PeopleNet Model Card

Model Overview

The Peoplenet model detects persons, bags, and faces in an image. This model 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)

Using TAO Pre-trained Models

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

Model Architecture

Architecture Type: Convolution Neural Network (CNN)
\nNetwork Architecture: DetectNet_v2 + ResNet34 (Feature Extractor)

This model is based on NVIDIA DetectNet_v2 detector with ResNet34 as feature extractor. This architecture, also known as GridBox object detection, uses bounding-box regression on a uniform grid on the input image. Gridbox system divides an input image into a grid which predicts four normalized bounding-box parameters (xc, yc, w, h) and confidence value per output class.

The raw normalized bounding-box and confidence detections needs to be post-processed by a clustering algorithm such as DBSCAN or NMS to produce final bounding-box coordinates and category labels.

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: NCHW, 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

Output:

Output Type(s): Label(s), Bounding-Box(es), Confidence Scores
\nOutput Format: Label: Text String(s); Bounding Box: (x-coordinate, y-coordinate, width, height), Confidence Scores: Floating Point
\nOther Properties Related to Output: Category Label(s): Bag, Face, Person, Bounding Box Coordinates, Confidence Scores

Output image

Software Integration:

Runtime Engine(s):

TAO 5.1
DeepStream 6.1 or later

Supported Hardware Architecture Compatibility:

Ampere
Jetson
Hopper
Lovelace
Pascal
Turing

Preferred Operating System(s):

Linux
Linux 4 Tegra

Model versions

trainable_v2.5 - Unpruned ResNet34 based pre-trained floating-point model. Intended for transfer learning.
pruned_v2.3 - Pruned ResNet34 floating point deployment model intended for applications with limited computational budget.
pruned_quantized_v2.3 – Pruned ResNet34 INT8 deployment models including deployments on DLA. Contains calibration cache and can be used to run inference on GPU as well as Jetson AGX Xavier or Xavier NX DLA. Intended for applications with limited computational budget.
deployable_quantized_v2.5 – Unpruned ResNet34 deployment models including floating point and INT8 deployments on GPU and DLA. Contains calibration cache and can be used to run inference on GPU as well as Jetson AGX Xavier or Xavier NX DLA. Intended for applications requiring best accuracy.

Training

Data Collection Method by dataset\n
- Automatic/Sensors
\n
Labeling Method by dataset:\n
- Human
\n

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. In the second phase the network is retrained using quantization-aware training (QAT).

Training Data Properties

Internal, proprietary dataset in excess of 7.6 million images of more than 71 million people. The training dataset consists of a mix of cameras, camera heights, crowd-density, and field-of view (FOV) taken from a camera. Approximately half of the training data consisted of images captured in an indoor office environment. The camera is typically set up at approximately 10 feet height, 45-degree angle and has close field-of-view. This was done to improve accuracy of the models for convenience-store retail analytics use-case. We have also added approximately 500 thousand images with low-density scenes with people extending their hands and feet to improve the performance for use-cases where person object detection is followed by pose-estimation.

\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\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	Object	Distribution
Category	Number of Images	Number of Persons	Number of Bags	Number of Faces
Natural	4804552	23085430	8061920	10786381
Rotated	5746323	19930535	7234679	10094039
Broadcast	566408	566408	0	358518
Broadcast Rotated	369895	369895	0	59104
Blended	24841	26041	11225	0
Blended Rotated	24335	24523	9707	0
Simulation	27417	368914	0	92916
Total	7656570	41334979	12280764	18354191

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 PeopleNet model. If you are looking to re-train with your own dataset, please follow the guideline below for highest accuracy.

PeopleNet 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

Performance

Evaluation Data Properties

Data Collection Method by dataset\n
- Automatic/Sensors
\n
Labeling Method by dataset:\n
- Human
\n

90000 images from an Internal, proprietary dataset in excess of 7.6 million images of more than 71 million people. The training dataset consists of a mix of cameras, camera heights, crowd-density, and field-of view (FOV) taken from a camera. Approximately half of the training data consisted of images captured in an indoor office environment. The camera is typically set up at approximately 10 feet height, 45-degree angle and has close field-of-view. This was done to improve accuracy of the models for convenience-store retail analytics use-case. We have also added approximately 500 thousand images with low-density scenes with people extending their hands and feet to improve the performance for use-cases where person object detection is followed by pose-estimation.

Methodology and KPI

The true positives, false positives, false negatives are calculated using intersection-over-union (IOU) criterion greater than 0.5. In addition, we have also added KPI with IOU criterion of greater than 0.8 for low-density and extended-hand sequences where tight bounding box is a requirement for subsequent human pose estimation algorithms. The KPI for the evaluation data are reported in the table below. Model is evaluated based on precision, recall and accuracy.

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

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

Network	PeopleNet	V2.6	FP16	PeopleNet	V2.6	INT8
Content	Precision	Recall	Accuracy	Precision	Recall	Accuracy
Generic	91.82	88.60	82.17	92.97	85.33	80.21
Office	95.64	91.92	88.22	96.08	89.48	86.33
Café	93.38	86.97	81.92	94.62	81.81	78.18
People (IOU > 0.8)	80.41	78.13	65.71	78.71	73.90	61.68
Extended-hands (IOU > 0.8)	93.98	86.82	82.25	94.41	86.80	82.55

Real-time Inference Performance

The inference is run on the provided unpruned model at INT8 precision. The inference performance is run using trtexec on Jetson AGX Xavier, Xavier NX, Orin, Orin NX and NVIDIA T4, and Ampere GPU. The Jetson devices are running at Max-N configuration for maximum GPU frequency. The performance shown here is the inference only performance. The end-to-end performance with streaming video data might slightly vary depending on other bottlenecks in the hardware and software.

\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\">

Model Arch	Version	Inference Resolution	Precision	Xavier NX		AGX Xavier
PeopleNet	Unpruned	960x544x3	INT8	GPU	DLA1+DLA2	GPU	DLA1+DLA2
Resnet34	v2.6			79	46	136	58

\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\">

Model Arch	Version	Inference Resolution	Precision	Orin NX		AGX Orin
PeopleNet	Unpruned	960x544x3	INT8	GPU	DLA1+DLA2	GPU	DLA1+DLA2
Resnet34	v2.6			140	70	421	104

\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\">

Model Arch	Version	Inference Resolution	Precision	T4	A100	A30	A10	A2
PeopleNet	Unpruned	960x544x3	INT8	GPU	GPU	GPU	GPU	GPU
Resnet34	v2.6			465	3291	1603	1007	231

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 primary use case intended for these models is detecting people in a color (RGB) image. The model can be used to detect people from photos and videos by using appropriate video or image decoding and pre-processing. As a secondary use case the model can also be used to detect bags and faces from images or videos. However, these additional classes are not the main intended use for these models.

There are two flavors of these models:

trainable (unpruned)
deployable (unpruned quantized)

The trainable or unpruned models intended for training using TAO Toolkit and the user's own dataset. 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.

The deployable_quantized models share the same structure as the trainable or unpruned model, however, these models have been trained by employing Quantization Aware Training and is intended for int-8 deployment.

The trainable (unpruned), deployable (unpruned quantized) models are encrypted and will only operate with the following key:

Model load key: tlt_encode

Please make sure to use this as the key for all TAO commands that require a model load key.

Instructions to use unpruned model with TAO

In order, to use these models as a pretrained weights for transfer learning, please use the snippet below as template for the model_config component of the experiment spec file to train a DetectNet_v2 model. For more information on the experiment spec file, please refer to the TAO Toolkit User Guide.

For ResNet34

model_config {\n  num_layers: 34\n  pretrained_model_file: \"/path/to/the/model.tlt\"\n  use_batch_norm: true\n  objective_set {\n    bbox {\n      scale: 35.0\n      offset: 0.5\n    }\n    cov {\n    }\n  }\n  training_precision {\n    backend_floatx: FLOAT32\n  }\n  arch: \"resnet\"\n  all_projections: true\n}\n

Instructions to deploy these models with DeepStream

To create the entire 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 pruned model included here can be integrated directly into deepstream by following the instructions mentioned\nbelow.

Run the default deepstream-app included in the DeepStream docker, by simply executing the commands below.

