Retail Object Recognition

Model Overview

This model encodes retail objects to embedding vectors and predicts their labels based on the embedding vectors in the reference space.

In version 2.0, the Retail Object Recognition model both generates embedding vectors in a reference space and provides a classification output to categorize the objects into seen classes, which refer to the classes that have appeared in the training datasets.

Both versions 1.0 and 2.0 are adept at executing few-shot learning, having been trained through the process of metric learning. For a more comprehensive understanding of metric learning, please consult the provided references.

Model Version Summary

\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 Version	Model Architecture	Input resolution	Model Size	Training Number of Classes	Task	Training data - synthetic images	Training data - real images	Decryption Code
Retail Object Recognition v1.0	ResNet101	224x224	46.7M	100	Recognition retail objects based on embedding vectors in reference space	600k, 100 retail subjects	50k, 100 retail subjects, cropped from 6 scenes	nvidia_tlt
Retail Object Recognition v2.0	FAN-Base-Hybrid	224x224	205.8M	315	This task involves a dual-output network structure, which generates an embedding vectors in reference space and a classification output for categorizing the objects into seen classes.	80k synthetic data with improved complexity and variances compaed to training data in v1.0	48k, 315 retail subjects, cropped from 7 scenes (added one more scene from real training data in v1.0)	None

Model Architecture

ResNet

Version 1.0 consists of a traunk and embedded. The trunk is a ResNet101 classification network with its fully connected layers removed. The embedder is a one-layer Perceptron with an input size of 2048 (the output dimension from the averagePool layer of the ResNet101 trunk) and an output size of 2048. Thus the embedding dimension of the Retail Object Recognition model is 2048.

FAN-Base-Hybrid

The Retail Object Recognition model (version 2.0) is composed of three primary components: a trunk, a classification head, and an embedding head.\nThe trunk uses the FAN-Base-Hybrid (Fully Attentional Network) model architecture for feature extraction. The embedding head uses four adaptors with an output size of 448. The adaptor consists of two fully connected layers, GELU activation functions and residual connection. The classifier is derived from the adaptors along with a one-layer perceptron with an output size corresponding to the number of classes.

Training

Version 1.0

The Retail Object Recognition model (version 1.0) was trained with the Triplet Loss network algorithm. The training algorithm optimizes the network to minimize the embedding output distances (cosine similarity) between the positive images and the anchor image while maximizing the distances between the negative images and the anchor image.

The trunk and embedder use different learning rates during training. The embedder uses a smaller learning rate than the trunk for a better fine-tuning effect.

Version 2.0

For the Retail Object Recognition model (version 2.0), the training algorithm optimizes the network to minimize the embedding features and target classes for the objects.\nThe trunk is initialized by the FAN-Base-Hybrid pretrained model and remains frozen during the training process.\nThe embedder is trained using the Triplet Loss algorithm, an approach that enhances the network by reducing the cosine similarity-measured distances between the anchor image and positive images, while concurrently extending the distances between the anchor image and negative images.\nThe classifier was trained through the Cross-entropy algorithm, minimizing the distance between the images and their corresponding classes.\nThe classifier and embedder are jointly trained that can potentially enhances the precision by leveraged on the features from both branches.

Training Data

The training data of the Retail Object Recognition model was cropped from images for Retail Object Detection model training and fine-tuning data (see Retail Object Detection - TRAINING DATA). Thus it is made up of both synthetic data and real data. By mixing the synthetic and real images in the training set, the model is enhanced, allowing it to bridge the gap between simulation and reality. Consequently, the model can learn feature representations from both synthetic and real image sources.

The training data encompasses multiple angles of the retail objects, which equips the model to identify a retail object from any given angle.

Version 1.0

Specifically, the model was trained on a mixture of 0.6 million synthetic images and 50k real images. During the training phase, the triplet loss on the mixture would be optimized. And during the validation phase, the accuracy of the similarity search would be calculated. The reference data for the validation set are synthetic while the query data for the validation set are real images.

\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\">

dataset	Total # of images	Training images	Testing images
Synthetic data	600,000	600,000	-
Real data	56,898	53,476	3,422

Version 2.0

The Retail Object Recognition model (version 2.0) trained with a new retail dataset, which total has 315 distinct categories.

Specifically, the model's training involved a combined dataset of more than 80,000 synthetic images and 48,000 real images. In the training phrase, both cross-entropy loss and triple loss are optimized with this composite dataset. In the testing phase, the probabilities for each category are computed. In parallel, a similarity search is conducted, which used synthetic data as the reference and real data as the query.

\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\">

Dataset	Total # of images	Training images	Testing images
Synthetic Data	80,872	80,872	-
Real Data	129,012	48,140	58,699

Inference Data Ground-truth Labeling Guidelines

The real training images were cropped from Retail Object Detection datasets with ground-truth bounding-boxes and categories by human labellers. To run inference on your own datasets, you may follow the guidelines below.

Reference Data Guidelines

Reference data is the database for similarity search during the inference stage for the Retail Object Recognition model. The prediction of the inference images would be decided by the L2 distances of the extracted features. Specifically, the algorithm would select the reference object with the smallest L2 distance to the query object in the reference database by Kmeans, and the predicted class would be the corresponding class of the selected reference object.

Therefore, to achieve the highest accuracy for retail object recognition, the reference data needs to be as close to the inference data as possible, regarding the background, occlusion, object orientations, etc.

For instance, if you decide that you only want to infer the retail objects with the front face, then you can collect the front side of the retail objects only as reference data. On the other hand, if you want the Retail Object Recognition model to recognize the objects with whatever angles presented, then more orientations of the retail objects need to be collected in the reference dataset.

Generally 20-30 images/class for reference data is of the highest efficiency. However, it would be definitely better to collect more reference examples, say 100 images/class.

Below are the guidelines for the specific conditions of the images:

All objects should take at least 70% of the frame.
Noisy background: images with noises in the backgrounds are fine.
Occlusion: objects occluded by distractions such as hands are fine. But at least 60% of the object should be visible.
Truncation: objects truncated by the edge of the frame with visibility of >= 60% are fine.
Each image should be assigned to a specific class. This model does not accommodate the case when multiple classes are classified to some “other”/”unknown” class.

Query Data Guidelines

Same as the reference data guidelines.

Notice that the Retail Object Recognition model can never correctly classify the retail object if the class is not in the reference dataset.

To get the most accurate predictions, you should avoid challenging the Retail Object Recognition model with some bad views, such as the top of a soda can (as this view can be the same across many different retail objects).

Performance

Evaluation Data

Here, we present the evaluation results of Retail Object Recognition models, including versions 1.0, 2.0, and an unreleased version 1.1. The purpose of version 1.1 is to serve as a benchmark for comparing the model architecture potentials of v1.0 and v2.0. It is important to note that all test Key Performance Indicator (KPI) data are proprietary and derived from the test KPI data of Retail Object Detection model. For more information of the test dataset, please check Retail Object Detection - TRAINING AND TEST DATA.

Methodology and KPI

The performance of Retail Object Recognition models is measured using Accuracy, which is the proportion of correct predictions (all classes) made by the model out of all predictions.

Accuracy of the Classification Head

\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	Model Architecture	Training dataset description	Test dataset description	Accuracy
Retail Object Recognition - v1.0	ResNet 101	A mixture of 60k synthetic images and 53k real images, 100 retail subjects. Real images are obtained from 6 scenes.	3,422 images, 100 retail subjects, 6 scenes in total	0.8453
Retail Object Recognition - v1.1 (not released)	ResNet 101	A mixture of 80k synthetic images and 48kk real images, 315 retail subjects. Real images are obtained from 7 scenes (added one more scene based on v1.0 model training dataset)	58,699 images, 315 retail subjects, 7 scenes in total (added one more scene based on v1.0 model test dataset)	0.7038
Retail Object Recognition - v2.0	FAN-Base-Hybrid	Same as v1.1	Same as v1.1	0.8797

Accuracy of the Embedding Head.

\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\">

# of test images/class	# of images/class in reference database	FAN-Base-Hybrid Accuracy
180	1	0.7718
180	5	0.8432
180	10	0.8485
180	20	0.8541
180	30	0.8561
180	40	0.8557
180	50	0.8577
180	60	0.8567
180	70	0.8581
180	80	0.8591
180	90	0.8605
180	100	0.8587

Real-time Inference Performance

The inference is run on the provided unpruned model at FP16 precision. The model input resolution is 224x224. The inference performance is run using trtexec on Jetson AGX Orin 64GB and A10. 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.

Version 1.0

\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\">

model	device	batch size	Latency (ms)	Images per second
Retail Object Recognition - v1.0	Jetson AGX Orin 64GB	1	1.59	627
Retail Object Recognition - v1.0	Jetson AGX Orin 64GB	16	12.83	1247
Retail Object Recognition - v1.0	Jetson AGX Orin 64GB	32	23.61	1356
Retail Object Recognition - v1.0	Tesla A10	1	0.98	1018
Retail Object Recognition - v1.0	Tesla A10	16	5.95	2690
Retail Object Recognition - v1.0	Tesla A10	64	20.61	3106

Version 2.0

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

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

model	device	batch size	Latency (ms)	Images per second
Retail Object Recognition - v2.0	Jetson Orin Nano	4	99.50	40.2
Retail Object Recognition - v2.0	Orin NX 16GB	4	66.89	59.8
Retail Object Recognition - v2.0	AGX Orin 64GB	8	49.69	161
Retail Object Recognition - v2.0	A2	16	99.38	161
Retail Object Recognition - v2.0	T4	8	31.62	253
Retail Object Recognition - v2.0	A30	16	21.80	734
Retail Object Recognition - v2.0	L4	4	6.63	603
Retail Object Recognition - v2.0	L40	8	4.85	1648
Retail Object Recognition - v2.0	A100	64	39.48	1621
Retail Object Recognition - v2.0	H100	64	23.83	2686

How to use this model

Instructions to use unpruned model with TAO

The trainable version of this model can be used for transfer learning via TAO Toolkit, while the deployable versions can be used for inference deployment via DeepStream/TensorRT.

