Facial Landmark Estimator (FPENet) Model Card

Model Overview

The FPENet model described in this card is a facial keypoints estimator network, which aims to predict the (x,y) location of keypoints for a given input face image. FPEnet is generally used in conjuction with a face detector and the output is commonly used for face alignment, head pose estimation, emotion detection, eye blink detection, gaze estimation, among others.

This model predicts 68, 80 or 104 keypoints for a given face- Chin: 1-17, Eyebrows: 18-27, Nose: 28-36, Eyes: 37-48, Mouth: 49-61, Inner Lips: 62-68, Pupil: 69-76, Ears: 77-80, additional eye landmarks: 81-104. It can also handle visible or occluded flag for each keypoint. An example of the kaypoints is shown as follows:

Model Architecture

This is a classification model with a Recombinator network backbone. Recombinator networks are a family of CNN architectures that are suited for fine grained pixel level predictions (as oppose to image level prediction like classification). The model recombines the layer inputs such that convolutional layers in the finer branches get inputs from both coarse and fine layers.

Training Algorithmn

This model was trained using the FPENet entrypoint in TAO. The training algorithm optimizes the network to minimize the manhattan distance (L1), squared euclidean (L2) or the Wing Loss over the keypoints. Individual face regions can be weighted based on- the 'eyes', the 'mouth', the 'pupil' and the rest of the 'face'.

Training Data and Ground-truth Labeling Guidelines

A pre-trained (trainable) model is available, trained on a combination of NVIDIA internal dataset and Multi-PIE dataset. NVIDIA internal data has approximately 500k images and Multipie has 750k images.

The ground truth dataset is created by labeling ground-truth facial keypoints by human labellers.

If you are looking to re-train with your own dataset, please follow the guideline below.

Label the keypoints in the correct order as accuractely as possible. The human labeler would be able to zoom in to a face region to correctly localize the keypoint.
For keypoints that are not easily distinguishable such as chin or nose, the best estimate should be made by the human labeler. Some keypoints are easily distinguishable such as mouth corners or eye corners.
Label a keypoint as \"occluded\" if the keypoint is not visible due to an external object or due to extreme head pose angles. A keypoint is considered occluded when the keypoint is in the image but not visible.
To reduce discrepency in labeling between multiple human labelers, the same keypoint ordering and instructions should be used across labelers. An independent human labeler may be used to test the quality of the annotated landmarks and potential corrections.

Face bounding boxes labeling:

Face bounding boxes should be as tight as possible.
Label each face bounding box with an occlusion level ranging from 0 to 9. 0 means the face is fully visible and 9 means the face is 90% or more occluded. For training, only faces with occlusion level 0-5 are considered.
The datasets consist of webcam images so truncation is rarely seen. If faces are at the edge of the frame with visibility less than 60% due to truncation, this image is dropped from the dataset.

The Sloth and Label-Studio tools have been utilized for labeling.

Performance

Evaluation Dataset

The evaluation is done on the Multi-PIE dataset\nUsers IDs that are used for KPI- 342 079 164 250 343 080 165 251 344 081 166 252 345 082 167 253 346 083 168 254 084 169 255

Methodology and KPI accuracy

The region keypoint pixel error is the mean euclidean error in pixel location prediction as compared to the ground truth. We bucketize and average the error per face region (eyes, mouth, chin, etc.).\nMetric- Region keypoints pixel error

All keypoints: 6.1
Eyes region: 3.33
Mouth region: 2.96

Average latency

Batch Size = 1 at FP16
T4 - 0.33 ms
Jetson AGX - 0.84 ms
Jetson NX - 1.3 ms\n(all measurements using trtexec on the specific hardware)

Real-time Inference Performance

The inference uses FP16 precision. The inference performance runs with trtexec on Jetson Nano, AGX Xavier, Xavier NX and NVIDIA T4 GPU. The Jetson devices run at Max-N configuration for maximum system performance. The end-to-end performance with streaming video data might slightly vary depending on use cases of applications.