## Download Model:\n\nmkdir -p $HOME/peoplenet && \\\nwget https://api.ngc.nvidia.com/v2/models/nvidia/tao/peoplenet/versions/pruned_v2.1/files/resnet34_peoplenet_pruned.etlt \\\n-O $HOME/peoplenet/resnet34_peoplenet_pruned.etlt\n\n## Run Application\n\nxhost +\nsudo docker run --gpus all -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v $HOME:/opt/nvidia/deepstream/deepstream-5.1/samples/models/tlt_pretrained_models \\\n-w /opt/nvidia/deepstream/deepstream-5.1/samples/configs/tlt_pretrained_models nvcr.io/nvidia/deepstream:5.1-21.02-samples \\\ndeepstream-app -c deepstream_app_source1_peoplenet.txt\n

\n
Install deepstream on your local host and run the deepstream-app.
\n
Download and install DeepStream SDK. The installation instructions for DeepStream are provided in DeepStream development guide. The config files for the purpose-built models are located in:
\n
```
/opt/nvidia/deepstream/deepstream-5.1/samples/configs/tlt_pretrained_models\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
You will need 2 config files and 1 label file. These files are provided in the tlt_pretrained_models directory.
\n
```
deepstream_app_source1_peoplenet.txt - Main config file for DeepStream app\nconfig_infer_primary_peoplenet.txt - File to configure inference settings\nlabels_peoplenet.txt - Label file with 3 classes\n
```
\n
Key Parameters in config_infer_primary_peoplenet.txt
\n
```
tlt-model-key=\ntlt-encoded-model=\nlabelfile-path=\nint8-calib-file=\ninput-dims=\nnum-detected-classes=\n
```
\n
Run deepstream-app:
\n
```
deepstream-app -c deepstream_app_source1_peoplenet.txt\n
```
\n
Documentation to deploy with DeepStream is provided in \"Deploying to DeepStream\" chapter of TAO User Guide.
\n

Limitations

Very Small Objects

NVIDIA PeopleNet model were trained to detect objects larger than 10x10 pixels. Therefore it may not be able to detect objects that are smaller than 10x10 pixels.

Occluded Objects

When objects are occluded or truncated such that less than 20% of the object is visible, they may not be detected by the PeopleNet model. For people class objects, the model will detect occluded people as long as head and shoulders are visible. However if the person’s head and/or shoulders are not visible, the object might not be detected unless more than 60% of the person is visible.

Dark-lighting, Monochrome or Infrared Camera Images

The PeopleNet model were trained on RGB images in good lighting conditions. Therefore, images captured in dark lighting conditions or a monochrome image or IR camera image may not provide good detection results.

Warped and Blurry Images

The PeopleNet models were not trained on fish-eye lense cameras or moving cameras. Therefore, the models may not perform well for warped images and images that have motion-induced or other blur.

Face and Bag class

Although bag and face class are included in the model, the accuracy of these classes will be much lower than people class. Some re-training will be required on these classes to improve accuracy.

Technical blogs

Train like a ‘pro’ without being an AI expert using TAO AutoML
Create Custom AI models using NVIDIA TAO Toolkit with Azure Machine Learning
Developing and Deploying AI-powered Robots with NVIDIA Isaac Sim and NVIDIA TAO
Learn endless ways to adapt and supercharge your AI workflows with TAO - Whitepaper
Customize Action Recognition with TAO and deploy with DeepStream
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

License

License to use these models is covered by the Model EULA. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of NVIDIA Open Model License.

Ethical Considerations

NVIDIA PeopleNet 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.

","resourceVersions",[219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241],"resourceVersionsTotal",22,"hasMoreVersions","versionMetadata",{"_192":22,"_193":194,"_49":195,"_196":-5,"_197":198,"_199":200,"_201":202,"_203":16,"_204":31,"_205":-5,"_206":31,"_207":-5,"_208":31,"_209":-5,"_210":31,"_211":-5,"_212":31,"_213":-5,"_214":31,"_215":-5,"_216":31,"_217":218},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/tao/models/peoplenet/-","version","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/tao/peoplenet:deployable_quantized_onnx_v2.6.2","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/tao/models/peoplenet/deployable_quantized_onnx_v2.6.2/version-history","accessCheckResult",{"_191":111},"hasRelatedCollections","context",{"_187":188,"_189":190},1788182375871,[],[],[180,181,182,183,184,185,186],{"_45":60,"_47":71,"_49":62,"_51":63,"_53":72,"_55":56},{"_45":60,"_47":69,"_49":62,"_51":63,"_53":70,"_55":56},{"_45":60,"_47":67,"_49":62,"_51":63,"_53":68,"_55":56},{"_45":60,"_47":65,"_49":127,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":62,"_51":63,"_53":64,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":59,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56},"orgName","nvidia","teamName","tao","allowed","versionId","description","","UPLOAD_COMPLETE","releaseType","createdDate",["D",1712874644572],"updatedDate",["D",1740840463053],"totalSizeInBytes",88954423,"totalFileCount","isSigned","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_81":285,"_83":286,"_196":-5,"_204":111,"_206":31,"_217":284,"_208":31},{"_81":283,"_83":283,"_196":-5,"_204":111,"_206":31,"_217":284,"_208":31},{"_81":281,"_83":281,"_196":-5,"_204":31,"_206":31,"_217":282,"_208":31},{"_81":22,"_83":22,"_196":-5,"_204":31,"_206":31,"_217":280,"_208":31},{"_81":278,"_83":278,"_196":-5,"_204":31,"_206":31,"_217":279,"_208":31},{"_81":276,"_83":276,"_196":-5,"_204":31,"_206":31,"_217":277,"_208":31},{"_81":274,"_83":274,"_196":-5,"_204":31,"_206":31,"_217":275,"_208":31},{"_81":272,"_83":272,"_196":-5,"_204":31,"_206":31,"_217":273,"_208":31},{"_81":270,"_83":270,"_196":-5,"_204":31,"_206":31,"_217":271,"_208":31},{"_81":268,"_83":268,"_196":-5,"_204":31,"_206":31,"_217":269,"_208":31},{"_81":266,"_83":266,"_196":-5,"_204":31,"_206":31,"_217":267,"_208":31},{"_81":264,"_83":264,"_196":-5,"_204":31,"_206":31,"_217":265,"_208":31},{"_81":262,"_83":262,"_196":-5,"_204":31,"_206":31,"_217":263,"_208":31},{"_81":260,"_83":260,"_196":-5,"_204":31,"_206":31,"_217":261,"_208":31},{"_81":258,"_83":258,"_196":-5,"_204":31,"_206":31,"_217":259,"_208":31},{"_81":256,"_83":256,"_196":-5,"_204":31,"_206":31,"_217":257,"_208":31},{"_81":254,"_83":254,"_196":-5,"_204":31,"_206":31,"_217":255,"_208":31},{"_81":252,"_83":252,"_196":-5,"_204":31,"_206":31,"_217":253,"_208":31},{"_81":250,"_83":250,"_196":-5,"_204":31,"_206":31,"_217":251,"_208":31},{"_81":248,"_83":248,"_196":-5,"_204":31,"_206":31,"_217":249,"_208":31},{"_81":246,"_83":246,"_196":-5,"_204":31,"_206":31,"_217":247,"_208":31},{"_81":244,"_83":244,"_196":-5,"_204":31,"_206":31,"_217":245,"_208":31},{"_81":242,"_83":242,"_196":-5,"_204":31,"_206":31,"_217":243,"_208":31},"unpruned_v2.1",[],"unpruned_v2.0",[],"unpruned_v1.0",[],"pruned_v2.1",[],"pruned_v2.0",[],"pruned_v1.0",[],"pruned_quantized_v2.1",[],"pruned_quantized_v2.1.1",[],"pruned_quantized_v2.3",[],"pruned_v2.3",[],"trainable_v2.5",[],"deployable_quantized_v2.5",[],"trainable_v2.6",[],"deployable_quantized_v2.6",[],"deployable_quantized_v2.5.2",[],"deployable_quantized_v2.6.1",[],"pruned_quantized_v2.3.2",[],"trainable_unencrypted_v2.6",[],"pruned_quantized_decrypted_v2.3.3",[],[],"deployable_quantized_onnx_v2.6.3",[],"pruned_quantized_decrypted_v2.3.4",[],"-","Latest (pruned_quantized_decrypted_v2.3.4)","nvidia/tao/peoplenet","key","nvidia/tao/models/peoplenet","resourceType","models","resourceId","peoplenet","shortDescription","3 class object detection network to detect people in an image.","## PeopleNet Model Card\n\n### Model Overview \n\nThe Peoplenet model detects persons, bags, and faces in an image. This model is ready for commercial use. \n\n### References\n#### Citations \n\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#### 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 - [TrafficCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:trafficcamnet)\n - [DashCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:dashcamnet)\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### Model Architecture \n\n**Architecture Type:** Convolution Neural Network (CNN)
