvdinov2_classification_tao_config.txt -i file:///path/to/img.jpg\n```\n\nDocumentation to deploy with DeepStream is provided in \"Deploying to DeepStream\" chapter of [TAO User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\n### Limitations \n\nThis model was fine-tuned on the ImageNet1K dataset with 1000 object categories. Hence, the model may not perform well on different data distributions, so we recommend conducting further fine tuning on the target domain to get higher accuracy.\n\n### References\n\n#### Using TAO Pre-trained Models \n\n- Get [TAO Container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit)\n- Get other Purpose-built models from NGC model registry:\n - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)\n - [TrafficCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:trafficcamnet)\n - [FaceDetectIR](https://ngc.nvidia.com/catalog/models/nvidia:tao:facedetectir)\n - [VehicleMakeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehiclemakenet)\n - [VehicleTypeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehicletypenet)\n\n#### Technical blogs \n\n- Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - [Part 1](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-1) | [Part 2](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-2)\n- Learn how to train [real-time License plate detection and recognition app](https://developer.nvidia.com/blog/creating-a-real-time-license-plate-detection-and-recognition-app) with TAO and DeepStream.\n- Model accuracy is extremely important, learn how you can achieve [state of the art accuracy for classification and object detection models](https://developer.nvidia.com/blog/preparing-state-of-the-art-models-for-classification-and-object-detection-with-tao-toolkit/) using TAO\n- Learn how to train [Instance segmentation model using MaskRCNN with TAO](https://developer.nvidia.com/blog/training-instance-segmentation-models-using-maskrcnn-on-tao-toolkit/)\n- Read the technical tutorial on how [PeopleNet model can be trained with custom data using Transfer Learning Toolkit](https://devblogs.nvidia.com/training-custom-pretrained-models-using-tlt/)\n- Learn how to [train and deploy real-time intelligent video analytics apps and services using DeepStream SDK](https://devblogs.nvidia.com/building-iva-apps-using-deepstream-5.0/)\n\n#### Suggested reading \n\n- More information on about TAO Toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/tao-toolkit)\n- Read the [TAO Quick Start](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html) guide and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html).\n- If you have any questions or feedback, please refer to the discussions on [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17)\n- Deploy your model on the edge using DeepStream. Learn more about [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk)\n\n### License \n\nThis work is licensed under the Creative Commons Attribution NonCommercial ShareAlike 4.0 License (CC-BY-NC-SA-4.0). To view a copy of this license, please visit this [link](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode), or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.\n\n### Ethical Considerations \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 developers to ensure that it meets the requirements for the relevant industry and use case, that the necessary instructions 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","ImageNet NV-DINOv2",["D",1754419448640],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/imagenet_nv_dinov2","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[242,243,244,245],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_211":260,"_247":261,"_248":262,"_250":251},{"_211":258,"_247":258,"_248":259,"_250":251},{"_211":254,"_247":255,"_248":256,"_250":251},{"_211":246,"_247":246,"_248":249,"_250":251},"TAO Toolkit","value","filter",{"_252":253,"_247":246},"category","label","field","framework","NVIDIA AI","soln_nvidia_ai",{"_252":257,"_247":255},"technology","NSPECT-KHQL-F1M4",{"_252":251,"_247":258},"Computer