\n\n\n\n\n\n\n\n\n\n\n\n\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\">

Device	Precision	Batch_size	FPS
Nano	FP16	1	115
NX	FP16	1	483
Xavier	FP16	1	1015
T4	FP16	1	2489

How to use this model

This model needs to be used with NVIDIA Hardware and Software. For Hardware, the model can run on any NVIDIA GPU including NVIDIA Jetson devices. This model can only be used with Train Adapt Optimize (TAO) Toolkit, DeepStream 6.0 or TensorRT.

There are two flavors of the model:

trainable
deployable

The trainable model is intended for training using TAO Toolkit and the user's own dataset. This can provide high fidelity models that are adapted to the use case. The Jupyter notebook available as a part of TAO container can be used to re-train.\nThe deployable model is intended for efficient deployment on the edge using DeepStream or TensorRT.\nThe trainable and deployable models are encrypted and will only operate with the following key:

Model load key: nvidia_tlt

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

Input

Images of 80 X 80 X 1

Output

N X 2 keypoint locations.\nN X 1 keypoint confidence.

N is the number of keypoints.

General purpose key point estimation

Besides predicting the 68, 80 points, this model can be finetuned to predict other number of facial points or general purpose key points with TAO toolkit above 22.04 version. Following is an example to enable 10 keypoints estimation by changing num_keypoints in the training specification file:

num_keypoints: 10\ndataloader:\n  ...\n  num_keypoints: 10\n  ...\n

Limitations

Some known limitations include relative increase in keypoint estimation error in extreme head pose (yaw > 60 degree) and occlusions.

Model versions:

trainable_v1.0 - Pre-trained model that is intended for training.
deployable_v1.0 - Deployment models that is intended to run on the inference pipeline.
deployable_v3.0 - Deployment models that is intended to run on the inference pipeline with int8 calibration.

References

Citations

\n
Honari, S., Molchanov, P., Tyree, S., Vincent, P., Pal, C., & Kautz, J. (2018). Improving landmark localization with semi-supervised learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1546-1555).
\n
\n
Feng, Z. H., Kittler, J., Awais, M., Huber, P., & Wu, X. J. (2018). Wing loss for robust facial landmark localisation with convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2235-2245).
\n

Using TAO Pre-trained Models

Get TAO Container
Get other Purpose-built models from NGC model registry:\n
- FaceNet
- FPENet
- GazeNet
- EmotionNet
- GestureNet
- HeartRateNet
- PeopleNet
- TrafficCamNet
- DashCamNet
- VehicleMakeNet
- VehicleTypeNet
\n

Technical blogs

Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - Part 1 | Part 2
Read the technical tutorial on how PeopleNet model can be trained with custom data using Transfer Learning Toolkit