\n**Network Architecture:** DetectNet_v2 + ResNet34 (Feature Extractor)\n\nThis model is based on NVIDIA DetectNet_v2 detector with ResNet34 as feature extractor. This architecture, also known as GridBox object detection, uses bounding-box regression on a uniform grid on the input image. Gridbox system divides an input image into a grid which predicts four normalized bounding-box parameters (xc, yc, w, h) and confidence value per output class.\n\nThe raw normalized bounding-box and confidence detections needs to be post-processed by a clustering algorithm such as DBSCAN or NMS to produce final bounding-box coordinates and category labels.\n\n## Input:\n\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: NCHW, 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\n#### Input image\n\n

\n\n## Output:\n\n**Output Type(s):** Label(s), Bounding-Box(es), Confidence Scores
\n**Output Format:** Label: Text String(s); Bounding Box: (x-coordinate, y-coordinate, width, height), Confidence Scores: Floating Point
\n**Other Properties Related to Output:** Category Label(s): Bag, Face, Person, Bounding Box Coordinates, Confidence Scores\n\n#### Output image\n\n

\n\n## Software Integration:\n**Runtime Engine(s):**\n* TAO 5.1
\n* DeepStream 6.1 or later
\n\n**Supported Hardware Architecture Compatibility:**
\n* Ampere
\n* Jetson
\n* Hopper
\n* Lovelace
\n* Pascal
\n* Turing
\n\n**Preferred Operating System(s):**
\n* Linux
\n* Linux 4 Tegra
\n\n### Model versions
\n* **trainable_v2.5** - Unpruned ResNet34 based pre-trained floating-point model. Intended for transfer learning.
\n* **pruned_v2.3** - Pruned ResNet34 floating point deployment model intended for applications with limited computational budget.
\n* **pruned_quantized_v2.3** – Pruned ResNet34 INT8 deployment models including deployments on DLA. Contains calibration cache and can be used to run inference on GPU as well as Jetson AGX Xavier or Xavier NX DLA. Intended for applications with limited computational budget.
\n* **deployable_quantized_v2.5** – Unpruned ResNet34 deployment models including floating point and INT8 deployments on GPU and DLA. Contains calibration cache and can be used to run inference on GPU as well as Jetson AGX Xavier or Xavier NX DLA. Intended for applications requiring best accuracy.
\n\n### Training \n\n- **Data Collection Method by dataset**\n - Automatic/Sensors\n- **Labeling Method by dataset:**\n - 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. In the second phase the network is retrained using quantization-aware training (QAT).\n\n#### Training Data Properties \n\nInternal, proprietary dataset in excess of 7.6 million images of more than 71 million people. The training dataset consists of a mix of cameras, camera heights, crowd-density, and field-of view (FOV) taken from a camera. Approximately half of the training data consisted of images captured in an indoor office environment. The camera is typically set up at approximately 10 feet height, 45-degree angle and has close field-of-view. This was done to improve accuracy of the models for convenience-store retail analytics use-case. We have also added approximately 500 thousand images with low-density scenes with people extending their hands and feet to improve the performance for use-cases where person object detection is followed by pose-estimation. \n\n| | Training | Dataset | Object | Distribution |\n| -- | -- | -- | -- | -- |\n| Category | Number of Images | Number of Persons | Number of Bags | Number of Faces |\n| Natural | 4804552 | 23085430 | 8061920 | 10786381 |\n| Rotated | 5746323 | 19930535 | 7234679 | 10094039 |\n| Broadcast | 566408 | 566408 | 0 | 358518 |\n| Broadcast Rotated | 369895 | 369895 | 0 | 59104 |\n| Blended | 24841 | 26041 | 11225 | 0 |\n| Blended Rotated | 24335 | 24523 | 9707 | 0 |\n| Simulation | 27417 | 368914 | 0 |92916 |\n| Total | 7656570 | 41334979 | 12280764 | 18354191 |\n\n##### Training Data Ground-truth Labeling Guidelines\n\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 PeopleNet model. If you are looking to re-train with your own dataset, please follow the guideline below for highest accuracy.\n\nPeopleNet 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\n### Performance\n\n#### Evaluation Data Properties \n\n- **Data Collection Method by dataset**\n - Automatic/Sensors\n- **Labeling Method by dataset:**\n - Human\n\n90000 images from an Internal, proprietary dataset in excess of 7.6 million images of more than 71 million people. The training dataset consists of a mix of cameras, camera heights, crowd-density, and field-of view (FOV) taken from a camera. Approximately half of the training data consisted of images captured in an indoor office environment. The camera is typically set up at approximately 10 feet height, 45-degree angle and has close field-of-view. This was done to improve accuracy of the models for convenience-store retail analytics use-case. We have also added approximately 500 thousand images with low-density scenes with people extending their hands and feet to improve the performance for use-cases where person object detection is followed by pose-estimation. \n\n##### Methodology and KPI\n\nThe true positives, false positives, false negatives are calculated using intersection-over-union (IOU) criterion greater than 0.5. In addition, we have also added KPI with IOU criterion of greater than 0.8 for low-density and extended-hand sequences where tight bounding box is a requirement for subsequent human pose estimation algorithms. The KPI for the evaluation data are reported in the table below. Model is evaluated based on precision, recall and accuracy.\n\n| Network | PeopleNet | V2.6 | FP16 | PeopleNet | V2.6 | INT8 |\n| ------- | ------- | -------------- | ---- | ---- | ------------------------ | ---- |\n| Content | Precision | Recall | Accuracy | Precision | Recall | Accuracy |\n| Generic | 91.82 | 88.60 | 82.17 | 92.97 | 85.33 | 80.21 |\n| Office | 95.64 | 91.92 | 88.22 | 96.08 | 89.48 | 86.33 |\n| Café | 93.38 | 86.97 | 81.92 | 94.62 | 81.81 | 78.18 |\n| People (IOU > 0.8) | 80.41 | 78.13 | 65.71 | 78.71 | 73.90 | 61.68 |\n| Extended-hands (IOU > 0.8) | 93.98 | 86.82 | 82.25 | 94.41 | 86.80 | 82.55 |\n\n#### Real-time Inference Performance \n\nThe inference is run on the provided unpruned model at INT8 precision. The inference performance is run using [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/master/samples/trtexec) on Jetson AGX Xavier, Xavier NX, Orin, Orin NX and NVIDIA T4, and Ampere GPU. The Jetson devices are running at Max-N configuration for maximum GPU frequency. The performance shown here is the inference only performance. The end-to-end performance with streaming video data might slightly vary depending on other bottlenecks in the hardware and software.\n\n| Model Arch |Version | Inference Resolution | Precision | Xavier NX | | AGX Xavier | |\n|------------|--------|----------------------|-----------|-----------|-|------------|-|\n|PeopleNet|Unpruned| 960x544x3 | INT8 | GPU | DLA1+DLA2 | GPU | DLA1+DLA2 |\n|Resnet34 | v2.6 | | |79 |46 | 136 | 58|\n\n| Model Arch |Version | Inference Resolution | Precision | Orin NX | | AGX Orin | |\n|------------|--------|----------------------|-----------|---------|-|----------|-|\n|PeopleNet|Unpruned| 960x544x3 | INT8 | GPU | DLA1+DLA2 | GPU | DLA1+DLA2 |\n|Resnet34 | v2.6 | | |140 | 70 | 421 | 104|\n\n| Model Arch |Version | Inference Resolution | Precision |T4|A100|A30|A10|A2|\n|------------|--------|----------------------|-----------|--|----|---|---|--|\n|PeopleNet|Unpruned| 960x544x3 | INT8 |GPU|GPU|GPU|GPU|GPU|\n|Resnet34 | v2.6 | | |465|3291|1603|1007|231|\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 [Train Adapt Optimize (TAO) Toolkit](https://developer.nvidia.com/tao-toolkit), [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) or [TensorRT](https://developer.nvidia.com/tensorrt).\n\nThe primary use case intended for these models is detecting people in a color (RGB) image. The model can be used to detect people from photos and videos by using appropriate video or image decoding and pre-processing. As a secondary use case the model can also be used to detect bags and faces from images or videos. However, these additional classes are not the main intended use for these models.