Instructions to transfer learning the model with TAO Toolkit

Currently we only support transfer learning for Retail Object Recognition version 1.0. To proceed with this, kindly adhere to the following steps.

\n
Download and install TAO Toolkit.
\n
\n
In order to use the model as pre-trained weights for transfer learning, please use the snippet below as a template for the model component of the experiment spec file to train a Metric Learning Recognition model. Below is a code snippt to config the pretrained weights of Retail Object Recognition model v1.0 to Metric Learning Recognition transfer learning in TAO. For more details, check TAO - Metric Learning Recognition Documentation
\n

model:\n  backbone: resnet_101\n  pretrain_choice: \"\"\n  pretrained_model_path: /path/to/retail_object_recognition.pth\n  input_width: 224\n  input_height: 224\n  feat_dim: 2048\n\ntrain:\n  optim:\n    ...\n  smooth_loss: True\n  num_epochs: 100\n  checkpoint_interval: 5\n  resume_training_checkpoint_path: null\n  batch_size: 16\n  val_batch_size: 16\n  report_accuracy_per_class: True\n\ndataset:\n  train_dataset: /path/to/training/dataset\n  val_dataset: \n    reference: /path/to/reference/dataset\n    query: /path/to/query/dataset\n

To start the training of Metric Learning Recognition model, use following command:

\n\">

tao model ml_recog train -e=<train config file>\n

For more information on experiment spec file and training with TAO Toolkit, please refer to the notebook example at TAO Toolkit - Jupyter notebooks - Retail Object Recognition.

Instructions to deploy Retail Object Recognition Model (version 1.0) with TAO Toolkit

The TAO Toolkit also provides support for deploying Retail Object Recognition model v1.0 through TAO-Deploy. The ONNX model can be converted into a TensorRT engine first and then used for inference with a reference database. To proceed with this, please follow the steps below after downloading and installing the TAO Toolkit as instructed in the last section.

Convert the deployable (onnx file) Retail Object Recognition model v1.0 to TensorRT engine. Below is the code snippt for this conversion.

gen_trt_engine:\n  gpu_id: 0\n  onnx_file: /path/to/deployable/retail/object/recognition/v1.0\n  trt_engine: /path/to/converted/trt/engine/file\n  tensorrt:\n    data_type: fp32\n    workspace_size: 1024\n    min_batch_size: 1\n    opt_batch_size: 10\n    max_batch_size: 10\n

To launch the TensorRT conversion, use

\n\">

tao deploy ml_recog gen_trt_engine -e=<trt conversion config file>\n

Run Inference with the converted TensorRT engine file. Below is a code snippt to config the pretrained weights of Retail Object Recognition model v1.0 to Metric Learning Recognition transfer learning in TAO.

inference:\n  trt_engine: /path/to/converted/trt/engine/file\n  input_path: /path/to/test/images\n  inference_input_type: image_folder\n  topk: 5\n  ...\n\ndataset:\n  train_dataset: \"\"\n  val_dataset: \n    reference: /path/to/reference/dataset\n    query: \"\"\n  ...\n

To conduct the inference, use following command:

\n\">

tao deploy ml_recog inference -e=<inference config file>\n

For more information on experiment spec files and running inference with TAO Toolkit, please refer to the notebook example at TAO Toolkit - Jupyter notebooks - Retail Object Recognition.

Limitations

Very Small Objects

NVIDIA Retail Object Recognition models are trained to classify objects larger than 10x10 pixels. Therefore it may generate poor results when classifying objects that are smaller than 10x10 pixels.

Occluded Objects

When objects are occluded or truncated such that less than 40% of the object is visible, they may not be correctly classified by the Retail Object Detection model. Partial occlusion by hand is acceptable as the model was trained with examples having random occlusions.

Monochrome or Infrared Camera Images

The Retail Object Recognition models are trained on RGB images. Therefore, images captured in a monochrome image or IR camera image may not provide good detection results.

Warped and Blurry Images

The Retail Object Recognition models are 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.

Model versions

Deployable_v1.0: The encrypted onnx file and encrypted etlt file for Retail Object Recognition model, inferencable on DeepStream and TAO Toolkit.
Trainable_v1.0: The checkpoint for Retail Object Recognition model, trainable on TAO Toolkit.
Deployable_v2.0: The decrypted onnx file for Retail Recognition model, inferencable on DeepStream.

References

Citations

\n
Hoffer, Elad, and Nir Ailon. \"Deep metric learning using triplet network.\" International workshop on similarity-based pattern recognition. Springer, Cham, 2015.
\n
\n
Na, Shi, Liu Xumin, and Guan Yong. \"Research on k-means clustering algorithm: An improved k-means clustering algorithm.\" 2010 Third International Symposium on intelligent information technology and security informatics. Ieee, 2010.
\n
\n
Daquan Zhou, Zhiding Yu, Enze Xie, Chaowei Xiao, Anima Anandkumar, Jiashi Feng and Jose M. Alvarez. \"Understanding The Robustness in Vision Transformers\". International Conference on Machine Learning (ICML). 2022
\n

Technical blogs

Learn how to train real-time License plate detection and recognition app with TAO and DeepStream.
Model accuracy is extremely important, learn how you can achieve state of the art accuracy for classification and object detection models using TAO
Learn how to train and deploy real-time intelligent video analytics apps and services using DeepStream SDK

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 these licenses.

Ethical Considerations

NVIDIA Retail Object Recognition model classifies retail objects. However, no additional information such as people and other distractors in the background are inferred.\nTraining and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies.\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.