License

License to use this model 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

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

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

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\n\n## Model Architecture \n\nThis is a classification model with a [Recombinator network](https://openaccess.thecvf.com/content_cvpr_2016/papers/Honari_Recombinator_Networks_Learning_CVPR_2016_paper.pdf) backbone. Recombinator networks are a family of CNN architectures that are suited for fine grained pixel level predictions (as oppose to image level prediction like classification). The model recombines the layer inputs such that convolutional layers in the finer branches get inputs from both coarse and fine layers.\n\n## Training Algorithmn \n\nThis model was trained using the FPENet entrypoint in TAO. The training algorithm optimizes the network to minimize the manhattan distance (L1), squared euclidean (L2) or the Wing Loss over the keypoints. Individual face regions can be weighted based on- the 'eyes', the 'mouth', the 'pupil' and the rest of the 'face'.\n\n### Training Data and Ground-truth Labeling Guidelines\n\nA pre-trained (`trainable`) model is available, trained on a combination of NVIDIA internal dataset and [Multi-PIE dataset](http://www.cs.cmu.edu/afs/cs/project/PIE/MultiPie/Multi-Pie/Home.html). NVIDIA internal data has approximately 500k images and Multipie has 750k images.\n\nThe ground truth dataset is created by labeling ground-truth facial keypoints by human labellers.\n\nIf you are looking to re-train with your own dataset, please follow the guideline below.\n\n- Label the keypoints in the correct order as accuractely as possible. The human labeler would be able to zoom in to a face region to correctly localize the keypoint.\n- For keypoints that are not easily distinguishable such as chin or nose, the best estimate should be made by the human labeler. Some keypoints are easily distinguishable such as mouth corners or eye corners.\n- Label a keypoint as \"occluded\" if the keypoint is not visible due to an external object or due to extreme head pose angles. A keypoint is considered occluded when the keypoint is in the image but not visible.\n- To reduce discrepency in labeling between multiple human labelers, the same keypoint ordering and instructions should be used across labelers. An independent human labeler may be used to test the quality of the annotated landmarks and potential corrections.\n\nFace bounding boxes labeling:\n\n- Face bounding boxes should be as tight as possible.\n- Label each face bounding box with an occlusion level ranging from 0 to 9. 0 means the face is fully visible and 9 means the face is 90% or more occluded. For training, only faces with occlusion level 0-5 are considered.\n- The datasets consist of webcam images so truncation is rarely seen. If faces are at the edge of the frame with visibility less than 60% due to truncation, this image is dropped from the dataset.\n\nThe [Sloth](https://github.com/cvhciKIT/sloth) and [Label-Studio](https://labelstud.io/) tools have been utilized for labeling.\n\n## Performance\n\n### Evaluation Dataset\nThe evaluation is done on the [Multi-PIE dataset](http://www.cs.cmu.edu/afs/cs/project/PIE/MultiPie/Multi-Pie/Home.html)\nUsers IDs that are used for KPI- 342 079 164 250 343 080 165 251 344 081 166 252 345 082 167 253 346 083 168 254 084 169 255\n\n### Methodology and KPI accuracy \n\nThe region keypoint pixel error is the mean euclidean error in pixel location prediction as compared to the ground truth. We bucketize and average the error per face region (eyes, mouth, chin, etc.).\nMetric- Region keypoints pixel error\n\n- All keypoints: 6.1\n- Eyes region: 3.33\n- Mouth region: 2.96\n\n### Average latency \n\n- Batch Size = 1 at FP16\n- T4 - 0.33 ms\n- Jetson AGX - 0.84 ms\n- Jetson NX - 1.3 ms\n(all measurements using `trtexec` on the specific hardware)\n\n### Real-time Inference Performance \n\nThe inference uses FP16 precision. The inference performance runs with [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/master/samples/opensource/trtexec) on Jetson Nano, AGX Xavier, Xavier NX and NVIDIA T4 GPU. The Jetson devices run at Max-N configuration for maximum system performance. The end-to-end performance with streaming video data might slightly vary depending on use cases of applications.\n\n| Device | Precision | Batch_size | FPS |\n| ---- | ---- | ---- | --- |\n| Nano | FP16 |1| 115 |\n| NX | FP16 | 1 | 483 |\n| Xavier | FP16 | 1 | 1015 |\n| T4 | FP16 | 1 | 2489 |\n\n## How to use this model \n\nThis model needs to be used with NVIDIA Hardware and Software. For Hardware, the model can run on any NVIDIA GPU including NVIDIA Jetson devices. This model can only be used with [Train Adapt Optimize (TAO) Toolkit](https://developer.nvidia.com/tao-toolkit), [DeepStream 6.0](https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps) or [TensorRT](https://developer.nvidia.com/tensorrt).