\n\nThere are two flavors of these models:\n\n- trainable (unpruned)\n- deployable (unpruned quantized)\n\nThe `trainable or unpruned` models intended for training using TAO Toolkit and the user's own dataset. This can provide high fidelity models that are adapted to the use case. The Jupyter notebook available as a part of [TAO container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit-tf) can be used to re-train.\n\nThe `deployable_quantized` models share the same structure as the `trainable or unpruned` model, however, these models have been trained by employing Quantization Aware Training and is intended for int-8 deployment.\n\nThe `trainable (unpruned)`, `deployable (unpruned quantized)` models are encrypted and will only operate with the following key:\n\n- Model load key: `tlt_encode`\n\nPlease make sure to use this as the key for all TAO commands that require a model load key.\n\n#### Instructions to use unpruned model with TAO\n\nIn order, to use these models as a pretrained weights for transfer learning, please use the snippet below as template for the `model_config` component of the experiment spec file to train a DetectNet_v2 model. For more information on the experiment spec file, please refer to the [TAO Toolkit User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\n1. For ResNet34\n\n```py\nmodel_config {\n num_layers: 34\n pretrained_model_file: \"/path/to/the/model.tlt\"\n use_batch_norm: true\n objective_set {\n bbox {\n scale: 35.0\n offset: 0.5\n }\n cov {\n }\n }\n training_precision {\n backend_floatx: FLOAT32\n }\n arch: \"resnet\"\n all_projections: true\n}\n```\n\n#### Instructions to deploy these models with DeepStream\n\nTo create the entire 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 pruned model included here can be integrated directly into deepstream by following the instructions mentioned\nbelow.\n\n1. Run the default `deepstream-app` included in the DeepStream docker, by simply executing the commands below.\n\n ```sh\n ## Download Model:\n\n mkdir -p $HOME/peoplenet && \\\n wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/peoplenet/versions/pruned_v2.1/files/resnet34_peoplenet_pruned.etlt \\\n -O $HOME/peoplenet/resnet34_peoplenet_pruned.etlt\n\n ## Run Application\n\n xhost +\n sudo docker run --gpus all -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v $HOME:/opt/nvidia/deepstream/deepstream-5.1/samples/models/tlt_pretrained_models \\\n -w /opt/nvidia/deepstream/deepstream-5.1/samples/configs/tlt_pretrained_models nvcr.io/nvidia/deepstream:5.1-21.02-samples \\\n deepstream-app -c deepstream_app_source1_peoplenet.txt\n ```\n2. Install deepstream on your local host and run the deepstream-app.\n\n [Download](https://developer.nvidia.com/deepstream-sdk) and install DeepStream SDK. The installation instructions for DeepStream are provided in [DeepStream development guide](https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html). The config files for the purpose-built models are located in:\n\n ```sh\n /opt/nvidia/deepstream/deepstream-5.1/samples/configs/tlt_pretrained_models\n ```\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\n You will need 2 config files and 1 label file. These files are provided in the `tlt_pretrained_models` directory.\n\n ```sh\n deepstream_app_source1_peoplenet.txt - Main config file for DeepStream app\n config_infer_primary_peoplenet.txt - File to configure inference settings\n labels_peoplenet.txt - Label file with 3 classes\n ```\n\n Key Parameters in `config_infer_primary_peoplenet.txt`\n\n ```sh\n tlt-model-key=\n tlt-encoded-model=\n labelfile-path=\n int8-calib-file=\n input-dims=\n num-detected-classes=\n ```\n\n Run `deepstream-app`:\n\n ```sh\n deepstream-app -c deepstream_app_source1_peoplenet.txt\n ```\n\n Documentation 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\n#### Very Small Objects\n\nNVIDIA PeopleNet model were trained to detect objects larger than 10x10 pixels. Therefore it may not be able to detect objects that are smaller than 10x10 pixels.\n\n#### Occluded Objects\n\nWhen objects are occluded or truncated such that less than 20% of the object is visible, they may not be detected by the PeopleNet model. For people class objects, the model will detect occluded people as long as head and shoulders are visible. However if the person’s head and/or shoulders are not visible, the object might not be detected unless more than 60% of the person is visible.\n\n#### Dark-lighting, Monochrome or Infrared Camera Images\n\nThe PeopleNet model were trained on RGB images in good lighting conditions. Therefore, images captured in dark lighting conditions or a monochrome image or IR camera image may not provide good detection results.\n\n#### Warped and Blurry Images\n\nThe PeopleNet models were not trained on fish-eye lense cameras or moving cameras. Therefore, the models may not perform well for warped images and images that have motion-induced or other blur.\n\n#### Face and Bag class\n\nAlthough bag and face class are included in the model, the accuracy of these classes will be much lower than people class. Some re-training will be required on these classes to improve accuracy.\n\n#### Technical blogs \n\n- [Train like a ‘pro’ without being an AI expert using TAO AutoML](https://developer.nvidia.com/blog/training-like-an-ai-pro-using-tao-automl/)\n- [Create Custom AI models using NVIDIA TAO Toolkit with Azure Machine Learning](https://developer.nvidia.com/blog/creating-custom-ai-models-using-nvidia-tao-toolkit-with-azure-machine-learning/)\n- [Developing and Deploying AI-powered Robots with NVIDIA Isaac Sim and NVIDIA TAO](https://developer.nvidia.com/blog/developing-and-deploying-ai-powered-robots-with-nvidia-isaac-sim-and-nvidia-tao/)\n- Learn endless ways to adapt and supercharge your AI workflows with TAO - [Whitepaper](https://developer.nvidia.com/tao-toolkit-usecases-whitepaper/1-introduction)\n- [Customize Action Recognition with TAO and deploy with DeepStream](https://developer.nvidia.com/blog/developing-and-deploying-your-custom-action-recognition-application-without-any-ai-expertise-using-tao-and-deepstream/)\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\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- [TAO documentation](https://docs.nvidia.com/tao/tao-toolkit/index.html)\n- Read the [TAO getting Started](https://docs.nvidia.com/tao/tao-toolkit/text/tao_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 models for video analytics application using DeepStream. Learn more about [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk)\n- Deploy your models in [Riva](https://developer.nvidia.com/riva) for ConvAI use case.\n\n### License \n\nLicense to use these models is covered by the Model EULA. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\n### Ethical Considerations \n\nNVIDIA PeopleNet 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","PeopleNet",["D",1749754271283],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/peoplenet","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",8805800,"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.|","explainability","| Field | Response |\n| -- | -- |\n| Intended Application(s) & Domain(s): | Detecting people in smart spaces, retail, industrial applications |\n| Model Type: | Object Detection specified for people detection |\n| Intended Users: | Persons working in smart spaces, retail, and industrial applications. |\n| Output: | Bounding box |\n| Describe how the model works: | People detected when shown in camera field of view |\n| Technical Limitations: | Model should not be used in low-light, low contrast lighting condition and with very small and very large objects. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Precision, Recall and Accuracy. |\n| Potential Known Risks: | Not recommended for life-critical use cases |\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 (PII)? | None |\n| Protected classes used to create this model? | None |\n| Was consent obtained for any PII used? | Yes |\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? | 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?\t| Yes |\n| If PII collected for the development of this AI model, was it minimized to only what was required? | Yes |\n| Is data in dataset traceable?