EmotionNet Model Card

Model Overview

The model described in this card is a classification network, which aims to classify human emotion into 6 categories.

- Neutral\n- Happy\n- Surprise\n- Squint\n- Disgust\n- Scream\n

Model Architecture

This is a classification model with five fully connected layers.

Training Algorithm

This model was trained using the EmotionNet entrypoint in TAO. The training algorithm optimizes the network to minimize the categorical cross entropy loss for the emotion classes.

Training Data

EmotionNet v1.0 model was trained on MultiPie dataset with more than 750K images. The training dataset consists of images taken from cameras mounted at varied heights and angles. It contains 337 subjects, imaged under 15 view points and 19 illumination conditions in up to four recording sessions. The facial landmarks labels were acquired from NVIDIA data factory team and applied to training. All data has been labeled including subjects with both profile and non-profile faces.

Multi-PIE dataset\nThe information to purchase and license Multi-PIE dataset can be found here

Training Data Ground-truth Labeling Guidelines

The training dataset is created by labeling ground-truth landmarks by human labellers. Please refer to the FPENet Model Card for the\ninstructions of landmarks labeling.

During the data collection, subjects are asked to perform posed emotion. The emotion labels are obtained during collection.

Performance

Evaluation Data

Dataset

The inference performance of EmotionNet model was measured against 71020 MultiPie images across a variety of subjects, illuminitation conditions, camera heights and camera angles.

Methodology and KPI

The KPI results for EmotionNet landmarks_v1 model are reported in the table below. Model is evaluated based on Precision, Recall, and f_scroe.

================  ===========  ========  =========  ============\ncontent             precision    recall    f_score    numsamples\n================  ===========  ========  =========  ============\ndisgust                0.8156   0.73016    0.77052          6300\nhappy                 0.81961   0.93027    0.87144         13480\nneutral               0.95134   0.94047    0.94587         33260\nscream                0.98347   0.99443    0.98892          7180\nsquint                0.76419   0.64815     0.7014          5400\nsurprise              0.93284   0.92593    0.92937          5400\nAverage               0.87784   0.86157    0.86792         71020\nWeighted_average      0.90191     0.902     0.9007         71020\n================  ===========  ========  =========  ============\n

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	1190
NX	FP16	1	4016
Xavier	FP16	1	5988
T4	FP16	1	19644

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.

Primary use case for this model is to detect human emotion. The model can be used to detect human emotion from photos and videos by using appropriate video or image decoding and pre-processing. The model takes in facial landmarks as input and provide emotion classes as output.

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

68 points (X, Y) of Human facial landmarks (1 x 136 x 1)

The training pipeline can accept more input points, but the pre-train model is trained with 68 points input.

Output

Category labels (emotion) of each subject in the input image.

Instructions to use the model with TAO

In order to use this model as a pretrained weights for transfer learning, please use the below mentioned template for the model component of the experiment spec file to train a EmotionNet model.

model:\n  __class_name__: EmotionNetModel\n  model_parameters:\n    use_batch_norm: True\n    data_format: channels_first\n    regularization_type: l2\n    regularization_factor: 0.0015\n    bias_regularizer: null\n    use_landmarks_input: True\n    activation_type: 'relu'\n    dropout_rate: 0.3\n    num_class: 6\n

Instructions to deploy this model with DeepStream

To create the entire end-to-end video analytic application, deploy this model with DeepStream.

Limitations

Spontaneous Emotion

NVIDIA EmotionNet model were trained with MultiPie datasets that have six emotion classes. The subjects in the datasets were performing pose emotion. Therefore it will not be able to detect emotion classes that are not belong to these classes or spontaneous emotions.

Unstable landmarks

NVIDIA EmotionNet model (landmarks_v1.0) does not give good results on detectioning emotion, if the landmarks are not stable (generally, this happens when the face are occluded).

Model versions

trainable_v1.0 - Pre-trained model that is intended for training.
deployabale_v1.0 - Deployment models that is intended to run on the inference pipeline.

References

Citations

Gross, R., Iain M., Jeffrey C., Takeo K., and Simon B.. \"Multi-pie.\" Image and Vision Computing 28, no. 5 (2010): 807-813.
Lucey, Patrick, Jeffrey F. Cohn, Takeo Kanade, Jason Saragih, Zara Ambadar, and Iain Matthews. \"The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression.\" In 2010 ieee computer society conference on computer vision and pattern recognition-workshops, pp. 94-101. IEEE, 2010.