\n\nThere are two flavors of the model:\n\n- trainable\n- deployable\n\nThe `trainable` model is intended for training using TAO Toolkit and the user's own dataset. This can provide high fidelity models that are adapted to the use case. The Jupyter notebook available as a part of [TAO container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit-tf) can be used to re-train.\nThe `deployable` model is intended for efficient deployment on the edge using DeepStream or [TensorRT](https://developer.nvidia.com/tensorrt).\nThe `trainable` and `deployable` models are encrypted and will only operate with the following key:\n\n- Model load key: `nvidia_tlt`\n\nPlease make sure to use this as the key for all TAO commands that require a model load key.\n\n### Input\n\nImages of 80 X 80 X 1\n\n### Output\n\nN X 2 keypoint locations.\nN X 1 keypoint confidence.\n\nN is the number of keypoints.\n\n### General purpose key point estimation\n\nBesides predicting the 68, 80 points, this model can be finetuned to predict other number of facial points or general purpose key points with TAO toolkit above 22.04 version. Following is an example to enable 10 keypoints estimation by changing `num_keypoints` in the training specification file:\n\n```yaml\nnum_keypoints: 10\ndataloader:\n ...\n num_keypoints: 10\n ...\n``` \n\n\n## Limitations \n\nSome known limitations include relative increase in keypoint estimation error in extreme head pose (yaw > 60 degree) and occlusions.\n\n## Model versions:\n- **trainable_v1.0** - Pre-trained model that is intended for training.\n- **deployable_v1.0** - Deployment models that is intended to run on the inference pipeline.\n- **deployable_v3.0** - Deployment models that is intended to run on the inference pipeline with int8 calibration.\n\n## References \n\n### Citations \n\n- Honari, S., Molchanov, P., Tyree, S., Vincent, P., Pal, C., & Kautz, J. (2018). Improving landmark localization with semi-supervised learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1546-1555).\n\n- Feng, Z. H., Kittler, J., Awais, M., Huber, P., & Wu, X. J. (2018). Wing loss for robust facial landmark localisation with convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2235-2245).\n\n## Using TAO Pre-trained Models \n\n- Get [TAO Container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit)\n- Get other Purpose-built models from NGC model registry:\n - [FaceNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:facenet)\n - [FPENet](https://ngc.nvidia.com/catalog/models/nvidia:tao:fpenet)\n - [GazeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:gazenet)\n - [EmotionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:emotionnet)\n - [GestureNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:gesturenet)\n - [HeartRateNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:heartratenet)\n - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)\n - [TrafficCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:trafficcamnet)\n - [DashCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:dashcamnet)\n - [VehicleMakeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehiclemakenet)\n - [VehicleTypeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehicletypenet)\n\n## Technical blogs \n\n- Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - [Part 1](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-1) | [Part 2](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-2)\n- Read the technical tutorial on how [PeopleNet model can be trained with custom data using Transfer Learning Toolkit](https://devblogs.nvidia.com/training-custom-pretrained-models-using-tlt/)\n\n## Suggested reading \n\n- More information on about TAO Toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/tao-toolkit)\n- [TAO documentation](https://docs.nvidia.com/tao/tao-toolkit/index.html)\n- Read the [TAO getting Started](https://docs.nvidia.com/tao/tao-toolkit/text/tao_quick_start_guide.html) guide and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html).\n- If you have any questions or feedback, please refer to the discussions on [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17)\n- Deploy your models for video analytics application using DeepStream. Learn more about [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk)\n- Deploy your models in [Riva](https://developer.nvidia.com/riva) for ConvAI use case.