\t| No |\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/about-nvidia/privacy-policy/](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) |","safetyAndSecurity","| Field | Response |\n| -- | -- |\n| Model Application(s): | Object Detection |\n| Describe the life-critical application (if present). | Detecting presence of people in floor space. |\n| Use Case Restrictions: | Should not be used for safety critical use cases |\n| Explicit model and dataset restrictions: | Dataset access restrictions. |\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",[334,335],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_297":343,"_337":343,"_338":344,"_340":341},{"_297":336,"_337":336,"_338":339,"_340":341},"NSPECT-ITZN-CAOK","value","filter",{"_342":341,"_337":336},"category","label","field","DetectNet_v2 Architecture",{"_342":341,"_337":343},{"_132":1317,"_134":1318},"ngcApps",[1270],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1251":1252,"_1253":1254,"_1255":1256,"_1257":1258,"_1259":356,"_164":382,"_1260":1261,"_1262":56,"_1263":111,"_1264":111,"_1265":1266,"_1267":111,"_1268":1269},"amplitude",{"_1248":1249,"_893":-7,"_1250":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1242":1243,"_1244":1245,"_1246":1247},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1788182375765,"1.12.0","defaultSearchSuggestions",{"_1133":194,"_1134":1135,"_1136":1137,"_1138":1139},"announcements",[],"consentState",{"_420":1123,"_1124":111,"_1125":31,"_1126":31,"_1127":1128},"consentConfig",{"_402":403,"_404":405,"_406":407,"_408":409,"_410":411,"_412":413,"_414":415,"_416":417,"_418":419,"_420":421},"showContactAdmin",1788182375766,[],[],[396,397,398,399,400,401],{"_45":60,"_47":71,"_49":127,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":127,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":67,"_49":127,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":127,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":127,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":127,"_51":-5,"_53":-5,"_55":56},"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. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

Under certain privacy laws, you have the right to direct us not to \"sell\" or \"share\" your personal information for targeted advertising. To opt-out of the \"sale\" and \"sharing\" of personal information through cookies, you must opt-out of optional cookies using the toggles below. To opt out of the \"sale\" and \"sharing\" of data collected by other means (e.g., online forms) you must also update your data sharing preferences through the NVIDIA Preference Center.

Click on the different category headings below to find out more and change the settings according to your preference. You cannot opt out of Required Cookies as they are deployed to ensure the proper functioning of our website (such as prompting the cookie banner and remembering your settings, etc.). By clicking \"Save and Accept\" or \"Decline All\" at the bottom, you consent to the use of cookies and other tools as described in our Cookie Policy in accordance with your settings and accept our Terms of Service (which contains important waivers). For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1115":1116,"_1117":1118,"_1119":1120,"_1121":1122},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[422,423,424,425,426],{"_427":805,"_83":806,"_193":807,"_431":111,"_432":18,"_434":808,"_436":809},{"_427":708,"_83":709,"_193":710,"_431":31,"_432":59,"_434":711,"_436":712},{"_427":616,"_83":617,"_193":618,"_431":31,"_432":54,"_434":619,"_436":620},{"_427":438,"_83":439,"_193":440,"_431":31,"_432":16,"_434":441,"_436":442},{"_427":428,"_83":429,"_193":430,"_431":31,"_432":433,"_434":435,"_436":437},"optanonGroupId","C0005","Social Media Cookies","These cookies are set by a range of social media services that we have added to the site to enable you to share our content with your friends and networks. 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.",[558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579],[443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459],{"_460":553,"_297":553,"_193":194,"_434":554},{"_460":549,"_297":549,"_193":194,"_434":550},{"_460":544,"_297":544,"_193":194,"_434":545},{"_460":540,"_297":540,"_193":194,"_434":541},{"_460":535,"_297":535,"_193":194,"_434":536},{"_460":530,"_297":530,"_193":194,"_434":531},{"_460":522,"_297":522,"_193":194,"_434":523},{"_460":514,"_297":514,"_193":194,"_434":515},{"_460":509,"_297":509,"_193":194,"_434":510},{"_460":504,"_297":504,"_193":194,"_434":505},{"_460":499,"_297":499,"_193":194,"_434":500},{"_460":494,"_297":494,"_193":194,"_434":495},{"_460":489,"_297":489,"_193":194,"_434":490},{"_460":482,"_297":482,"_193":194,"_434":483},{"_460":477,"_297":477,"_193":194,"_434":478},{"_460":470,"_297":470,"_193":194,"_434":471},{"_460":461,"_297":461,"_193":194,"_434":462},"hostName","amazon-adsystem.com",[463,464],{"_83":469,"_466":461,"_467":31,"_193":468},{"_83":465,"_466":461,"_467":31,"_193":468},"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",[472,473],{"_83":476,"_466":470,"_467":31,"_193":475},{"_83":474,"_466":470,"_467":31,"_193":475},"IDE","This domain is owned by Doubleclick (Google). The main business activity is: Doubleclick is Googles real time bidding advertising exchange","test_cookie","bing.com",[479],{"_83":480,"_466":477,"_467":31,"_193":481},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[484,485],{"_83":488,"_466":482,"_467":31,"_193":487},{"_83":486,"_466":482,"_467":31,"_193":487},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_*","If you know anything about this host and its uses please add your contributions here. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","tiktok.com",[491],{"_83":492,"_466":489,"_467":31,"_193":493},"_ttp","TikTok is a social media platform owned by ByteDance, known for short-form video sharing. The service is used to create and share videos, and it utilizes cookies for various purposes including advertising and analytics.","t.co",[496],{"_83":497,"_466":494,"_467":31,"_193":498},"muc_ads","This domain is owned by Twitter. It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[501],{"_83":502,"_466":499,"_467":31,"_193":503},"sp","Testing","google.com",[506],{"_83":507,"_466":504,"_467":31,"_193":508},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","linkedin.com",[511],{"_83":512,"_466":509,"_467":31,"_193":513},"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",[516,517],{"_83":520,"_466":514,"_467":31,"_193":521},{"_83":518,"_466":514,"_467":31,"_193":519},"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_*","This domain is owned by X (formerly Twitter). The main business activity is: Social Networking Services. Where X (formerly 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.","youtube.com",[524,525],{"_83":528,"_466":522,"_467":31,"_193":529},{"_83":526,"_466":522,"_467":31,"_193":527},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-*","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.","demdex.net",[532],{"_83":533,"_466":530,"_467":31,"_193":534},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[537],{"_83":538,"_466":535,"_467":31,"_193":539},"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",[542],{"_83":543,"_466":540,"_467":31,"_193":503},"R","6sc.co",[546],{"_83":547,"_466":544,"_467":31,"_193":548},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. It is primarily used for tracking and targeting in marketing automation and advertising.","hushly.com",[551],{"_83":552,"_466":549,"_467":31,"_193":503},"_hly_gvid","t.contentsquare.net",[555],{"_83":556,"_466":553,"_467":111,"_193":557},"_cs_*","This is a session cookie. This is a pattern type cookie where a random 13-digit number is appended to the prefix _cs_.",{"_83":614,"_466":581,"_467":31,"_193":615},{"_83":612,"_466":581,"_467":31,"_193":613},{"_83":610,"_466":581,"_467":31,"_193":611},{"_83":492,"_466":581,"_467":31,"_193":609},{"_83":586,"_466":581,"_467":111,"_193":588},{"_83":608,"_466":581,"_467":31,"_193":194},{"_83":607,"_466":584,"_467":31,"_193":194},{"_83":605,"_466":581,"_467":31,"_193":606},{"_83":603,"_466":581,"_467":31,"_193":604},{"_83":602,"_466":581,"_467":31,"_193":194},{"_83":600,"_466":581,"_467":31,"_193":601},{"_83":586,"_466":599,"_467":111,"_193":588},{"_83":597,"_466":584,"_467":31,"_193":598},{"_83":597,"_466":587,"_467":31,"_193":598},{"_83":556,"_466":581,"_467":31,"_193":557},{"_83":595,"_466":581,"_467":31,"_193":596},{"_83":502,"_466":593,"_467":31,"_193":594},{"_83":591,"_466":581,"_467":31,"_193":592},{"_83":589,"_466":581,"_467":31,"_193":590},{"_83":586,"_466":587,"_467":111,"_193":588},{"_83":583,"_466":584,"_467":31,"_193":585},{"_83":580,"_466":581,"_467":111,"_193":582},"AMCVS_xxxxxAdobeOrg","nvidia.com","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.