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 model, you accept the terms and conditions of these licenses.

Ethical Considerations

NVIDIA EmotionNet model detects emotion categories. However, no additional information such as race, gender, and skin type about the faces is inferred.

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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The training algorithm optimizes the network to minimize the categorical cross entropy loss for the emotion classes.\n\n### Training Data \n\nEmotionNet v1.0 model was trained on MultiPie dataset with more than 750K images. The training dataset consists of images taken from cameras mounted at varied heights and angles. It contains 337 subjects, imaged under 15 view points and 19 illumination conditions in up to four recording sessions. The facial landmarks labels were acquired from NVIDIA data factory team and applied to training. All data has been labeled including subjects with both profile and non-profile faces.\n\n[Multi-PIE dataset](http://www.cs.cmu.edu/afs/cs/project/PIE/MultiPie/Multi-Pie/Home.html)\nThe information to purchase and license Multi-PIE dataset can be found [here](https://cmu.flintbox.com/#technologies/67027840-27d5-4570-86dd-ad4715ef3c09)\n\n#### Training Data Ground-truth Labeling Guidelines \n\nThe training dataset is created by labeling ground-truth landmarks by human labellers. Please refer to the FPENet Model Card for the\ninstructions of landmarks labeling.\n\nDuring the data collection, subjects are asked to perform posed emotion. The emotion labels are obtained during collection.\n\n## Performance\n\n### Evaluation Data \n\n#### Dataset \n\nThe inference performance of EmotionNet model was measured against 71020 MultiPie images across a variety of subjects, illuminitation conditions, camera heights and camera angles.\n\n#### Methodology and KPI \n\nThe KPI results for EmotionNet landmarks_v1 model are reported in the table below. Model is evaluated based on Precision, Recall, and f_scroe.\n\n```\n================ =========== ======== ========= ============\ncontent precision recall f_score numsamples\n================ =========== ======== ========= ============\ndisgust 0.8156 0.73016 0.77052 6300\nhappy 0.81961 0.93027 0.87144 13480\nneutral 0.95134 0.94047 0.94587 33260\nscream 0.98347 0.99443 0.98892 7180\nsquint 0.76419 0.64815 0.7014 5400\nsurprise 0.93284 0.92593 0.92937 5400\nAverage 0.87784 0.86157 0.86792 71020\nWeighted_average 0.90191 0.902 0.9007 71020\n================ =========== ======== ========= ============\n```\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| 1190 |\n| NX | FP16 | 1 | 4016 |\n| Xavier | FP16 | 1 | 5988 |\n| T4 | FP16 | 1 | 19644 |\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\nPrimary use case for this model is to detect human emotion. The model can be used to detect human emotion from photos and videos by using appropriate video or image decoding and pre-processing. The model takes in facial landmarks as input and provide emotion classes as output.\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.\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\n[68 points](https://www.studytonight.com/post/dlib-68-points-face-landmark-detection-with-opencv-and-python) (X, Y) of Human facial landmarks (1 x 136 x 1)\n\nThe training pipeline can accept more input points, but the pre-train model is trained with 68 points input.\n\n### Output\n\nCategory labels (emotion) of each subject in the input image.\n\n### Instructions to use the model with TAO \n\nIn order to use this model as a pretrained weights for transfer learning, please use the below mentioned template for the `model` component of the experiment spec file to train a EmotionNet model.\n\n```py\nmodel:\n __class_name__: EmotionNetModel\n model_parameters:\n use_batch_norm: True\n data_format: channels_first\n regularization_type: l2\n regularization_factor: 0.0015\n bias_regularizer: null\n use_landmarks_input: True\n activation_type: 'relu'\n dropout_rate: 0.3\n num_class: 6\n```\n\n### Instructions to deploy this model with DeepStream \n\nTo create the entire end-to-end video analytic application, deploy this model with [DeepStream](https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps).\n\n## Limitations \n\n**Spontaneous Emotion**\n\nNVIDIA EmotionNet model were trained with MultiPie datasets that have six emotion classes. The subjects in the datasets were performing pose emotion. Therefore it will not be able to detect emotion classes that are not belong to these classes or spontaneous emotions.