\n\n## License \n\nLicense to use this model 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 \n\nTraining and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies.\n\nNVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.","displayName","Facial Landmarks Estimation",["D",1732745288302],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/fpenet","rawImageUrl","https://raw.githubusercontent.com/kbojo/images/master/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[301],"displayLabels",[256,257,258,259,260,261,262,263,264,265],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_224":298,"_267":299,"_268":300,"_270":271},{"_224":296,"_267":296,"_268":297,"_270":271},{"_224":293,"_267":294,"_268":295,"_270":271},{"_224":291,"_267":291,"_268":292,"_270":271},{"_224":287,"_267":288,"_268":289,"_270":271},{"_224":285,"_267":285,"_268":286,"_270":271},{"_224":281,"_267":282,"_268":283,"_270":271},{"_224":278,"_267":279,"_268":280,"_270":271},{"_224":274,"_267":275,"_268":276,"_270":271},{"_224":266,"_267":266,"_268":269,"_270":271},"TAO Toolkit","value","filter",{"_272":273,"_267":266},"category","label","field","framework","Robotics","indus_robotics",{"_272":277,"_267":275},"industry","Retail","indus_retail",{"_272":277,"_267":279},"NVIDIA AI","soln_nvidia_ai",{"_272":284,"_267":282},"technology","NSPECT-O7BY-43C5",{"_272":271,"_267":285},"Metropolis","pltfm_metropolis",{"_272":290,"_267":288},"platform","Facial landmark estimation",{"_272":271,"_267":291},"DeepStream","pltfm_deepstream",{"_272":290,"_267":294},"CV",{"_272":271,"_267":296},"Computer Vision","soln_computer_vision",{"_272":284,"_267":299},{"_224":302,"_267":302,"_268":303,"_270":304},"NVIDIA AI Enterprise Supported",{"_272":271,"_267":302},"feature",{"_113":1282,"_115":1283},"ngcApps",[1235],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1216":1217,"_1218":1219,"_1220":1221,"_1222":1223,"_1224":316,"_145":340,"_1225":1226,"_1227":57,"_1228":93,"_1229":93,"_1230":1231,"_1232":93,"_1233":1234},"amplitude",{"_1213":1214,"_851":-7,"_1215":32},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1207":1208,"_1209":1210,"_1211":1212},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1091":176,"_1092":1093,"_1094":1095,"_1096":1097},"announcements",[],"consentState",{"_377":1081,"_1082":93,"_1083":32,"_1084":32,"_1085":1086},"consentConfig",{"_359":360,"_361":362,"_363":364,"_365":366,"_367":368,"_369":370,"_371":372,"_373":374,"_375":376,"_377":378},"showContactAdmin",1785219038892,[],[],[354,355,356,357,358],{"_46":60,"_48":71,"_50":68,"_52":-5,"_54":-5,"_56":57},{"_46":60,"_48":69,"_50":68,"_52":-5,"_54":-5,"_56":57},{"_46":60,"_48":61,"_50":68,"_52":-5,"_54":-5,"_56":57},{"_46":58,"_48":59,"_50":51,"_52":53,"_54":137,"_56":57},{"_46":47,"_48":49,"_50":68,"_52":-5,"_54":-5,"_56":57},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1073":1074,"_1075":1076,"_1077":1078,"_1079":1080},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[379,380,381,382,383],{"_384":763,"_80":764,"_175":765,"_388":93,"_389":18,"_391":766,"_393":767},{"_384":666,"_80":667,"_175":668,"_388":32,"_389":55,"_391":669,"_393":670},{"_384":574,"_80":575,"_175":576,"_388":32,"_389":137,"_391":577,"_393":578},{"_384":395,"_80":396,"_175":397,"_388":32,"_389":398,"_391":399,"_393":400},{"_384":385,"_80":386,"_175":387,"_388":32,"_389":390,"_391":392,"_393":394},"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,[516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537],[401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417],{"_418":511,"_224":511,"_175":176,"_391":512},{"_418":507,"_224":507,"_175":176,"_391":508},{"_418":502,"_224":502,"_175":176,"_391":503},{"_418":498,"_224":498,"_175":176,"_391":499},{"_418":493,"_224":493,"_175":176,"_391":494},{"_418":488,"_224":488,"_175":176,"_391":489},{"_418":480,"_224":480,"_175":176,"_391":481},{"_418":472,"_224":472,"_175":176,"_391":473},{"_418":467,"_224":467,"_175":176,"_391":468},{"_418":462,"_224":462,"_175":176,"_391":463},{"_418":457,"_224":457,"_175":176,"_391":458},{"_418":452,"_224":452,"_175":176,"_391":453},{"_418":447,"_224":447,"_175":176,"_391":448},{"_418":440,"_224":440,"_175":176,"_391":441},{"_418":435,"_224":435,"_175":176,"_391":436},{"_418":428,"_224":428,"_175":176,"_391":429},{"_418":419,"_224":419,"_175":176,"_391":420},"hostName","amazon-adsystem.com",[421,422],{"_80":427,"_424":419,"_425":32,"_175":426},{"_80":423,"_424":419,"_425":32,"_175":426},"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",[430,431],{"_80":434,"_424":428,"_425":32,"_175":433},{"_80":432,"_424":428,"_425":32,"_175":433},"IDE","This domain is owned by Doubleclick (Google). 