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","AMP_*","build.nvidia.com","Supports Amplitude product analytics.","_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.","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. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674],[621,622,623,624,625],{"_460":535,"_297":535,"_193":194,"_434":645},{"_460":509,"_297":509,"_193":194,"_434":640},{"_460":635,"_297":635,"_193":194,"_434":636},{"_460":630,"_297":630,"_193":194,"_434":631},{"_460":626,"_297":626,"_193":194,"_434":627},"nvidia.partners",[628],{"_83":629,"_466":626,"_467":31,"_193":503},"edge","app.hushly.com",[632],{"_83":633,"_466":630,"_467":31,"_193":634},"*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",[637],{"_83":638,"_466":635,"_467":31,"_193":639},"_octo","This domain is owned by Github. The main business activity is: Source code repository and collaboration platform",[641,642],{"_83":644,"_466":509,"_467":31,"_193":513},{"_83":643,"_466":509,"_467":31,"_193":513},"lidc","li_gc",[646,647],{"_83":649,"_466":535,"_467":31,"_193":539},{"_83":648,"_466":535,"_467":31,"_193":539},"imid_*","imid",{"_83":675,"_466":584,"_467":31,"_193":677},{"_83":706,"_466":581,"_467":31,"_193":707},{"_83":675,"_466":705,"_467":31,"_193":677},{"_83":703,"_466":581,"_467":111,"_193":704},{"_83":701,"_466":581,"_467":31,"_193":702},{"_83":700,"_466":683,"_467":111,"_193":194},{"_83":675,"_466":679,"_467":31,"_193":677},{"_83":698,"_466":581,"_467":31,"_193":699},{"_83":675,"_466":587,"_467":31,"_193":677},{"_83":686,"_466":584,"_467":31,"_193":194},{"_83":675,"_466":697,"_467":31,"_193":677},{"_83":695,"_466":581,"_467":31,"_193":696},{"_83":692,"_466":693,"_467":31,"_193":694},{"_83":690,"_466":691,"_467":31,"_193":194},{"_83":675,"_466":687,"_467":31,"_193":677},{"_83":689,"_466":581,"_467":31,"_193":194},{"_83":675,"_466":688,"_467":31,"_193":677},{"_83":686,"_466":687,"_467":31,"_193":194},{"_83":685,"_466":683,"_467":111,"_193":194},{"_83":675,"_466":684,"_467":31,"_193":677},{"_83":675,"_466":683,"_467":31,"_193":677},{"_83":675,"_466":682,"_467":31,"_193":677},{"_83":681,"_466":581,"_467":111,"_193":677},{"_83":678,"_466":679,"_467":111,"_193":680},{"_83":675,"_466":676,"_467":31,"_193":677},"_gd_*","info.nvidia.com","This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv_country_code","developer.nvidia.com","This cookie is likely used to store the country code of the visitor to provide localized content and possibly for legal compliance with regional data privacy laws.","_gdxxxxxxxxxxxxx","nvidianews.nvidia.com","docs.api.nvidia.com","research.nvidia.com","readme_library","wcmmode","ai-podcast.nvidia.com","resources.nvidia.com","geo_locator","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","crowdsource.nvidia.com","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","readme_language","vid","This cookie name 'vid' is used for tracking the search request made by the user on the 'Find a practitioner' page.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","blogs.nvidia.com","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.","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 performance.",[733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761],[713,714,715,716],{"_460":728,"_297":728,"_193":194,"_434":729},{"_460":723,"_297":723,"_193":194,"_434":724},{"_460":522,"_297":522,"_193":194,"_434":720},{"_460":635,"_297":635,"_193":194,"_434":717},[718],{"_83":719,"_466":635,"_467":111,"_193":639},"_gh_sess",[721],{"_83":722,"_466":522,"_467":111,"_193":529},"YSC","bizible.com",[725],{"_83":726,"_466":723,"_467":31,"_193":727},"_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",[730],{"_83":731,"_466":728,"_467":31,"_193":732},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_83":803,"_466":581,"_467":31,"_193":804},{"_83":788,"_466":802,"_467":111,"_193":789},{"_83":771,"_466":584,"_467":31,"_193":696},{"_83":771,"_466":684,"_467":31,"_193":696},{"_83":800,"_466":581,"_467":31,"_193":801},{"_83":788,"_466":587,"_467":111,"_193":789},{"_83":798,"_466":581,"_467":31,"_193":799},{"_83":788,"_466":584,"_467":111,"_193":789},{"_83":796,"_466":683,"_467":31,"_193":797},{"_83":788,"_466":705,"_467":111,"_193":789},{"_83":768,"_466":584,"_467":31,"_193":770},{"_83":794,"_466":581,"_467":111,"_193":795},{"_83":792,"_466":581,"_467":31,"_193":793},{"_83":790,"_466":581,"_467":111,"_193":791},{"_83":788,"_466":679,"_467":111,"_193":789},{"_83":786,"_466":581,"_467":31,"_193":787},{"_83":784,"_466":581,"_467":31,"_193":785},{"_83":782,"_466":581,"_467":111,"_193":783},{"_83":780,"_466":688,"_467":31,"_193":781},{"_83":768,"_466":679,"_467":31,"_193":770},{"_83":778,"_466":581,"_467":31,"_193":779},{"_83":776,"_466":581,"_467":111,"_193":777},{"_83":774,"_466":581,"_467":31,"_193":775},{"_83":772,"_466":581,"_467":31,"_193":773},{"_83":771,"_466":581,"_467":31,"_193":696},{"_83":768,"_466":769,"_467":31,"_193":770},{"_83":766,"_466":581,"_467":31,"_193":767},{"_83":764,"_466":581,"_467":111,"_193":765},{"_83":762,"_466":581,"_467":31,"_193":763},"_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. It is used to persist the Hotjar User ID, unique to that site on the browser. This ensures that behavior in subsequent visits to the same site will be attributed to the same user ID.","_hjHasCachedUserAttributes","This cookie name is associated with Hotjar which is used to know whether the data set in _hjUserAttributes Local Storage item is up to date or not.","s_ppv","This cookie is associated with Adobe Analytics site tracking and is used to measure a user's scroll activity to see how much of a page they view before moving to another page.","_gid","This cookie name is associated with Google Universal Analytics. This appears to be a new cookie and as of Spring 2017 no information is available from Google. It appears to store and update a unique value for each page visited.","_hjTLDTest","When the Hotjar script executes we try to determine the most generic cookie path we should use, instead of the page hostname. This is done so that cookies can be shared across subdomains (where applicable). To determine this, we try to store the _hjTLDTest cookie for different URL substring alternatives until it fails. After this check, the cookie is removed.","ekfls-2025-03-27","This cookie name is associated with readme.io which is used to help maintain data for the API reference support page.","s_getNewRepeat","This cookie is used to determine whether a visitor is a new visitor or a repeat visitor to the site, and is used for analytical purposes.","_hly_sid","This cookie is likely associated with website analytics, as the prefix '_hly_' suggests a connection to Holistics, a business intelligence and analytics platform.","docs.nvidia.com","_hjSession_xxxxxx","A cookie that holds the current session data. This ensues that subsequent requests within the session window will be attributed to the same Hotjar session.","C0001","Required Cookies","These cookies enable core functionality such as security, network management, and accessibility. These cookies are required for the site to function and cannot be turned off.",[950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999,1000,1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022],[810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834],{"_460":947,"_297":947,"_193":194,"_434":948},{"_460":941,"_297":941,"_193":194,"_434":942},{"_460":938,"_297":938,"_193":194,"_434":939},{"_460":926,"_297":926,"_193":194,"_434":927},{"_460":923,"_297":923,"_193":194,"_434":924},{"_460":920,"_297":920,"_193":194,"_434":921},{"_460":915,"_297":915,"_193":194,"_434":916},{"_460":522,"_297":522,"_193":194,"_434":912},{"_460":909,"_297":909,"_193":194,"_434":910},{"_460":514,"_297":514,"_193":194,"_434":907},{"_460":509,"_297":509,"_193":194,"_434":905},{"_460":900,"_297":900,"_193":194,"_434":901},{"_460":895,"_297":895,"_193":194,"_434":896},{"_460":890,"_297":890,"_193":194,"_434":891},{"_460":887,"_297":887,"_193":194,"_434":888},{"_460":635,"_297":635,"_193":194,"_434":884},{"_460":494,"_297":494,"_193":194,"_434":881},{"_460":630,"_297":630,"_193":194,"_434":874},{"_460":626,"_297":626,"_193":194,"_434":868},{"_460":862,"_297":862,"_193":194,"_434":863},{"_460":857,"_297":857,"_193":194,"_434":858},{"_460":852,"_297":852,"_193":194,"_434":853},{"_460":848,"_297":848,"_193":194,"_434":849},{"_460":843,"_297":843,"_193":194,"_434":844},{"_460":835,"_297":835,"_193":194,"_434":836},"polyfill.io",[837,838],{"_83":841,"_466":835,"_467":31,"_193":842},{"_83":839,"_466":835,"_467":31,"_193":840},"cf_ob_info","This