\n\n**Unstable landmarks**\n\nNVIDIA EmotionNet model (landmarks_v1.0) does not give good results on detectioning emotion, if the landmarks are not stable (generally, this happens when the face are occluded).\n\n## Model versions\n\n- **trainable_v1.0** - Pre-trained model that is intended for training.\n- **deployabale_v1.0** - Deployment models that is intended to run on the inference pipeline.\n\n## References\n\n### Citations \n\n- Gross, R., Iain M., Jeffrey C., Takeo K., and Simon B.. \"Multi-pie.\" Image and Vision Computing 28, no. 5 (2010): 807-813.\n- Lucey, Patrick, Jeffrey F. Cohn, Takeo Kanade, Jason Saragih, Zara Ambadar, and Iain Matthews. \"The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression.\" In 2010 ieee computer society conference on computer vision and pattern recognition-workshops, pp. 94-101. IEEE, 2010.\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 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\nNVIDIA EmotionNet model detects emotion categories. However, no additional information such as race, gender, and skin type about the faces is inferred.\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","EmotionNet",["D",1732745273527],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/emotionnet","rawImageUrl","https://raw.githubusercontent.com/kbojo/images/master/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",4588024,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[295],"displayLabels",[250,251,252,253,254,255,256,257,258,259],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_217":292,"_261":293,"_262":294,"_264":265},{"_217":290,"_261":290,"_262":291,"_264":265},{"_217":287,"_261":288,"_262":289,"_264":265},{"_217":285,"_261":285,"_262":286,"_264":265},{"_217":282,"_261":283,"_262":284,"_264":265},{"_217":278,"_261":279,"_262":280,"_264":265},{"_217":276,"_261":276,"_262":277,"_264":265},{"_217":272,"_261":273,"_262":274,"_264":265},{"_217":268,"_261":269,"_262":270,"_264":265},{"_217":260,"_261":260,"_262":263,"_264":265},"TAO Toolkit","value","filter",{"_266":267,"_261":260},"category","label","field","framework","Retail","indus_retail",{"_266":271,"_261":269},"industry","NVIDIA AI","soln_nvidia_ai",{"_266":275,"_261":273},"technology","NSPECT-1HSG-3OU3",{"_266":265,"_261":276},"Metropolis","pltfm_metropolis",{"_266":281,"_261":279},"platform","Healthcare","indus_healthcare",{"_266":271,"_261":283},"Emotion Recognition",{"_266":265,"_261":285},"DeepStream","pltfm_deepstream",{"_266":281,"_261":288},"CV",{"_266":265,"_261":290},"Computer Vision","soln_computer_vision",{"_266":275,"_261":293},{"_217":296,"_261":296,"_262":297,"_264":298},"NVIDIA AI Enterprise Supported",{"_266":265,"_261":296},"feature",{"_114":1472,"_116":1473},"ngcApps",[1425],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1406":1407,"_1408":1409,"_1410":1411,"_1412":1413,"_1414":310,"_145":334,"_1415":1416,"_1417":56,"_1418":93,"_1419":93,"_1420":1421,"_1422":93,"_1423":1424},"amplitude",{"_1403":1404,"_1006":-7,"_1405":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1397":1398,"_1399":1400,"_1401":1402},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1281":174,"_1282":1283,"_1284":1285,"_1286":1287},"announcements",[],"consentState",{"_372":1271,"_1272":93,"_1273":31,"_1274":31,"_1275":1276},"consentConfig",{"_354":355,"_356":357,"_358":359,"_360":361,"_362":363,"_364":365,"_366":367,"_368":369,"_370":371,"_372":373},"showContactAdmin",1784050082752,[],[],[348,349,350,351,352,353],{"_45":60,"_47":71,"_49":109,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":109,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":67,"_49":109,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":109,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":109,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":109,"_51":-5,"_53":-5,"_55":56},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",[522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556],[395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411],{"_412":518,"_217":518,"_173":174,"_386":519},{"_412":513,"_217":513,"_173":174,"_386":514},{"_412":509,"_217":509,"_173":174,"_386":510},{"_412":504,"_217":504,"_173":174,"_386":505},{"_412":499,"_217":499,"_173":174,"_386":500},{"_412":487,"_217":487,"_173":174,"_386":488},{"_412":475,"_217":475,"_173":174,"_386":476},{"_412":470,"_217":470,"_173":174,"_386":471},{"_412":459,"_217":459,"_173":174,"_386":460},{"_412":