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It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","linkedin.com",[469],{"_80":470,"_424":467,"_425":32,"_175":471},"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. 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. 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.",[608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632],[579,580,581,582,583],{"_418":493,"_224":493,"_175":176,"_391":603},{"_418":467,"_224":467,"_175":176,"_391":598},{"_418":593,"_224":593,"_175":176,"_391":594},{"_418":588,"_224":588,"_175":176,"_391":589},{"_418":584,"_224":584,"_175":176,"_391":585},"nvidia.partners",[586],{"_80":587,"_424":584,"_425":32,"_175":461},"edge","app.hushly.com",[590],{"_80":591,"_424":588,"_425":32,"_175":592},"*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",[595],{"_80":596,"_424":593,"_425":32,"_175":597},"_octo","This domain is owned by Github. 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We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv_country_code","developer.nvidia.com","This cookie is likely used to store the country code of the visitor to provide localized content and possibly for legal compliance with regional data privacy laws.","_gdxxxxxxxxxxxxx","nvidianews.nvidia.com","docs.api.nvidia.com","research.nvidia.com","readme_library","wcmmode","ai-podcast.nvidia.com","resources.nvidia.com","geo_locator","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","crowdsource.nvidia.com","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","readme_language","vid","This cookie name 'vid' is used for tracking the search request made by the user on the 'Find a practitioner' page.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","blogs.nvidia.com","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719],[671,672,673,674],{"_418":686,"_224":686,"_175":176,"_391":687},{"_418":681,"_224":681,"_175":176,"_391":682},{"_418":480,"_224":480,"_175":176,"_391":678},{"_418":593,"_224":593,"_175":176,"_391":675},[676],{"_80":677,"_424":593,"_425":93,"_175":597},"_gh_sess",[679],{"_80":680,"_424":480,"_425":93,"_175":487},"YSC","bizible.com",[683],{"_80":684,"_424":681,"_425":32,"_175":685},"_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",[688],{"_80":689,"_424":686,"_425":32,"_175":690},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_80":761,"_424":539,"_425":32,"_175":762},{"_80":746,"_424":760,"_425":93,"_175":747},{"_80":729,"_424":542,"_425":32,"_175":654},{"_80":729,"_424":642,"_425":32,"_175":654},{"_80":758,"_424":539,"_425":32,"_175":759},{"_80":746,"_424":545,"_425":93,"_175":747},{"_80":756,"_424":539,"_425":32,"_175":757},{"_80":746,"_424":542,"_425":93,"_175":747},{"_80":754,"_424":641,"_425":32,"_175":755},{"_80":746,"_424":663,"_425":93,"_175":747},{"_80":726,"_424":542,"_425":32,"_175":728},{"_80":752,"_424":539,"_425":93,"_175":753},{"_80":750,"_424":539,"_425":32,"_175":751},{"_80":748,"_424":539,"_425":93,"_175":749},{"_80":746,"_424":637,"_425":93,"_175":747},{"_80":744,"_424":539,"_425":32,"_175":745},{"_80":742,"_424":539,"_425":32,"_175":743},{"_80":740,"_424":539,"_425":93,"_175":741},{"_80":738,"_424":646,"_425":32,"_175":739},{"_80":726,"_424":637,"_425":32,"_175":728},{"_80":736,"_424":539,"_425":32,"_175":737},{"_80":734,"_424":539,"_425":93,"_175":735},{"_80":732,"_424":539,"_425":32,"_175":733},{"_80":730,"_424":539,"_425":32,"_175":731},{"_80":729,"_424":539,"_425":32,"_175":654},{"_80":726,"_424":727,"_425":32,"_175":728},{"_80":724,"_424":539,"_425":32,"_175":725},{"_80":722,"_424":539,"_425":93,"_175":723},{"_80":720,"_424":539,"_425":32,"_175":721},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_session_id","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. This ensures that behavior in subsequent visits to the same site will be attributed to the same user ID.","_hjHasCachedUserAttributes","This cookie name is associated with Hotjar which is used to know whether the data set in _hjUserAttributes Local Storage item is up to date or not.","s_ppv","This cookie is associated with Adobe Analytics site tracking and is used to measure a user's scroll activity to see how much of a page they view before moving to another page.","_gid","This cookie name is associated with Google Universal Analytics. This appears to be a new cookie and as of Spring 2017 no information is available from Google. It appears to store and update a unique value for each page visited.","_hjTLDTest","When the Hotjar script executes we try to determine the most generic cookie path we should use, instead of the page hostname. 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Name	Size	Updated	Actions
int8_calibration.txt	2.19 KB	March 1, 2025 UTC	curl wget Download
model.etlt	2.24 MB	March 1, 2025 UTC	curl wget Download