cookie is associated with sites using CloudFlare. It is used to improve page load times and to override any security restrictions based on the visitors IP address. It does not contain any user identification information.","cf_use_ob","This cookie is associated with sites using CloudFlare. It is used to improve page load times and to override any security restrictions based on the visitors IP address. It does not contain any user identification information. ","www.recaptcha.net",[845],{"_83":846,"_466":843,"_467":31,"_193":847},"_GRECAPTCHA","This domain is used by Google reCAPTCHA, which provides spam protection services to websites by detecting if a user is a human or a bot.","api2.hcaptcha.com",[850],{"_83":851,"_466":848,"_467":31,"_193":503},"__cflb","instagram.com",[854],{"_83":855,"_466":852,"_467":31,"_193":856},"csrftoken","Instagram is a social networking service owned by Facebook, Inc. It's designed for sharing photos and videos from a smartphone. The service also uses cookies for various purposes, including analytics, personalization, and advertising.","readme.io",[859],{"_83":860,"_466":857,"_467":31,"_193":861},"__cf_bm","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.","nvidiacorp.us-5.evergage.com",[864,865],{"_83":867,"_466":862,"_467":31,"_193":503},{"_83":866,"_466":862,"_467":31,"_193":503},"AWSALBTG","AWSALBTGCORS",[869,870],{"_83":872,"_466":626,"_467":31,"_193":873},{"_83":871,"_466":626,"_467":31,"_193":503},"ak_bmsc","bm_mi","This domain is likely associated with NVIDIA's partner program, which could involve marketing, sales, and support services for partners.",[875,876,877],{"_83":880,"_466":630,"_467":31,"_193":503},{"_83":879,"_466":630,"_467":111,"_193":503},{"_83":878,"_466":630,"_467":31,"_193":503},"AWSALBCORS","JSESSIONID","AWSALB",[882],{"_83":860,"_466":494,"_467":31,"_193":883},"This is a CloudFoundry cookie",[885],{"_83":886,"_466":635,"_467":31,"_193":639},"logged_in","hcaptcha.com",[889],{"_83":860,"_466":887,"_467":31,"_193":883},"tracking.intentsify.io",[892],{"_83":893,"_466":890,"_467":31,"_193":894},"userId","Used to track usage of the embedded video player.","static.reo.dev",[897],{"_83":898,"_466":895,"_467":31,"_193":899},"cookietest","Common cookie name could have a number of different origins. Where this is first party and a session cookie, its most likely to do with checking to see if the browser is set to block or allow cookies.","www.datadoghq-browser-agent.com",[902],{"_83":903,"_466":900,"_467":31,"_193":904},"dd_cookie_test_","dd_cookie_test",[906],{"_83":860,"_466":509,"_467":31,"_193":883},[908],{"_83":860,"_466":514,"_467":31,"_193":883},"supabase.co",[911],{"_83":860,"_466":909,"_467":31,"_193":883},[913],{"_83":914,"_466":522,"_467":31,"_193":529},"VISITOR_PRIVACY_METADATA","dosa-prd.azurewebsites.net",[917],{"_83":918,"_466":915,"_467":31,"_193":919},"access_token","This cookie is used to obtain the permissions for users of the portal to allow for API access in order to retrieve information required to perform portal functions.","w.hcaptcha.com",[922],{"_83":860,"_466":920,"_467":31,"_193":883},"cdn.readme.io",[925],{"_83":860,"_466":923,"_467":31,"_193":883},"queue-it.net",[928,929,930,931,932],{"_83":937,"_466":926,"_467":31,"_193":194},{"_83":936,"_466":926,"_467":31,"_193":194},{"_83":935,"_466":926,"_467":31,"_193":194},{"_83":934,"_466":926,"_467":31,"_193":194},{"_83":933,"_466":926,"_467":31,"_193":194},"Queue-it-{queueId}","Queue-it-{dmonvidia }________{eventId}","Queue-it-token","queueitsoftblock_{queueId}","Queue-it-{nvidia}________{eventId}","x.com",[940],{"_83":860,"_466":938,"_467":31,"_193":861},"api.hcaptcha.com",[943,944],{"_83":946,"_466":941,"_467":31,"_193":503},{"_83":851,"_466":941,"_467":31,"_193":945},"This domain is associated with hCaptcha, a service that provides CAPTCHA and reCAPTCHA alternatives for user verification and bot protection.","hmt_id","vimeo.com",[949],{"_83":860,"_466":947,"_467":31,"_193":883},{"_83":1083,"_466":581,"_467":31,"_193":1114},{"_83":1112,"_466":676,"_467":111,"_193":1113},{"_83":1105,"_466":1074,"_467":111,"_193":1106},{"_83":1111,"_466":769,"_467":111,"_193":194},{"_83":1109,"_466":581,"_467":31,"_193":1110},{"_83":867,"_466":1095,"_467":31,"_193":1034},{"_83":1042,"_466":1104,"_467":111,"_193":1044},{"_83":866,"_466":1033,"_467":31,"_193":1034},{"_83":1045,"_466":1104,"_467":31,"_193":1046},{"_83":1107,"_466":581,"_467":111,"_193":1108},{"_83":1105,"_466":1081,"_467":111,"_193":1106},{"_83":1039,"_466":1104,"_467":31,"_193":1041},{"_83":1102,"_466":581,"_467":31,"_193":1103},{"_83":866,"_466":1025,"_467":31,"_193":1034},{"_83":1030,"_466":1101,"_467":31,"_193":1032},{"_83":878,"_466":1031,"_467":31,"_193":1047},{"_83":880,"_466":1031,"_467":31,"_193":1098},{"_83":1045,"_466":1040,"_467":31,"_193":1046},{"_83":1099,"_466":679,"_467":31,"_193":1100},{"_83":880,"_466":705,"_467":31,"_193":1098},{"_83":1096,"_466":581,"_467":31,"_193":1097},{"_83":872,"_466":705,"_467":31,"_193":1091},{"_83":1068,"_466":684,"_467":111,"_193":194},{"_83":866,"_466":1095,"_467":31,"_193":1034},{"_83":1093,"_466":1033,"_467":111,"_193":1094},{"_83":1057,"_466":581,"_467":31,"_193":1048},{"_83":1030,"_466":581,"_467":31,"_193":1032},{"_83":1092,"_466":593,"_467":31,"_193":194},{"_83":872,"_466":581,"_467":31,"_193":1091},{"_83":880,"_466":1025,"_467":31,"_193":1090},{"_83":1088,"_466":688,"_467":111,"_193":1089},{"_83":1086,"_466":581,"_467":111,"_193":1087},{"_83":1085,"_466":581,"_467":31,"_193":194},{"_83":1083,"_466":1031,"_467":31,"_193":1084},{"_83":1082,"_466":593,"_467":31,"_193":194},{"_83":1073,"_466":1081,"_467":111,"_193":1075},{"_83":1079,"_466":684,"_467":31,"_193":1080},{"_83":1077,"_466":769,"_467":31,"_193":1078},{"_83":1076,"_466":587,"_467":31,"_193":194},{"_83":1073,"_466":1074,"_467":111,"_193":1075},{"_83":1071,"_466":1040,"_467":111,"_193":1072},{"_83":1069,"_466":581,"_467":31,"_193":1070},{"_83":1068,"_466":584,"_467":111,"_193":194},{"_83":1039,"_466":1043,"_467":31,"_193":1041},{"_83":871,"_466":1058,"_467":31,"_193":1048},{"_83":1066,"_466":584,"_467":31,"_193":1067},{"_83":1064,"_466":581,"_467":31,"_193":1065},{"_83":1062,"_466":581,"_467":31,"_193":1063},{"_83":1061,"_466":581,"_467":31,"_193":194},{"_83":903,"_466":584,"_467":31,"_193":1060},{"_83":1057,"_466":705,"_467":31,"_193":1048},{"_83":860,"_466":676,"_467":31,"_193":1059},{"_83":1057,"_466":1058,"_467":31,"_193":1048},{"_83":1056,"_466":593,"_467":31,"_193":194},{"_83":1055,"_466":581,"_467":31,"_193":1028},{"_83":1053,"_466":1040,"_467":31,"_193":1054},{"_83":866,"_466":1051,"_467":31,"_193":1052},{"_83":1050,"_466":593,"_467":31,"_193":194},{"_83":1049,"_466":581,"_467":31,"_193":194},{"_83":1042,"_466":1040,"_467":111,"_193":1044},{"_83":871,"_466":705,"_467":31,"_193":1048},{"_83":878,"_466":705,"_467":31,"_193":1047},{"_83":1045,"_466":1043,"_467":31,"_193":1046},{"_83":1042,"_466":1043,"_467":111,"_193":1044},{"_83":1039,"_466":1040,"_467":31,"_193":1041},{"_83":1037,"_466":1033,"_467":111,"_193":1038},{"_83":1035,"_466":691,"_467":31,"_193":1036},{"_83":867,"_466":1033,"_467":31,"_193":1034},{"_83":1030,"_466":1031,"_467":31,"_193":1032},{"_83":860,"_466":683,"_467":31,"_193":1029},{"_83":1027,"_466":581,"_467":31,"_193":1028},{"_83":878,"_466":1025,"_467":31,"_193":1026},{"_83":1023,"_466":581,"_467":31,"_193":1024},"LoginHints","Keeps users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","LSKey-c$CookieConsentPolicy","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","CookieConsentPolicy","Used to apply end-user cookie consent preferences set by our client-side utility.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","nv_co","__uxq412__ses.88c1","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","OptanonAlertBoxClosed","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. It is a temporary cookie used to determine if the browser accepts cookies.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","bm_ss","This cookie is used to help protect websites from automated bot traffic.","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","docs.ngc.nvidia.com","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. Usually used to maintain an anonymised user session by the server.","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","c_code","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a one year lifespan and contains no personal information.","default","