","resourceVersions",[195,196,197,198,199,200,201],"resourceVersionsTotal",6,"hasMoreVersions","versionMetadata",{"_168":21,"_169":170,"_48":171,"_172":-5,"_173":174,"_175":176,"_177":78,"_178":16,"_179":30,"_180":-5,"_181":-5,"_182":30,"_183":-5,"_184":30,"_185":-5,"_186":30,"_187":-5,"_188":30,"_189":-5,"_190":30,"_191":-5,"_192":30,"_193":194},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/tao/models/retail_object_recognition/-","version","imageReference","registry","targetPath","cliDownloadCommand","ngc registry model download-version nvidia/tao/retail_object_recognition:deployable_v1.1","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/tao/models/retail_object_recognition/deployable_v1.1/version-history","accessCheckResult",{"_167":84},"hasRelatedCollections","showNimMetadataTab","context",{"_163":164,"_165":166},1793968182973,[],[],[156,157,158,159,160,161,162],{"_44":58,"_46":68,"_48":60,"_50":61,"_52":69,"_54":55},{"_44":58,"_46":67,"_48":60,"_50":61,"_52":66,"_54":55},{"_44":58,"_46":65,"_48":60,"_50":61,"_52":66,"_54":55},{"_44":58,"_46":63,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":59,"_48":60,"_50":61,"_52":62,"_54":55},{"_44":56,"_46":57,"_48":49,"_50":51,"_52":53,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55},"orgName","nvidia","teamName","tao","allowed","versionId","description","","UPLOAD_COMPLETE","releaseType","createdDate",["D",1689634423590],"updatedDate",["D",1740841964224],"totalSizeInBytes","totalFileCount","isSigned","digest","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_74":213,"_76":214,"_172":-5,"_179":84,"_182":30,"_193":212,"_184":30},{"_74":211,"_76":211,"_172":-5,"_179":84,"_182":30,"_193":212,"_184":30},{"_74":209,"_76":209,"_172":-5,"_179":84,"_182":30,"_193":210,"_184":30},{"_74":207,"_76":207,"_172":-5,"_179":30,"_182":30,"_193":208,"_184":30},{"_74":205,"_76":205,"_172":-5,"_179":30,"_182":30,"_193":206,"_184":30},{"_74":203,"_76":203,"_172":-5,"_179":30,"_182":30,"_193":204,"_184":30},{"_74":21,"_76":21,"_172":-5,"_179":30,"_182":30,"_193":202,"_184":30},[],"trainable_v1.0",[],"deployable_v1.0",[],"deployable_v2.0",[],"deployable_onnx_v1.0",[],"trainable_head_fan_base_v2.0",[],"-","Latest (trainable_head_fan_base_v2.0)","nvidia/tao/retail_object_recognition","key","nvidia/tao/models/retail_object_recognition","resourceType","models","resourceId","retail_object_recognition","shortDescription","Embedding generator model to recognize objects on a checkout counter.","# Retail Object Recognition\n\n## Model Overview \nThis model encodes retail objects to embedding vectors and predicts their labels based on the embedding vectors in the reference space. \n\nIn version 2.0, the Retail Object Recognition model both generates embedding vectors in a reference space and provides a classification output to categorize the objects into seen classes, which refer to the classes that have appeared in the training datasets.\n\nBoth versions 1.0 and 2.0 are adept at executing few-shot learning, having been trained through the process of metric learning. For a more comprehensive understanding of metric learning, please consult the provided references.\n\n## Model Version Summary \n\n| Model Version | Model Architecture | Input resolution | Model Size | Training Number of Classes | Task | Training data - synthetic images | Training data - real images | Decryption Code |\n| -------------------------- | ------------------ | ---------------- | ---------- | -------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------ | --------------- |\n| Retail Object Recognition v1.0 | ResNet101 | 224x224 | 46.7M | 100 | Recognition retail objects based on embedding vectors in reference space | 600k, 100 retail subjects | 50k, 100 retail subjects, cropped from 6 scenes | nvidia_tlt |\n| Retail Object Recognition v2.0 | FAN-Base-Hybrid | 224x224 | 205.8M | 315 | This task involves a dual-output network structure, which generates an embedding vectors in reference space and a classification output for categorizing the objects into seen classes. | 80k synthetic data with improved complexity and variances compaed to training data in v1.0 | 48k, 315 retail subjects, cropped from 7 scenes (added one more scene from real training data in v1.0) | None |\n\n\n## Model Architecture \n\n### ResNet\nVersion 1.0 consists of a traunk and embedded. The trunk is a ResNet101 classification network with its fully connected layers removed. The embedder is a one-layer Perceptron with an input size of 2048 (the output dimension from the averagePool layer of the ResNet101 trunk) and an output size of 2048. Thus the embedding dimension of the Retail Object Recognition model is 2048.\n\n### FAN-Base-Hybrid\nThe Retail Object Recognition model (version 2.0) is composed of three primary components: a trunk, a classification head, and an embedding head. \nThe trunk uses the [FAN-Base-Hybrid](https://arxiv.org/pdf/2204.12451.pdf) (Fully Attentional Network) model architecture for feature extraction. The embedding head uses four adaptors with an output size of 448. The adaptor consists of two fully connected layers, GELU activation functions and residual connection. The classifier is derived from the adaptors along with a one-layer perceptron with an output size corresponding to the number of classes. \n\n## Training \n\n### Version 1.0\nThe Retail Object Recognition model (version 1.0) was trained with the Triplet Loss network algorithm. The training algorithm optimizes the network to minimize the embedding output distances (cosine similarity) between the positive images and the anchor image while maximizing the distances between the negative images and the anchor image. \n\nThe trunk and embedder use different learning rates during training. The embedder uses a smaller learning rate than the trunk for a better fine-tuning effect.\n\n### Version 2.0\nFor the Retail Object Recognition model (version 2.0), the training algorithm optimizes the network to minimize the embedding features and target classes for the objects.\nThe trunk is initialized by the FAN-Base-Hybrid pretrained model and remains frozen during the training process.\nThe embedder is trained using the Triplet Loss algorithm, an approach that enhances the network by reducing the cosine similarity-measured distances between the anchor image and positive images, while concurrently extending the distances between the anchor image and negative images.\nThe classifier was trained through the Cross-entropy algorithm, minimizing the distance between the images and their corresponding classes.\nThe classifier and embedder are jointly trained that can potentially enhances the precision by leveraged on the features from both branches. \n\n### Training Data \nThe training data of the Retail Object Recognition model was cropped from images for Retail Object Detection model training and fine-tuning data (see [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection) - TRAINING DATA). Thus it is made up of both synthetic data and real data. By mixing the synthetic and real images in the training set, the model is enhanced, allowing it to bridge the gap between simulation and reality. Consequently, the model can learn feature representations from both synthetic and real image sources. \n\nThe training data encompasses multiple angles of the retail objects, which equips the model to identify a retail object from any given angle.\n\n#### Version 1.0\nSpecifically, the model was trained on a mixture of 0.6 million synthetic images and 50k real images. During the training phase, the triplet loss on the mixture would be optimized. And during the validation phase, the accuracy of the similarity search would be calculated. The reference data for the validation set are synthetic while the query data for the validation set are real images. \n\n\n| dataset | Total # of images | Training images | Testing images |\n| -------------- | ----------------- | --------------- | -------------- |\n| Synthetic data | 600,000 | 600,000 | \\- |\n| Real data | 56,898 | 53,476 | 3,422 |\n\n

\n\n\n#### Version 2.0\nThe Retail Object Recognition model (version 2.0) trained with a new retail dataset, which total has 315 distinct categories.\n\nSpecifically, the model's training involved a combined dataset of more than 80,000 synthetic images and 48,000 real images. In the training phrase, both cross-entropy loss and triple loss are optimized with this composite dataset. In the testing phase, the probabilities for each category are computed. In parallel, a similarity search is conducted, which used synthetic data as the reference and real data as the query. \n\n\n| Dataset | Total # of images | Training images | Testing images|\n|--|--|--|--|\n|Synthetic Data| 80,872 | 80,872 | -\n|Real Data | 129,012 | 48,140 | 58,699 \n\n