Vision","soln_computer_vision",{"_252":257,"_247":261},{"_105":1240,"_107":1241},"ngcApps",[1193],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1174":1175,"_1176":1177,"_1178":1179,"_1180":1181,"_1182":274,"_136":300,"_1183":1184,"_1185":55,"_1186":84,"_1187":84,"_1188":1189,"_1190":84,"_1191":1192},"amplitude",{"_1171":1172,"_813":-7,"_1173":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1165":1166,"_1167":1168,"_1169":1170},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787526335475,"1.12.0","defaultSearchSuggestions",{"_1053":166,"_1054":1055,"_1056":1057,"_1058":1059},"announcements",[],"consentState",{"_339":1043,"_1044":84,"_1045":30,"_1046":30,"_1047":1048},"consentConfig",{"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336,"_337":338,"_339":340},"showContactAdmin",1787526335476,[],[],[314,315,316,317,318,319],{"_44":58,"_46":68,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":66,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":65,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":59,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":320,"_54":55},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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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,[478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499],[363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379],{"_380":473,"_211":473,"_165":166,"_353":474},{"_380":469,"_211":469,"_165":166,"_353":470},{"_380":464,"_211":464,"_165":166,"_353":465},{"_380":460,"_211":460,"_165":166,"_353":461},{"_380":455,"_211":455,"_165":166,"_353":456},{"_380":450,"_211":450,"_165":166,"_353":451},{"_380":442,"_211":442,"_165":166,"_353":443},{"_380":434,"_211":434,"_165":166,"_353":435},{"_380":429,"_211":429,"_165":166,"_353":430},{"_380":424,"_211":424,"_165":166,"_353":425},{"_380":419,"_211":419,"_165":166,"_353":420},{"_380":414,"_211":414,"_165":166,"_353":415},{"_380":409,"_211":409,"_165":166,"_353":410},{"_380":402,"_211":402,"_165":166,"_353":403},{"_380":397,"_211":397,"_165":166,"_353":398},{"_380":390,"_211":390,"_165":166,"_353":391},{"_380":381,"_211":381,"_165":166,"_353":382},"hostName","amazon-adsystem.com",[383,384],{"_76":389,"_386":381,"_387":30,"_165":388},{"_76":385,"_386":381,"_387":30,"_165":388},"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",[392,393],{"_76":396,"_386":390,"_387":30,"_165":395},{"_76":394,"_386":390,"_387":30,"_165":395},"IDE","This domain is owned by Doubleclick (Google). 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If you do not allow these cookies, then some or all of these services may not function properly.",[570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594],[541,542,543,544,545],{"_380":455,"_211":455,"_165":166,"_353":565},{"_380":429,"_211":429,"_165":166,"_353":560},{"_380":555,"_211":555,"_165":166,"_353":556},{"_380":550,"_211":550,"_165":166,"_353":551},{"_380":546,"_211":546,"_165":166,"_353":547},"nvidia.partners",[548],{"_76":549,"_386":546,"_387":30,"_165":423},"edge","app.hushly.com",[552],{"_76":553,"_386":550,"_387":30,"_165":554},"*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",[557],{"_76":558,"_386":555,"_387":30,"_165":559},"_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.",[653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681],[633,634,635,636],{"_380":648,"_211":648,"_165":166,"_353":649},{"_380":643,"_211":643,"_165":166,"_353":644},{"_380":442,"_211":442,"_165":166,"_353":640},{"_380":555,"_211":555,"_165":166,"_353":637},[638],{"_76":639,"_386":555,"_387":84,"_165":559},"_gh_sess",[641],{"_76":642,"_386":442,"_387":84,"_165":449},"YSC","bizible.com",[645],{"_76":646,"_386":643,"_387":30,"_165":647},"_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",[650],{"_76":651,"_386":648,"_387":30,"_165":652},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_76":723,"_386":501,"_387":30,"_165":724},{"_76":708,"_386":722,"_387":84,"_165":709},{"_76":691,"_386":504,"_387":30,"_165":616},{"_76":691,"_386":604,"_387":30,"_165":616},{"_76":720,"_386":501,"_387":30,"_165":721},{"_76":708,"_386":507,"_387":84,"_165":709},{"_76":718,"_386":501,"_387":30,"_165":719},{"_76":708,"_386":504,"_387":84,"_165":709},{"_76":716,"_386":603,"_387":30,"_165":717},{"_76":708,"_386":625,"_387":84,"_165":709},{"_76":688,"_386":504,"_387":30,"_165":690},{"_76":714,"_386":501,"_387":84,"_165":715},{"_76":712,"_386":501,"_387":30,"_165":713},{"_76":710,"_386":501,"_387":84,"_165":711},{"_76":708,"_386":599,"_387":84,"_165":709},{"_76":706,"_386":501,"_387":30,"_165":707},{"_76":704,"_386":501,"_387":30,"_165":705},{"_76":702,"_386":501,"_387":84,"_165":703},{"_76":700,"_386":608,"_387":30,"_165":701},{"_76":688,"_386":599,"_387":30,"_165":690},{"_76":698,"_386":501,"_387":30,"_165":699},{"_76":696,"_386":501,"_387":84,"_165":697},{"_76":694,"_386":501,"_387":30,"_165":695},{"_76":692,"_386":501,"_387":30,"_165":693},{"_76":691,"_386":501,"_387":30,"_165":616},{"_76":688,"_386":689,"_387":30,"_165":690},{"_76":686,"_386":501,"_387":30,"_165":687},{"_76":684,"_386":501,"_387":84,"_165":685},{"_76":682,"_386":501,"_387":30,"_165":683},"_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. 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