NVIDIA uses cookies to improve your experience on our web site. We and our third-party partners also use cookies and other tools to collect and record information you provide as well as information about your interactions with our websites for performance improvement, analytics, and to assist in marketing efforts. By clicking \"Accept All\", you consent to our use of cookies and other tools as described in our Cookie Policy. You can manage your cookie settings by clicking on \"Manage Settings.\" By continuing to use this site or by clicking one of the buttons below, you agree to our Terms of Service (which contains important waivers). Please see our Privacy Policy for more information on our privacy practices.

","gpc","

We have detected the Global Privacy Control (GPC) signal and recorded your rejection of all optional cookies on this site for this browser. You can manage your cookie settings by clicking on \"Manage Settings\". Please see our Cookie Policy for more information. To opt out of non-cookie personal information \"sales\" / \"sharing\" for targeted advertising purposes, please visit the NVIDIA Preference Center. Please see our Privacy Policy for more information on our privacy practices.

","gpcLoggedIn","

We have detected the Global Privacy Control Signal (GPC) and have opted you out of all optional cookies on this browser. You can manage your cookie settings by clicking on \"Manage Settings\". Please see our Cookie Policy for more information. We have also opted you out of \"sharing\"/\"sales\" of personal information outside of cookies. You can manage these settings in the NVIDIA NVIDIA Preference Center. Please see our Privacy Policy for more information.

","gpcLoggedInOverridden","

We have detected the Global Privacy Control Signal (GPC) and have opted you out of all optional cookies on this browser. You can manage your cookie settings by clicking on \"Manage Settings\". Please see our Cookie Policy for more information. We have also opted you out of \"sharing\"/\"sales\" of personal information outside of cookies which overrides at least one of your previous settings. You can manage them in the NVIDIA Preference Center. Please see our Privacy Policy for more information.

",{"_805":18,"_708":56,"_616":56,"_438":56,"_428":56},"hasConsented","gpcApplied","showBanner","geolocation",{"_1129":1130,"_1131":1132},"country","US","state","OR","query","categories",[1151,1152,1153,1154,1155],"optionUrls",[1140,1141,1142,1143,1144,1145,1146,163,1147,1148,1149,1150],"total",12,"/orgs/nim/deepseek-ai/containers/deepseek-r1/-","/orgs/nim/nvidia/containers/llama-3.1-nemotron-70b-instruct/-","/orgs/nvidia/-/containers/pytorch/-","/orgs/nvidia/omniverse/collections/kit/-","/orgs/nvidia/-/collections/deepstream_sdk/-","/orgs/nvidia/omniverse/collections/kit-appstreaming-collection/-","/orgs/nvidia/research/models/stylegan3/-","/orgs/nvidia/tao/models/trafficcamnet/-","/orgs/nvidia/riva/resources/riva_quickstart/-","/orgs/nvidia/-/helm-charts/gpu-operator/-","/orgs/nvidia/-/helm-charts/k8s-nim-operator/-",{"_290":1226,"_1157":1227,"_1159":56},{"_290":1191,"_1157":1192,"_1159":59},{"_290":291,"_1157":1178,"_1159":1179},{"_290":1170,"_1157":1171,"_1159":1172},{"_290":1156,"_1157":1158,"_1159":1160},"helm-charts","items",[1161,1162],"startIndex",10,{"_290":1156,"_297":1167,"_187":188,"_189":-7,"_292":1168,"_300":301,"_331":1169,"_1166":1149},{"_290":1156,"_297":1163,"_187":188,"_189":-7,"_292":1164,"_300":301,"_331":1165,"_1166":1150},"NVIDIA NIM Operator","k8s-nim-operator",[],"url","GPU Operator","gpu-operator",[],"resources",[1173],9,{"_290":1170,"_297":1174,"_187":188,"_189":1175,"_292":1176,"_300":301,"_331":1177,"_1166":1148},"Riva Skills Quick Start","riva","riva_quickstart",[],[1180,1181,1182],6,{"_290":291,"_297":1187,"_187":188,"_189":1188,"_292":1189,"_300":301,"_331":1190,"_1166":1146},{"_290":291,"_297":298,"_187":188,"_189":190,"_292":293,"_300":301,"_331":1186,"_1166":163},{"_290":291,"_297":1183,"_187":188,"_189":190,"_292":1184,"_300":301,"_331":1185,"_1166":1147},"TrafficCamNet","trafficcamnet",[],[],"StyleGAN3 pretrained models","research","stylegan3",[],"collections",[1193,1194,1195],{"_290":1191,"_297":1223,"_187":188,"_189":1197,"_292":1224,"_300":301,"_331":1225,"_1166":1143},{"_290":1191,"_297":1220,"_187":188,"_189":-7,"_292":1221,"_300":301,"_331":1222,"_1166":1144},{"_290":1191,"_297":1196,"_187":188,"_189":1197,"_292":1198,"_300":301,"_331":1199,"_1166":1145},"Omniverse Kit App Streaming","omniverse","kit-appstreaming-collection",[1200,1201,1202],{"_83":1214,"_297":1215,"_294":1216,"_305":1217,"_1207":1218},{"_83":1197,"_297":1210,"_294":1211,"_305":1206,"_1207":1212},{"_83":1203,"_297":1204,"_294":1205,"_305":1206,"_1207":1208},"omniverse-dev","NVIDIA Developer Program","Join the Developer Program for access to free tools, support, and tech resources.","https://store-images.s-microsoft.com/image/apps.33936.6f1bcf42-55d0-45d1-b4b6-f08c68a50bbe.e91ca707-db64-49d6-9761-4781590a9685.7ba64fc5-7c44-4864-a5e6-7bde05126935","supportedFeatures",[1209],"UNIFIED_CATALOG","NVIDIA Omniverse","NVIDIA Omniverse is a platform of APIs, services, and software development kits (SDKs) that enable developers to build generative AI-enabled tools and applications.",[1209,1213],"SHAREABLE_TO_USER_GROUPS","nv-ai-enterprise","NVIDIA AI Enterprise","Accelerate your AI agent development","https://assets.ngc.nvidia.com/products/catalog/images/product-NVAIE.jpg",[1209,1213,1219],"NGC_CONSOLE","DeepStream SDK","deepstream_sdk",[],"Omniverse Kit (FB)","kit",[1200,1201,1202],"containers",[1228,1229,1230],{"_290":1226,"_297":1238,"_187":1235,"_189":1239,"_292":1240,"_300":301,"_331":1241,"_1166":1140},{"_290":1226,"_297":1234,"_187":1235,"_189":188,"_292":1236,"_300":301,"_331":1237,"_1166":1141},{"_290":1226,"_297":1231,"_187":188,"_189":-7,"_292":1232,"_300":301,"_331":1233,"_1166":1142},"PyTorch","pytorch",[],"Llama-3.1-Nemotron-70B-Instruct","nim","llama-3.1-nemotron-70b-instruct",[],"DeepSeek-R1","deepseek-ai","deepseek-r1",[],"ownerOrgs",[],"memberOrgs",[],"externalOrgs",[],"apiKey","2b44f86fea50e90ffd2db29ee2028419","consentGranted","applicationId","4c857e4f-5ffa-45fa-b75e-af773c22f0ad","clientToken","pubb6927db33dae60ba5031a186cde0242a","site","datadoghq.com","service","catalog-ui","env","sessionSampleRate",100,"sessionReplaySampleRate","compressIntakeRequests","storeContextsAcrossPages","defaultPrivacyLevel","mask-user-input","trackViewsManually","trackingConsent","not-granted",{"_1271":1272,"_1273":375,"_1274":31,"_1275":1276,"_1157":1277,"_1278":1279,"_1280":1272,"_1281":31,"_1282":31,"_1283":31,"_1284":1285},"appName","ngc-catalog","appOrigin","hideOnPrivateOrg","icon","ObjectsModules2",[1286,1287,1288,1289,1290,1291,1292],"menuIcon","editor-swatches","productName","requiresAllScopes","requiresLogin","requiresOrgLevelScopesOnly","title","Catalog",{"_1284":1314,"_47":1315,"_1278":1316},{"_1307":1308,"_1284":1309,"_47":1310,"_1280":1214},{"_1284":1305,"_47":1306,"_1278":1279},{"_1284":1302,"_47":1303,"_1278":1304},{"_1284":1299,"_47":1300,"_1278":1301},{"_1284":1296,"_47":1297,"_1278":1298},{"_1284":1293,"_47":1294,"_1278":1295},"Resources","/resources","misc-library","Models","/models","misc-neural-network","Helm Charts","/helm-charts","misc-ship-wheel","Containers","/containers","3d-cube","Collections","/collections","access",[1311],"Enterprise Catalog","/enterprise",[1312,1313],"NVAIE","READ","Explore Catalog","/","common-home",{"_137":138,"_139":140,"_141":142,"_143":144,"_145":146,"_147":148},{"_136":31}]