\n\n### Inference Data Ground-truth Labeling Guidelines\nThe real training images were cropped from Retail Object Detection datasets with ground-truth bounding-boxes and categories by human labellers. To run inference on your own datasets, you may follow the guidelines below.\n\n#### Reference Data Guidelines\n\nReference data is the database for similarity search during the inference stage for the Retail Object Recognition model. The prediction of the inference images would be decided by the L2 distances of the extracted features. Specifically, the algorithm would select the reference object with the smallest L2 distance to the query object in the reference database by Kmeans, and the predicted class would be the corresponding class of the selected reference object. \n\nTherefore, to achieve the highest accuracy for retail object recognition, the reference data needs to be as close to the inference data as possible, regarding the background, occlusion, object orientations, etc.\n\nFor instance, if you decide that you only want to infer the retail objects with the front face, then you can collect the front side of the retail objects only as reference data. On the other hand, if you want the Retail Object Recognition model to recognize the objects with whatever angles presented, then more orientations of the retail objects need to be collected in the reference dataset.\n\nGenerally 20-30 images/class for reference data is of the highest efficiency. However, it would be definitely better to collect more reference examples, say 100 images/class. \n\nBelow are the guidelines for the specific conditions of the images:\n1. All objects should take at least 70% of the frame. \n2. Noisy background: images with noises in the backgrounds are fine.\n3. Occlusion: objects occluded by distractions such as hands are fine. But at least 60% of the object should be visible.\n4. Truncation: objects truncated by the edge of the frame with visibility of >= 60% are fine.\n5. Each image should be assigned to a specific class. This model does not accommodate the case when multiple classes are classified to some “other”/”unknown” class.\n\n#### Query Data Guidelines\n\nSame as the reference data guidelines. \n\nNotice that the Retail Object Recognition model can never correctly classify the retail object if the class is not in the reference dataset.\n\nTo get the most accurate predictions, you should avoid challenging the Retail Object Recognition model with some bad views, such as the top of a soda can (as this view can be the same across many different retail objects).\n\n## Performance\n### Evaluation Data \n\nHere, we present the evaluation results of Retail Object Recognition models, including versions 1.0, 2.0, and an unreleased version 1.1. The purpose of version 1.1 is to serve as a benchmark for comparing the model architecture potentials of v1.0 and v2.0. It is important to note that all test Key Performance Indicator (KPI) data are proprietary and derived from the test KPI data of [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection) model. For more information of the test dataset, please check [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection) - TRAINING AND TEST DATA.\n\n### Methodology and KPI\n\nThe performance of Retail Object Recognition models is measured using Accuracy, which is the proportion of correct predictions (all classes) made by the model out of all predictions.\n\nAccuracy of the Classification Head\n| Model | Model Architecture | Training dataset description | Test dataset description | Accuracy |\n| ------------------------------------------- | ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | -------- |\n| Retail Object Recognition - v1.0 | ResNet 101 | A mixture of 60k synthetic images and 53k real images, 100 retail subjects. Real images are obtained from 6 scenes. | 3,422 images, 100 retail subjects, 6 scenes in total | 0.8453 |\n| Retail Object Recognition - v1.1 (not released) | ResNet 101 | A mixture of 80k synthetic images and 48kk real images, 315 retail subjects. Real images are obtained from 7 scenes (added one more scene based on v1.0 model training dataset) | 58,699 images, 315 retail subjects, 7 scenes in total (added one more scene based on v1.0 model test dataset) | 0.7038 |\n| Retail Object Recognition - v2.0 | FAN-Base-Hybrid | Same as v1.1 | Same as v1.1 | 0.8797 |\n \nAccuracy of the Embedding Head.\n| # of test images/class | # of images/class in reference database | FAN-Base-Hybrid Accuracy \n|-- |-- |--|\n|180 | 1 | 0.7718\n|180 | 5 | 0.8432\n|180 | 10 | 0.8485\n|180 | 20 | 0.8541\n|180 | 30 | 0.8561\n|180 | 40 | 0.8557\n|180 | 50 | 0.8577\n|180 | 60 | 0.8567\n|180 | 70 | 0.8581\n|180 | 80 | 0.8591\n|180 | 90 | 0.8605\n|180 | 100| 0.8587\n\n### Real-time Inference Performance \nThe inference is run on the provided unpruned model at FP16 precision. The model input resolution is 224x224. The inference performance is run using [trtexec](https://github.com/NVIDIA/TensorRT/tree/master/samples/trtexec) on Jetson AGX Orin 64GB and A10. 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#### Version 1.0\n\n| model | device | batch size | Latency (ms) | Images per second |\n| -------------------------------- | -------------------- | ---------- | ------------ | ----------------- |\n| Retail Object Recognition - v1.0 | Jetson AGX Orin 64GB | 1 | 1.59 | 627 |\n| Retail Object Recognition - v1.0 | Jetson AGX Orin 64GB | 16 | 12.83 | 1247 |\n| Retail Object Recognition - v1.0 | Jetson AGX Orin 64GB | 32 | 23.61 | 1356 |\n| Retail Object Recognition - v1.0 | Tesla A10 | 1 | 0.98 | 1018 |\n| Retail Object Recognition - v1.0 | Tesla A10 | 16 | 5.95 | 2690 |\n| Retail Object Recognition - v1.0 | Tesla A10 | 64 | 20.61 | 3106 |\n\n\n#### Version 2.0\n| model | device | batch size | Latency (ms) | Images per second |\n| -------------------------------- | ---------------- | ---------- | ------------ | ----------------- |\n| Retail Object Recognition - v2.0 | Jetson Orin Nano | 4 | 99.50 | 40.2 |\n| Retail Object Recognition - v2.0 | Orin NX 16GB | 4 | 66.89 | 59.8 |\n| Retail Object Recognition - v2.0 | AGX Orin 64GB | 8 | 49.69 | 161 |\n| Retail Object Recognition - v2.0 | A2 | 16 | 99.38 | 161 |\n| Retail Object Recognition - v2.0 | T4 | 8 | 31.62 | 253 |\n| Retail Object Recognition - v2.0 | A30 | 16 | 21.80 | 734 |\n| Retail Object Recognition - v2.0 | L4 | 4 | 6.63 | 603 |\n| Retail Object Recognition - v2.0 | L40 | 8 | 4.85 | 1648 |\n| Retail Object Recognition - v2.0 | A100 | 64 | 39.48 | 1621 |\n| Retail Object Recognition - v2.0 | H100 | 64 | 23.83 | 2686 |\n\n## How to use this model \n### Instructions to use unpruned model with TAO\nThe trainable version of this model can be used for transfer learning via [TAO Toolkit](https://docs.nvidia.com/tao/tao-toolkit/), while the deployable versions can be used for inference deployment via DeepStream/TensorRT.\n\n### Instructions to transfer learning the model with TAO Toolkit\n\nCurrently we only support transfer learning for Retail Object Recognition version 1.0. To proceed with this, kindly adhere to the following steps.\n\n1. [Download](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html) and install TAO Toolkit.\n\n2. In order to use the model as pre-trained weights for transfer learning, please use the snippet below as a template for the model component of the experiment spec file to train a `Metric Learning Recognition` model. Below is a code snippt to config the pretrained weights of Retail Object Recognition model v1.0 to Metric Learning Recognition transfer learning in TAO. For more details, check [TAO - Metric Learning Recognition Documentation](https://docs.nvidia.com/tao/tao-toolkit/text/metric_learning_recognition/index.html)\n\n```\nmodel:\n backbone: resnet_101\n pretrain_choice: \"\"\n pretrained_model_path: /path/to/retail_object_recognition.pth\n input_width: 224\n input_height: 224\n feat_dim: 2048\n\ntrain:\n optim:\n ...\n smooth_loss: True\n num_epochs: 100\n checkpoint_interval: 5\n resume_training_checkpoint_path: null\n batch_size: 16\n val_batch_size: 16\n report_accuracy_per_class: True\n\ndataset:\n train_dataset: /path/to/training/dataset\n val_dataset: \n reference: /path/to/reference/dataset\n query: /path/to/query/dataset\n```\n\n3. To start the training of `Metric Learning Recognition` model, use following command:\n\n```bash\ntao model ml_recog train -e=\n```\n\n4. For more information on experiment spec file and training with TAO Toolkit, please refer to the notebook example at [TAO Toolkit](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/resources/tao-getting-started) - Jupyter notebooks - Retail Object Recognition.\n\n\n### Instructions to deploy Retail Object Recognition Model (version 1.0) with TAO Toolkit\n\nThe TAO Toolkit also provides support for deploying Retail Object Recognition model v1.0 through [TAO-Deploy](https://docs.nvidia.com/tao/tao-toolkit/text/metric_learning_recognition/metric_learning_recognition.html#deploying-the-model). The ONNX model can be converted into a [TensorRT](https://developer.nvidia.com/tensorrt) engine first and then used for inference with a reference database. To proceed with this, please follow the steps below after downloading and installing the TAO Toolkit as instructed in the last section.\n\n1. Convert the deployable (onnx file) Retail Object Recognition model v1.0 to TensorRT engine. Below is the code snippt for this conversion.\n\n```\ngen_trt_engine:\n gpu_id: 0\n onnx_file: /path/to/deployable/retail/object/recognition/v1.0\n trt_engine: /path/to/converted/trt/engine/file\n tensorrt:\n data_type: fp32\n workspace_size: 1024\n min_batch_size: 1\n opt_batch_size: 10\n max_batch_size: 10\n```\n\nTo launch the TensorRT conversion, use\n```bash\ntao deploy ml_recog gen_trt_engine -e=\n```\n\n2. Run Inference with the converted TensorRT engine file. Below is a code snippt to config the pretrained weights of Retail Object Recognition model v1.0 to `Metric Learning Recognition` transfer learning in TAO.\n\n```\ninference:\n trt_engine: /path/to/converted/trt/engine/file\n input_path: /path/to/test/images\n inference_input_type: image_folder\n topk: 5\n ...\n\ndataset:\n train_dataset: \"\"\n val_dataset: \n reference: /path/to/reference/dataset\n query: \"\"\n ...\n```\n\nTo conduct the inference, use following command:\n```bash\ntao deploy ml_recog inference -e=\n```\n\n3. For more information on experiment spec files and running inference with TAO Toolkit, please refer to the notebook example at [TAO Toolkit](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/resources/tao-getting-started) - Jupyter notebooks - Retail Object Recognition.