454,"_217":454,"_173":174,"_386":455},{"_412":449,"_217":449,"_173":174,"_386":450},{"_412":444,"_217":444,"_173":174,"_386":445},{"_412":439,"_217":439,"_173":174,"_386":440},{"_412":434,"_217":434,"_173":174,"_386":435},{"_412":429,"_217":429,"_173":174,"_386":430},{"_412":422,"_217":422,"_173":174,"_386":423},{"_412":413,"_217":413,"_173":174,"_386":414},"hostName","amazon-adsystem.com",[415,416],{"_80":421,"_418":413,"_419":31,"_173":420},{"_80":417,"_418":413,"_419":31,"_173":420},"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",[424,425],{"_80":428,"_418":422,"_419":31,"_173":427},{"_80":426,"_418":422,"_419":31,"_173":427},"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",[431],{"_80":432,"_418":429,"_419":31,"_173":433},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[436],{"_80":437,"_418":434,"_419":31,"_173":438},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","If you know anything about this host and its uses please add your contributions here. ","tiktok.com",[441],{"_80":442,"_418":439,"_419":31,"_173":443},"_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",[446],{"_80":447,"_418":444,"_419":31,"_173":448},"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",[451],{"_80":452,"_418":449,"_419":31,"_173":453},"sp","Testing","google.com",[456],{"_80":457,"_418":454,"_419":31,"_173":458},"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.","tags.srv.stackadapt.com",[461,462,463,464],{"_80":469,"_418":459,"_419":31,"_173":466},{"_80":468,"_418":459,"_419":31,"_173":466},{"_80":467,"_418":459,"_419":31,"_173":466},{"_80":465,"_418":459,"_419":31,"_173":466},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[472],{"_80":473,"_418":470,"_419":31,"_173":474},"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",[477,478,479,480],{"_80":486,"_418":475,"_419":31,"_173":482},{"_80":484,"_418":475,"_419":31,"_173":485},{"_80":483,"_418":475,"_419":31,"_173":482},{"_80":481,"_418":475,"_419":31,"_173":482},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[489,490,491,492],{"_80":498,"_418":487,"_419":31,"_173":496},{"_80":497,"_418":487,"_419":31,"_173":496},{"_80":495,"_418":487,"_419":31,"_173":496},{"_80":493,"_418":487,"_419":31,"_173":494},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[501],{"_80":502,"_418":499,"_419":31,"_173":503},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[506],{"_80":507,"_418":504,"_419":31,"_173":508},"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",[511],{"_80":512,"_418":509,"_419":31,"_173":453},"R","6sc.co",[515],{"_80":516,"_418":513,"_419":31,"_173":517},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665],[610,611,612,613,614],{"_412":504,"_217":504,"_173":174,"_386":636},{"_412":470,"_217":470,"_173":174,"_386":631},{"_412":624,"_217":624,"_173":174,"_386":625},{"_412":620,"_217":620,"_173":174,"_386":621},{"_412":615,"_217":615,"_173":174,"_386":616},"nvcrm.my.site.com",[617],{"_80":618,"_418":615,"_419":31,"_173":619},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[622],{"_80":623,"_418":620,"_419":31,"_173":453},"edge","github.com",[626,627],{"_80":630,"_418":624,"_419":31,"_173":629},{"_80":628,"_418":624,"_419":31,"_173":629},"logged_in","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.modal.cuda","This cookie name suggests it is related to a modal popup functionality on NVIDIA's website, possibly to remember user preferences or interactions with the modal.","readme_library","docs.api.nvidia.com","_im_ses.1006927","geo_locator","nv.modal.deepstream","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.","_im_id.1006927","_hjUserAttributesHash","docs.nvidia.com","This cookie name is associated with Hotjar which is used to know when any user attribute has changed and needs to be updated.","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","nv.modal.cudnn","This cookie name suggests it is related to modal popup handling or user interface preferences on NVIDIA's website, specifically for the cuDNN library section.","readme_language","nv.modal.hpc","This cookie name suggests it is related to a modal pop-up on a website, possibly to control the display of a high-priority cookie notice or other important information to the user.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","_biz_uid","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.\n","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","_biz_nA","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website 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