\n\n\n## Limitations \n### Very Small Objects\nNVIDIA Retail Object Recognition models are trained to classify objects larger than 10x10 pixels. Therefore it may generate poor results when classifying objects that are smaller than 10x10 pixels. \n### Occluded Objects\nWhen objects are occluded or truncated such that less than 40% of the object is visible, they may not be correctly classified by the Retail Object Detection model. Partial occlusion by hand is acceptable as the model was trained with examples having random occlusions. \n### Monochrome or Infrared Camera Images \nThe Retail Object Recognition models are trained on RGB images. Therefore, images captured in a monochrome image or IR camera image may not provide good detection results.\n### Warped and Blurry Images\nThe Retail Object Recognition models are 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## Model versions\n- Deployable_v1.0: The encrypted onnx file and encrypted etlt file for Retail Object Recognition model, inferencable on DeepStream and TAO Toolkit.\n- Trainable_v1.0: The checkpoint for Retail Object Recognition model, trainable on TAO Toolkit.\n- Deployable_v2.0: The decrypted onnx file for Retail Recognition model, inferencable on DeepStream.\n\n\n## References\n### Citations \n\n- Hoffer, Elad, and Nir Ailon. \"Deep metric learning using triplet network.\" *International workshop on similarity-based pattern recognition*. Springer, Cham, 2015.\n\n- Na, Shi, Liu Xumin, and Guan Yong. \"Research on k-means clustering algorithm: An improved k-means clustering algorithm.\" *2010 Third International Symposium on intelligent information technology and security informatics.* Ieee, 2010.\n\n- Daquan Zhou, Zhiding Yu, Enze Xie, Chaowei Xiao, Anima Anandkumar, Jiashi Feng and Jose M. Alvarez. \"Understanding The Robustness in Vision Transformers\". *International Conference on Machine Learning (ICML).* 2022\n\n### Technical blogs \n- Learn how to train [real-time License plate detection and recognition app](https://developer.nvidia.com/blog/creating-a-real-time-license-plate-detection-and-recognition-app) with TAO and DeepStream.\n- Model accuracy is extremely important, learn how you can achieve [state of the art accuracy for classification and object detection models](https://developer.nvidia.com/blog/preparing-state-of-the-art-models-for-classification-and-object-detection-with-tao-toolkit/) using TAO\n- Learn how to [train and deploy real-time intelligent video analytics apps and services using DeepStream SDK](https://devblogs.nvidia.com/building-iva-apps-using-deepstream-5.0/)\n\n### Suggested reading \n- More information on about TAO Toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/tao-toolkit)\n- Read the [TAO getting Started](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html) guide and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html).\n- If you have any questions or feedback, please refer to the discussions on [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17)\n- Deploy your model on the edge using DeepStream. Learn more about [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk)\n\n## License \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 these [licenses](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/).\n\n## Ethical Considerations \nNVIDIA Retail Object Recognition model classifies retail objects. However, no additional information such as people and other distractors in the background are inferred.\nTraining and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies.\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","Retail Object Recognition",["D",1710373709380],"publisher","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/retail_object_recognition","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",4608061,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[258,259,260,261,262,263,264,265,266,267,268,269,270],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_225":306,"_272":307,"_273":308,"_275":276},{"_225":302,"_272":303,"_273":304,"_275":276},{"_225":300,"_272":300,"_273":301,"_275":276},{"_225":298,"_272":298,"_273":299,"_275":276},{"_225":295,"_272":296,"_273":297,"_275":276},{"_225":291,"_272":292,"_273":293,"_275":276},{"_225":289,"_272":289,"_273":290,"_275":276},{"_225":287,"_272":287,"_273":288,"_275":276},{"_225":285,"_272":285,"_273":286,"_275":276},{"_225":282,"_272":282,"_273":283,"_275":276},{"_225":280,"_272":280,"_273":281,"_275":276},{"_225":278,"_272":278,"_273":279,"_275":276},{"_225":271,"_272":271,"_273":274,"_275":276},"Transfer Learning Toolkit","value","filter",{"_277":276,"_272":271},"category","label","field","Transfer Learning",{"_277":276,"_272":278},"TLT",{"_277":276,"_272":280},"TAO Toolkit",{"_277":284,"_272":282},"framework","TAO",{"_277":276,"_272":285},"Smart Infrastructure",{"_277":276,"_272":287},"Smart City",{"_277":276,"_272":289},"Metropolis","pltfm_metropolis",{"_277":294,"_272":292},"platform","DeepStream","pltfm_deepstream",{"_277":294,"_272":296},"Deep Learning",{"_277":276,"_272":298},"CV",{"_277":276,"_272":300},"Computer Vision","soln_computer_vision",{"_277":305,"_272":303},"technology","AI","soln_ai",{"_277":305,"_272":307},{"_105":1442,"_107":1443},"ngcApps",[1395],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1376":1377,"_1378":1379,"_1380":1381,"_1382":1383,"_1384":320,"_137":346,"_1385":1386,"_1387":55,"_1388":84,"_1389":84,"_1390":1391,"_1392":84,"_1393":1394},"amplitude",{"_1373":1374,"_981":-7,"_1375":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0yJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1367":1368,"_1369":1370,"_1371":1372},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1793968182925,"1.30.0","defaultSearchSuggestions",{"_1251":170,"_1252":1253,"_1254":1255,"_1256":1257},"announcements",[],"consentState",{"_383":1241,"_1242":30,"_1243":30,"_1244":84,"_1245":1246},"consentConfig",{"_365":366,"_367":368,"_369":370,"_371":372,"_373":374,"_375":376,"_377":378,"_379":380,"_381":382,"_383":384},"showContactAdmin",[],[],[359,360,361,362,363,364],{"_44":58,"_46":68,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":67,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":65,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":59,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55},"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",{"_1233":1234,"_1235":1236,"_1237":1238,"_1239":1240},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[385,386,387,388,389],{"_390":863,"_76":864,"_169":865,"_394":84,"_395":16,"_397":866,"_399":867},{"_390":723,"_76":724,"_169":725,"_394":30,"_395":53,"_397":726,"_399":727},{"_390":616,"_76":617,"_169":618,"_394":30,"_395":619,"_397":620,"_399":621},{"_390":401,"_76":402,"_169":403,"_394":30,"_395":404,"_397":405,"_399":406},{"_390":391,"_76":392,"_169":393,"_394":30,"_395":396,"_397":398,"_399":400},"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.",5,[530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567],[407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424],{"_425":525,"_225":525,"_169":170,"_397":526},{"_425":521,"_225":521,"_169":170,"_397":522},{"_425":516,"_225":516,"_169":170,"_397":517},{"_425":512,"_225":512,"_169":170,"_397":513},{"_425":507,"_225":507,"_169":170,"_397":508},{"_425":502,"_225":502,"_169":170,"_397":503},{"_425":495,"_225":495,"_169":170,"_397":496},{"_425":487,"_225":487,"_169":170,"_397":488},{"_425":482,"_225":482,"_169":170,"_397":483},{"_425":477,"_225":477,"_169":170,"_397":478},{"_425":469,"_225":469,"_169":170,"_397":470},{"_425":464,"_225":464,"_169":170,"_397":465},{"_425":459,"_225":459,"_169":170,"_397":460},{"_425":454,"_225":454,"_169":170,"_397":455},{"_425":447,"_225":447,"_169":170,"_397":448},{"_425":442,"_225":442,"_169":170,"_397":443},{"_425":435,"_225":435,"_169":170,"_397":436},{"_425":426,"_225":426,"_169":170,"_397":427},"hostName","amazon-adsystem.com",[428,429],{"_76":434,"_431":426,"_432":30,"_169":433},{"_76":430,"_431":426,"_432":30,"_169":433},"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",[437,438],{"_76":441,"_431":435,"_432":30,"_169":440},{"_76":439,"_431":435,"_432":30,"_169":440},"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",[444],{"_76":445,"_431":442,"_432":30,"_169":446},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[449,450],{"_76":453,"_431":447,"_432":30,"_169":452},{"_76":451,"_431":447,"_432":30,"_169":452},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_*","If you know anything about this host and its uses please add your contributions here. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","tiktok.com",[456],{"_76":457,"_431":454,"_432":30,"_169":458},"_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",[461],{"_76":462,"_431":459,"_432":30,"_169":463},"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",[466],{"_76":467,"_431":464,"_432":30,"_169":468},"sp","Testing","google.com",[471,472],{"_76":475,"_431":469,"_432":30,"_169":476},{"_76":473,"_431":469,"_432":30,"_169":474},"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.","__Secure-*","Auto-detected during scan","tags.srv.stackadapt.com",[479],{"_76":480,"_431":477,"_432":30,"_169":481},"sa-user-*","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","linkedin.com",[484],{"_76":485,"_431":482,"_432":30,"_169":486},"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",[489,490],{"_76":493,"_431":487,"_432":30,"_169":494},{"_76":491,"_431":487,"_432":30,"_169":492},"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",[497,498],{"_76":475,"_431":495,"_432":30,"_169":501},{"_76":499,"_431":495,"_432":30,"_169":500},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","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",[504],{"_76":505,"_431":502,"_432":30,"_169":506},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[509],{"_76":510,"_431":507,"_432":30,"_169":511},"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",[514],{"_76":515,"_431":512,"_432":30,"_169":468},"R","6sc.co",[518],{"_76":519,"_431":516,"_432":30,"_169":520},"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",[523],{"_76":524,"_431":521,"_432":30,"_169":468},"_hly_gvid","t.contentsquare.net",[527],{"_76":528,"_431":525,"_432":84,"_169":529},"_cs_*","This is a session cookie. This is a pattern type cookie where a random 13-digit number is appended to the prefix _cs_.",{"_76":591,"_431":590,"_432":30,"_169":592},{"_76":614,"_431":569,"_432":30,"_169":615},{"_76":480,"_431":599,"_432":30,"_169":170},{"_76":480,"_431":605,"_432":30,"_169":170},{"_76":612,"_431":569,"_432":30,"_169":613},{"_76":610,"_431":569,"_432":30,"_169":611},{"_76":457,"_431":569,"_432":30,"_169":609},{"_76":608,"_431":569,"_432":30,"_169":587},{"_76":574,"_431":569,"_432":30,"_169":576},{"_76":607,"_431":569,"_432":30,"_169":587},{"_76":606,"_431":569,"_432":30,"_169":170},{"_76":583,"_431":572,"_432":30,"_169":170},{"_76":583,"_431":605,"_432":30,"_169":585},{"_76":603,"_431":569,"_432":30,"_169":604},{"_76":601,"_431":569,"_432":30,"_169":602},{"_76":600,"_431":569,"_432":30,"_169":170},{"_76":583,"_431":599,"_432":30,"_169":585},{"_76":597,"_431":569,"_432":30,"_169":598},{"_76":595,"_431":575,"_432":30,"_169":596},{"_76":574,"_431":594,"_432":84,"_169":576},{"_76":591,"_431":572,"_432":30,"_169":592},{"_76":591,"_431":575,"_432":30,"_169":592},{"_76":593,"_431":569,"_432":30,"_169":587},{"_76":591,"_431":569,"_432":30,"_169":592},{"_76":571,"_431":590,"_432":30,"_169":573},{"_76":589,"_431":569,"_432":30,"_169":587},{"_76":528,"_431":569,"_432":30,"_169":529},{"_76":588,"_431":569,"_432":30,"_169":587},{"_76":586,"_431":569,"_432":30,"_169":587},{"_76":583,"_431":584,"_432":30,"_169":585},{"_76":467,"_431":581,"_432":30,"_169":582},{"_76":480,"_431":572,"_432":30,"_169":170},{"_76":579,"_431":569,"_432":30,"_169":580},{"_76":577,"_431":569,"_432":30,"_169":578},{"_76":451,"_431":569,"_432":30,"_169":170},{"_76":574,"_431":575,"_432":84,"_169":576},{"_76":571,"_431":572,"_432":30,"_169":573},{"_76":568,"_431":569,"_432":84,"_169":570},"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.","_zitok","marketplace.nvidia.com","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_gat_gtag_UA_140825383_2","Google Analytics","_gat_gtag_UA_162631457_1","_gat_gtag_UA_53775284_8","forums.developer.nvidia.com","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","_gat_gtag_UA_146100304_1","catalog.ngc.nvidia.com","aws-waf-token","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.","_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. ","ai-podcast.nvidia.com","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","research.nvidia.com","_twpid","_gat_gtag_UA_141699104_1","_gat_gtag_UA_53775284_6","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686],[622,623,624,625,626,627],{"_425":507,"_225":507,"_169":170,"_397":651},{"_425":482,"_225":482,"_169":170,"_397":646},{"_425":641,"_225":641,"_169":170,"_397":642},{"_425":636,"_225":636,"_169":170,"_397":637},{"_425":632,"_225":632,"_169":170,"_397":633},{"_425":628,"_225":628,"_169":170,"_397":629},"nvcrm.my.site.com",[630],{"_76":631,"_431":628,"_432":30,"_169":170},"BrowserId","nvidia.partners",[634],{"_76":635,"_431":632,"_432":30,"_169":468},"edge","app.hushly.com",[638],{"_76":639,"_431":636,"_432":30,"_169":640},"*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",[643],{"_76":644,"_431":641,"_432":30,"_169":645},"_octo","This domain is owned by Github. The main business activity is: Source code repository and collaboration platform",[647,648],{"_76":650,"_431":482,"_432":30,"_169":486},{"_76":649,"_431":482,"_432":30,"_169":486},"lidc","li_gc",[652,653],{"_76":655,"_431":507,"_432":30,"_169":511},{"_76":654,"_431":507,"_432":30,"_169":511},"imid_*","imid",{"_76":687,"_431":572,"_432":30,"_169":689},{"_76":721,"_431":569,"_432":30,"_169":722},{"_76":687,"_431":720,"_432":30,"_169":689},{"_76":718,"_431":569,"_432":84,"_169":719},{"_76":716,"_431":569,"_432":30,"_169":717},{"_76":715,"_431":696,"_432":84,"_169":170},{"_76":687,"_431":691,"_432":30,"_169":689},{"_76":713,"_431":569,"_432":30,"_169":714},{"_76":687,"_431":575,"_432":30,"_169":689},{"_76":698,"_431":572,"_432":30,"_169":170},{"_76":712,"_431":569,"_432":30,"_169":170},{"_76":687,"_431":711,"_432":30,"_169":689},{"_76":710,"_431":569,"_432":30,"_169":704},{"_76":707,"_431":708,"_432":30,"_169":709},{"_76":705,"_431":706,"_432":30,"_169":170},{"_76":703,"_431":569,"_432":30,"_169":704},{"_76":687,"_431":702,"_432":30,"_169":689},{"_76":687,"_431":599,"_432":30,"_169":689},{"_76":701,"_431":569,"_432":30,"_169":170},{"_76":700,"_431":572,"_432":30,"_169":170},{"_76":699,"_431":572,"_432":30,"_169":170},{"_76":698,"_431":599,"_432":30,"_169":170},{"_76":697,"_431":696,"_432":84,"_169":170},{"_76":687,"_431":605,"_432":30,"_169":689},{"_76":687,"_431":696,"_432":30,"_169":689},{"_76":687,"_431":584,"_432":30,"_169":689},{"_76":687,"_431":695,"_432":30,"_169":689},{"_76":694,"_431":569,"_432":30,"_169":170},{"_76":693,"_431":569,"_432":84,"_169":689},{"_76":690,"_431":691,"_432":84,"_169":692},{"_76":687,"_431":688,"_432":30,"_169":689},"_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","__ngc_ui_apps","nvidianews.nvidia.com","docs.api.nvidia.com","readme_library","wcmmode","_im_id.1006927","_im_ses.1006927","geo_locator","la.blogs.nvidia.com","_semaJ2rWcw","This cookie is categorized as Functionality Cookie.","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","crowdsource.nvidia.com","__ngc_ui","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.",[756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799],[728,729,730,731,732],{"_425":751,"_225":751,"_169":170,"_397":752},{"_425":746,"_225":746,"_169":170,"_397":747},{"_425":495,"_225":495,"_169":170,"_397":743},{"_425":736,"_225":736,"_169":170,"_397":737},{"_425":641,"_225":641,"_169":170,"_397":733},[734],{"_76":735,"_431":641,"_432":84,"_169":645},"_gh_sess","statcounter.com",[738,739],{"_76":742,"_431":736,"_432":30,"_169":741},{"_76":740,"_431":736,"_432":30,"_169":741},"is_unique","This domain is owned by StatCounter.\nThe main business activity is: Real time visitor analytics","is_visitor_unique",[744],{"_76":745,"_431":495,"_432":84,"_169":501},"YSC","bizible.com",[748],{"_76":749,"_431":746,"_432":30,"_169":750},"_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",[753],{"_76":754,"_431":751,"_432":30,"_169":755},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_76":861,"_431":569,"_432":30,"_169":862},{"_76":859,"_431":569,"_432":84,"_169":860},{"_76":856,"_431":857,"_432":30,"_169":858},{"_76":834,"_431":847,"_432":84,"_169":835},{"_76":854,"_431":569,"_432":30,"_169":855},{"_76":808,"_431":572,"_432":30,"_169":704},{"_76":808,"_431":605,"_432":30,"_169":704},{"_76":852,"_431":569,"_432":30,"_169":853},{"_76":836,"_431":691,"_432":84,"_169":837},{"_76":834,"_431":575,"_432":84,"_169":835},{"_76":850,"_431":569,"_432":30,"_169":851},{"_76":848,"_431":569,"_432":30,"_169":849},{"_76":842,"_431":569,"_432":30,"_169":170},{"_76":833,"_431":847,"_432":30,"_169":476},{"_76":834,"_431":572,"_432":84,"_169":835},{"_76":845,"_431":605,"_432":30,"_169":846},{"_76":843,"_431":696,"_432":30,"_169":844},{"_76":834,"_431":720,"_432":84,"_169":835},{"_76":842,"_431":691,"_432":30,"_169":170},{"_76":806,"_431":572,"_432":30,"_169":807},{"_76":833,"_431":572,"_432":30,"_169":476},{"_76":840,"_431":569,"_432":84,"_169":841},{"_76":838,"_431":569,"_432":30,"_169":839},{"_76":836,"_431":569,"_432":84,"_169":837},{"_76":834,"_431":691,"_432":84,"_169":835},{"_76":833,"_431":691,"_432":30,"_169":476},{"_76":831,"_431":569,"_432":30,"_169":832},{"_76":829,"_431":569,"_432":30,"_169":830},{"_76":827,"_431":569,"_432":84,"_169":828},{"_76":824,"_431":825,"_432":30,"_169":826},{"_76":806,"_431":691,"_432":30,"_169":807},{"_76":822,"_431":569,"_432":84,"_169":823},{"_76":820,"_431":569,"_432":30,"_169":821},{"_76":819,"_431":691,"_432":84,"_169":814},{"_76":817,"_431":569,"_432":84,"_169":818},{"_76":815,"_431":584,"_432":30,"_169":816},{"_76":813,"_431":691,"_432":84,"_169":814},{"_76":811,"_431":569,"_432":30,"_169":812},{"_76":809,"_431":569,"_432":30,"_169":810},{"_76":808,"_431":569,"_432":30,"_169":704},{"_76":806,"_431":584,"_432":30,"_169":807},{"_76":804,"_431":569,"_432":30,"_169":805},{"_76":802,"_431":569,"_432":84,"_169":803},{"_76":800,"_431":569,"_432":30,"_169":801},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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.","s_tp","This cookie is associated with Adobe Analytics. The cookie is used for measuring a visitor’s scroll activity to see how much of a page they view before moving on to another page.","_an_uid","This cookie is likely used for analytics purposes to track user behavior and site usage, which may include unique user identification for tracking.","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","s_ips","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","s_plt","This cookie is used to measure the time taken for pages to load, allowing website owners to identify slow loading pages and improve user experience.","_session_id","resources.nvidia.com","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. This ensures that behavior in subsequent visits to the same site will be attributed to the same user ID.","__sec_*","_hjHasCachedUserAttributes","This cookie name is associated with Hotjar which is used to know whether the data set in _hjUserAttributes Local Storage item is up to date or not.","s_ppv","This cookie is associated with Adobe Analytics site tracking and is used to measure a user's scroll activity to see how much of a page they view before moving to another page.","_gid","This cookie name is associated with Google Universal Analytics. This appears to be a new cookie and as of Spring 2017 no information is available from Google. It appears to store and update a unique value for each page visited.","_hjTLDTest","When the Hotjar script executes we try to determine the most generic cookie path we should use, instead of the page hostname. This is done so that cookies can be shared across subdomains (where applicable). 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.","nv.*","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.","sc_is_visitor_unique","This cookie is used by StatCounter to determine whether a visitor is a unique visitor or a returning visitor, helping to estimate the number of unique visitors to the site.","docs.nvidia.com","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.","_gat","This cookie name is associated with Google Universal Analytics, according to documentation it is used to throttle the request rate - limiting the collection of data on high traffic sites. It expires after 10 minutes.","_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.","_biz_*","This cookie 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.","dmn_chk_-xxxxxxxxxxxx","com","Posthog Analytics","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","_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.",[1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103,1104,1105,1106,1107,1108,1109,1110,1111,1112,1113,1114,1115,1116,1117,1118,1119,1120,1121,1122,1123,1124,1125,1126,1127,1128,1129,1130,1131,1132,1133,1134,1135,1136,1137],[868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898],{"_425":1043,"_225":1043,"_169":170,"_397":1044},{"_425":1037,"_225":1037,"_169":170,"_397":1038},{"_425":1034,"_225":1034,"_169":170,"_397":1035},{"_425":1031,"_225":1031,"_169":170,"_397":1032},{"_425":1019,"_225":1019,"_169":170,"_397":1020},{"_425":1016,"_225":1016,"_169":170,"_397":1017},{"_425":1013,"_225":1013,"_169":170,"_397":1014},{"_425":1008,"_225":1008,"_169":170,"_397":1009},{"_425":495,"_225":495,"_169":170,"_397":1005},{"_425":1002,"_225":1002,"_169":170,"_397":1003},{"_425":997,"_225":997,"_169":170,"_397":998},{"_425":487,"_225":487,"_169":170,"_397":995},{"_425":482,"_225":482,"_169":170,"_397":993},{"_425":988,"_225":988,"_169":170,"_397":989},{"_425":983,"_225":983,"_169":170,"_397":984},{"_425":978,"_225":978,"_169":170,"_397":979},{"_425":974,"_225":974,"_169":170,"_397":975},{"_425":971,"_225":971,"_169":170,"_397":972},{"_425":641,"_225":641,"_169":170,"_397":968},{"_425":459,"_225":459,"_169":170,"_397":965},{"_425":636,"_225":636,"_169":170,"_397":959},{"_425":632,"_225":632,"_169":170,"_397":953},{"_425":946,"_225":946,"_169":170,"_397":947},{"_425":941,"_225":941,"_169":170,"_397":942},{"_425":936,"_225":936,"_169":170,"_397":937},{"_425":932,"_225":932,"_169":170,"_397":933},{"_425":927,"_225":927,"_169":170,"_397":928},{"_425":922,"_225":922,"_169":170,"_397":923},{"_425":628,"_225":628,"_169":170,"_397":915},{"_425":907,"_225":907,"_169":170,"_397":908},{"_425":899,"_225":899,"_169":170,"_397":900},"www.pny.com",[901,902],{"_76":905,"_431":899,"_432":30,"_169":906},{"_76":903,"_431":899,"_432":84,"_169":904},"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.","AWSALB","AWS ELB application load balancer","polyfill.io",[909,910],{"_76":913,"_431":907,"_432":30,"_169":914},{"_76":911,"_431":907,"_432":30,"_169":912},"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. ",[916,917],{"_76":920,"_431":628,"_432":30,"_169":921},{"_76":918,"_431":628,"_432":30,"_169":919},"CookieConsentPolicy","Used to apply end-user cookie consent preferences set by our client-side utility.","LSKey-c$CookieConsentPolicy","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","web.prd.q4inc.com",[924],{"_76":925,"_431":922,"_432":84,"_169":926},"_cfuvid","The _cfuvid cookie is used by Cloudflare to distinguish individual users who share the same IP address for the purpose of enforcing rate limiting rules.","www.recaptcha.net",[929],{"_76":930,"_431":927,"_432":30,"_169":931},"_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",[934],{"_76":935,"_431":932,"_432":30,"_169":468},"__cflb","instagram.com",[938],{"_76":939,"_431":936,"_432":30,"_169":940},"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",[943],{"_76":944,"_431":941,"_432":30,"_169":945},"__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",[948,949],{"_76":952,"_431":946,"_432":30,"_169":468},{"_76":950,"_431":946,"_432":30,"_169":951},"AWSALBTG","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","AWSALBTGCORS",[954,955],{"_76":957,"_431":632,"_432":30,"_169":958},{"_76":956,"_431":632,"_432":30,"_169":468},"ak_bmsc","bm_mi","This domain is likely associated with NVIDIA's partner program, which could involve marketing, sales, and support services for partners.",[960,961,962],{"_76":905,"_431":636,"_432":30,"_169":468},{"_76":964,"_431":636,"_432":84,"_169":468},{"_76":963,"_431":636,"_432":30,"_169":468},"AWSALBCORS","JSESSIONID",[966],{"_76":944,"_431":459,"_432":30,"_169":967},"This is a CloudFoundry cookie",[969],{"_76":970,"_431":641,"_432":30,"_169":645},"logged_in","hcaptcha.com",[973],{"_76":944,"_431":971,"_432":30,"_169":967},"ferndocs.com",[976],{"_76":944,"_431":974,"_432":30,"_169":977},"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.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.","tracking.intentsify.io",[980],{"_76":981,"_431":978,"_432":30,"_169":982},"userId","Used to track usage of the embedded video player.","static.reo.dev",[985],{"_76":986,"_431":983,"_432":30,"_169":987},"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",[990],{"_76":991,"_431":988,"_432":30,"_169":992},"dd_cookie_test_","dd_cookie_test",[994],{"_76":944,"_431":482,"_432":30,"_169":967},[996],{"_76":944,"_431":487,"_432":30,"_169":967},"upload.wikimedia.org",[999],{"_76":1000,"_431":997,"_432":30,"_169":1001},"WMF-Uniq","This domain is used by Wikimedia Foundation to host media files (images, videos, audio, etc.) for Wikipedia and other Wikimedia websites.","supabase.co",[1004],{"_76":944,"_431":1002,"_432":30,"_169":967},[1006],{"_76":1007,"_431":495,"_432":30,"_169":501},"VISITOR_PRIVACY_METADATA","dosa-prd.azurewebsites.net",[1010],{"_76":1011,"_431":1008,"_432":30,"_169":1012},"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",[1015],{"_76":944,"_431":1013,"_432":30,"_169":967},"cdn.readme.io",[1018],{"_76":944,"_431":1016,"_432":30,"_169":967},"queue-it.net",[1021,1022,1023,1024,1025],{"_76":1030,"_431":1019,"_432":30,"_169":170},{"_76":1029,"_431":1019,"_432":30,"_169":170},{"_76":1028,"_431":1019,"_432":30,"_169":170},{"_76":1027,"_431":1019,"_432":30,"_169":170},{"_76":1026,"_431":1019,"_432":30,"_169":170},"Queue-it-{queueId}","Queue-it-{dmonvidia }________{eventId}","Queue-it-token","queueitsoftblock_{queueId}","Queue-it-{nvidia}________{eventId}","x.com",[1033],{"_76":944,"_431":1031,"_432":30,"_169":945},"app-sj14.marketo.com",[1036],{"_76":944,"_431":1034,"_432":30,"_169":967},"api.hcaptcha.com",[1039,1040],{"_76":1042,"_431":1037,"_432":30,"_169":468},{"_76":935,"_431":1037,"_432":30,"_169":1041},"This domain is associated with hCaptcha, a service that provides CAPTCHA and reCAPTCHA alternatives for user verification and bot protection.","hmt_id","vimeo.com",[1045],{"_76":944,"_431":1043,"_432":30,"_169":967},{"_76":1144,"_431":569,"_432":30,"_169":1232},{"_76":1230,"_431":688,"_432":84,"_169":1231},{"_76":903,"_431":1194,"_432":84,"_169":904},{"_76":1220,"_431":584,"_432":84,"_169":170},{"_76":1228,"_431":569,"_432":30,"_169":1229},{"_76":952,"_431":1215,"_432":30,"_169":1156},{"_76":1226,"_431":695,"_432":84,"_169":1227},{"_76":1162,"_431":1223,"_432":84,"_169":1164},{"_76":950,"_431":1155,"_432":30,"_169":1156},{"_76":918,"_431":1223,"_432":30,"_169":919},{"_76":1224,"_431":569,"_432":84,"_169":1225},{"_76":903,"_431":1203,"_432":84,"_169":904},{"_76":920,"_431":1223,"_432":30,"_169":921},{"_76":1221,"_431":569,"_432":30,"_169":1222},{"_76":950,"_431":1140,"_432":30,"_169":1156},{"_76":1150,"_431":1145,"_432":30,"_169":1152},{"_76":1220,"_431":569,"_432":84,"_169":1167},{"_76":963,"_431":1151,"_432":30,"_169":1165},{"_76":905,"_431":1151,"_432":30,"_169":1170},{"_76":918,"_431":1161,"_432":30,"_169":919},{"_76":1218,"_431":691,"_432":30,"_169":1219},{"_76":1150,"_431":1177,"_432":30,"_169":1152},{"_76":905,"_431":720,"_432":30,"_169":1170},{"_76":1216,"_431":569,"_432":30,"_169":1217},{"_76":957,"_431":720,"_432":30,"_169":1175},{"_76":1188,"_431":605,"_432":84,"_169":170},{"_76":950,"_431":1215,"_432":30,"_169":1156},{"_76":1213,"_431":1155,"_432":84,"_169":1214},{"_76":920,"_431":1177,"_432":30,"_169":921},{"_76":1178,"_431":569,"_432":30,"_169":1147},{"_76":1211,"_431":569,"_432":30,"_169":1212},{"_76":1150,"_431":569,"_432":30,"_169":1152},{"_76":1210,"_431":581,"_432":30,"_169":170},{"_76":957,"_431":569,"_432":30,"_169":1175},{"_76":905,"_431":1140,"_432":30,"_169":906},{"_76":1178,"_431":1148,"_432":30,"_169":1147},{"_76":1208,"_431":825,"_432":84,"_169":1209},{"_76":1206,"_431":569,"_432":84,"_169":1207},{"_76":1150,"_431":605,"_432":30,"_169":1152},{"_76":1205,"_431":569,"_432":30,"_169":170},{"_76":1144,"_431":1151,"_432":30,"_169":1146},{"_76":1204,"_431":581,"_432":30,"_169":170},{"_76":1193,"_431":1203,"_432":84,"_169":1195},{"_76":1201,"_431":605,"_432":30,"_169":1202},{"_76":1199,"_431":584,"_432":30,"_169":1200},{"_76":1197,"_431":569,"_432":30,"_169":1198},{"_76":1196,"_431":575,"_432":30,"_169":170},{"_76":1193,"_431":1194,"_432":84,"_169":1195},{"_76":1191,"_431":1161,"_432":84,"_169":1192},{"_76":1189,"_431":569,"_432":30,"_169":1190},{"_76":1188,"_431":572,"_432":84,"_169":170},{"_76":920,"_431":1163,"_432":30,"_169":921},{"_76":956,"_431":1179,"_432":30,"_169":1147},{"_76":1153,"_431":572,"_432":30,"_169":1154},{"_76":1186,"_431":569,"_432":30,"_169":1187},{"_76":1184,"_431":569,"_432":30,"_169":1185},{"_76":1183,"_431":569,"_432":30,"_169":170},{"_76":905,"_431":1182,"_432":30,"_169":1170},{"_76":918,"_431":1177,"_432":30,"_169":919},{"_76":991,"_431":572,"_432":30,"_169":1181},{"_76":1178,"_431":720,"_432":30,"_169":1147},{"_76":944,"_431":688,"_432":30,"_169":1180},{"_76":1178,"_431":1179,"_432":30,"_169":1147},{"_76":1144,"_431":1177,"_432":30,"_169":1146},{"_76":1176,"_431":581,"_432":30,"_169":170},{"_76":957,"_431":1148,"_432":30,"_169":1175},{"_76":1174,"_431":569,"_432":30,"_169":1143},{"_76":1172,"_431":1161,"_432":30,"_169":1173},{"_76":950,"_431":1171,"_432":30,"_169":951},{"_76":905,"_431":702,"_432":30,"_169":1170},{"_76":1169,"_431":581,"_432":30,"_169":170},{"_76":1168,"_431":569,"_432":30,"_169":170},{"_76":1162,"_431":1161,"_432":84,"_169":1164},{"_76":956,"_431":720,"_432":30,"_169":1147},{"_76":1166,"_431":569,"_432":30,"_169":1167},{"_76":963,"_431":720,"_432":30,"_169":1165},{"_76":918,"_431":1163,"_432":30,"_169":919},{"_76":1162,"_431":1163,"_432":84,"_169":1164},{"_76":920,"_431":1161,"_432":30,"_169":921},{"_76":1159,"_431":1155,"_432":84,"_169":1160},{"_76":1157,"_431":706,"_432":30,"_169":1158},{"_76":952,"_431":1155,"_432":30,"_169":1156},{"_76":1153,"_431":569,"_432":30,"_169":1154},{"_76":1150,"_431":1151,"_432":30,"_169":1152},{"_76":1144,"_431":605,"_432":30,"_169":1146},{"_76":944,"_431":696,"_432":30,"_169":1149},{"_76":956,"_431":1148,"_432":30,"_169":1147},{"_76":956,"_431":569,"_432":30,"_169":1147},{"_76":1144,"_431":1145,"_432":30,"_169":1146},{"_76":1142,"_431":569,"_432":30,"_169":1143},{"_76":963,"_431":1140,"_432":30,"_169":1141},{"_76":1138,"_431":569,"_432":30,"_169":1139},"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.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","omniverse.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","programs.nvidia.com","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","buildapi.ngc.nvidia.com","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.","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","_pf_consent","This cookie name is associated with Command Alkon Incorporated which is used to store user consent status and expiration.","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","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.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","bm_ss","This cookie is used to help protect websites from automated bot traffic.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","c_code","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. 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.

",{"_863":16,"_723":55,"_616":55,"_401":55,"_391":55},"hasConsented","gpcApplied","showBanner","geolocation",{"_1247":1248,"_1249":1250},"country","US","state","OR","query","categories",[1270,1271,1272,1273,1274],"optionUrls",[1258,1259,1260,1261,1262,1263,1264,1265,1266,1267,1268,1269],"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/peoplenet/-","/orgs/nvidia/tao/models/trafficcamnet/-","/orgs/nvidia/riva/resources/riva_quickstart/-","/orgs/nvidia/-/helm-charts/gpu-operator/-","/orgs/nvidia/-/helm-charts/k8s-nim-operator/-",{"_218":1347,"_1276":1348,"_1278":55},{"_218":1312,"_1276":1313,"_1278":53},{"_218":219,"_1276":1298,"_1278":129},{"_218":1290,"_1276":1291,"_1278":1292},{"_218":1275,"_1276":1277,"_1278":1279},"helm-charts","items",[1280,1281],"startIndex",10,{"_218":1275,"_225":1287,"_163":164,"_165":-7,"_220":1288,"_228":1284,"_254":1289,"_1286":1268},{"_218":1275,"_225":1282,"_163":164,"_165":-7,"_220":1283,"_228":1284,"_254":1285,"_1286":1269},"NVIDIA NIM Operator","k8s-nim-operator","NVIDIA",[],"url","GPU Operator","gpu-operator",[],"resources",[1293],9,{"_218":1290,"_225":1294,"_163":164,"_165":1295,"_220":1296,"_228":1284,"_254":1297,"_1286":1267},"Riva Skills Quick Start","riva","riva_quickstart",[],[1299,1300,1301],{"_218":219,"_225":1308,"_163":164,"_165":1309,"_220":1310,"_228":1284,"_254":1311,"_1286":1264},{"_218":219,"_225":1305,"_163":164,"_165":166,"_220":1306,"_228":1284,"_254":1307,"_1286":1265},{"_218":219,"_225":1302,"_163":164,"_165":166,"_220":1303,"_228":1284,"_254":1304,"_1286":1266},"TrafficCamNet","trafficcamnet",[],"PeopleNet","peoplenet",[],"StyleGAN3 pretrained models","research","stylegan3",[],"collections",[1314,1315,1316],{"_218":1312,"_225":1344,"_163":164,"_165":1318,"_220":1345,"_228":1284,"_254":1346,"_1286":1261},{"_218":1312,"_225":1341,"_163":164,"_165":-7,"_220":1342,"_228":1284,"_254":1343,"_1286":1262},{"_218":1312,"_225":1317,"_163":164,"_165":1318,"_220":1319,"_228":1284,"_254":1320,"_1286":1263},"Omniverse Kit App Streaming","omniverse","kit-appstreaming-collection",[1321,1322,1323],{"_76":1335,"_225":1336,"_222":1337,"_232":1338,"_1328":1339},{"_76":1318,"_225":1331,"_222":1332,"_232":1327,"_1328":1333},{"_76":1324,"_225":1325,"_222":1326,"_232":1327,"_1328":1329},"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",[1330],"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.",[1330,1334],"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",[1330,1334,1340],"NGC_CONSOLE","DeepStream SDK","deepstream_sdk",[],"Omniverse Kit (FB)","kit",[1321,1322,1323],"containers",[1349,1350,1351],{"_218":1347,"_225":1363,"_163":1356,"_165":1364,"_220":1365,"_228":1284,"_254":1366,"_1286":1258},{"_218":1347,"_225":1355,"_163":1356,"_165":164,"_220":1357,"_228":1284,"_254":1358,"_1286":1259},{"_218":1347,"_225":1352,"_163":164,"_165":-7,"_220":1353,"_228":1284,"_254":1354,"_1286":1260},"PyTorch","pytorch",[],"Llama-3.1-Nemotron-70B-Instruct","nim","llama-3.1-nemotron-70b-instruct",[1359,1321],{"_76":1360,"_225":1325,"_222":1326,"_232":1361,"_1328":1362},"nim-dev","https://assets.ngc.nvidia.com/products/catalog/images/product-nim-dev.jpg",[1330],"DeepSeek-R1","deepseek-ai","deepseek-r1",[1321,1359],"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",{"_1396":1397,"_1398":339,"_1399":30,"_1400":1401,"_1276":1402,"_1403":1404,"_1405":1397,"_1406":30,"_1407":30,"_1408":30,"_1409":1410},"appName","ngc-catalog","appOrigin","hideOnPrivateOrg","icon","ObjectsModules2",[1411,1412,1413,1414,1415,1416,1417],"menuIcon","editor-swatches","productName","requiresAllScopes","requiresLogin","requiresOrgLevelScopesOnly","title","Catalog",{"_1409":1439,"_46":1440,"_1403":1441},{"_1432":1433,"_1409":1434,"_46":1435,"_1405":1335},{"_1409":1430,"_46":1431,"_1403":1404},{"_1409":1427,"_46":1428,"_1403":1429},{"_1409":1424,"_46":1425,"_1403":1426},{"_1409":1421,"_46":1422,"_1403":1423},{"_1409":1418,"_46":1419,"_1403":1420},"Resources","/resources","misc-library","Models","/models","misc-neural-network","Helm Charts","/helm-charts","misc-ship-wheel","Containers","/containers","3d-cube","Collections","/collections","access",[1436],"Enterprise Catalog","/enterprise",[1437,1438],"NVAIE","READ","Explore Catalog","/","common-home",{"_110":111,"_112":113,"_114":115,"_116":117,"_118":119,"_120":121},{"_109":30}]