model_config {\n  num_layers: 18\n  pretrained_model_file: \"/path/to/the/model.tlt\"\n  use_batch_norm: true\n  objective_set {\n    bbox {\n      scale: 35.0\n      offset: 0.5\n    }\n    cov {\n    }\n  }\n  training_precision {\n    backend_floatx: FLOAT32\n  }\n  arch: \"resnet\"\n  all_projections: true\n}

","code-whmum3","

    ## Download Model:\n\n    mkdir -p $HOME/facedetectir && \\\n    wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/facedetectir/versions/pruned_v1.0/files/resnet18_facedetectir_pruned.etlt \\\n    -O $HOME/facedetectir/resnet18_facedetectir_pruned.etlt && \\\n    wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/facedetectir/versions/pruned_v1.0/files/facedetectir_int8.txt \\\n    -O $HOME/facedetectir/facedetectir_int8.txt\n\n    ## Run Application\n\n    xhost +\n    sudo docker run --gpus all -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v $HOME:/opt/nvidia/deepstream/deepstream-5.1/samples/models/tlt_pretrained_models \\\n    -w /opt/nvidia/deepstream/deepstream-5.1/samples/configs/tlt_pretrained_models nvcr.io/nvidia/deepstream:5.1-21.02-samples \\\n    deepstream-app -c deepstream_app_source1_labels_faceirnet.txt

","code-arlts","

      /opt/nvidia/deepstream/deepstream-5.1/samples/configs/tlt_pretrained_models

","code--t6bbae","

    deepstream_app_source1_faceirnet.txt - Main config file for DeepStream app\n    config_infer_primary_faceirnet.txt - File to configure inference settings\n    labels_faceirnet.txt - Label file with 1 class

","code-a9e803","

    tlt-model-key\n    tlt-encoded-model\n    labelfile-path\n    int8-calib-file\n    input-dims\n    num-detected-classes

","code-lekg9","

    deepstream-app -c deepstream_app_source1_faceirnet.txt

","code--hd0cl5","trtexec","code--1jfy99","unpruned","code--g78sna","pruned","code--cypw4g","384x240x3","code--kz6aay","24x15x4","code--kz6ab1","24x15x1","code--pgwoqv","tlt_encode","code--7g063s","model_config","code-g588xs","deepstream-app","code-tl995h","/opt/nvidia/deepstream","code--jphzby","tlt_pretrained_models","code-6hmsmm","config_infer_primary_faceirnet.txt","nvidia/tao/facedetectir","key","nvidia/tao/models/facedetectir","resourceId","facedetectir","shortDescription","1 class object detection network to detect faces in an image.","# FaceDetect-IR Model Card\n\n## Model Overview \n\nThe model described in this card detects one or more faces in the given image / video. Compared to the PeopleNet model, this model gives better results detecting large faces, such as faces in webcam images.\n\n## Model Architecture \n\nThe model is based on NVIDIA DetectNet_v2 detector with ResNet18 as a feature extractor. This architecture, also known as GridBox object detection, uses bounding-box regression on a uniform grid on the input image. Gridbox system divides an input image into a grid which predicts four normalized bounding-box parameters (xc, yc, w, h) and confidence value per output class.\n\nThe raw normalized bounding-box and confidence detections needs to be post-processed by a clustering algorithm such as DBSCAN or NMS to produce final bounding-box coordinates and category labels.\n\n## Training \n\nThis model was trained using the DetectNet_v2 entrypoint in TAO. The training algorithm optimizes the network to minimize the localization and confidence loss for the objects. The training is carried out in two phases. In the first phase, the network is trained with regularization to facilitate pruning. Following the first phase, we prune the network removing channels whose kernel norms are below the pruning threshold. In the second phase the pruned network is retrained. Regularization is not included during the second phase.\n\n## Training Data \n\nFaceDetectIR v1.0 model was trained on a proprietary dataset with more than 846K faces. The training dataset consists of images taken from cameras mounted at varied heights and angles, cameras of varied field-of view (FOV) and occlusions. While most training images are IR (gray) images, some color (RGB) images are added as seeded content to improve accuracy of the models.\n\n### Training Data Ground-truth Labeling Guidelines \n\nThe training dataset is created by labeling ground-truth bounding-boxes and categories by human labellers. Following guidelines were used while labelling the training data for NVIDIA FaceDetectIR model.\n\nFaceDetectIR project labelling guidelines\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\n## Performance\n\n### Evaluation Data \n\nThe inference performance of FaceIR v1.0 model was measured against 16559 proprietary images across a variety of environments, occlusion conditions, camera heights and camera angles.\n\n#### Methodology and KPI \n\nThe true positives, false positives, false negatives are calculated using intersection-over-union (IOU) criterion greater than 0.5. The KPI for the evaluation data are reported in the table below. Model is evaluated based on precision, recall and accuracy.\n\nThe key performance indicators (KPI) are calculated for IR images only. There are no color images in our evaluation set.\n\n| Model | | FaceDetectIR | |\n|---|---|---|---|\n| Content | Precision (in %) | Recall (in %) | Accuracy (in %) |\n|Evaluation set| 99.8 | 96.40 | 96.21 |\n\n### Real-time Inference Performance \n\nThe inference is run on the provided pruned model at INT8 precision. The inference performance is run using [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/master/samples/opensource/trtexec) on Jetson Nano, AGX Xavier, Xavier NX and NVIDIA T4 GPU. On the Jetson Nano FP16 inference is run. The Jetson devices are running at Max-N configuration for maximum GPU frequency. The performance shown here is the inference only performance. The end-to-end performance with streaming video data might slightly vary depending on other bottlenecks in the hardware and software.\n\n

\n\n## 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 SDK](https://developer.nvidia.com/deepstream-sdk) or [TensorRT](https://developer.nvidia.com/tensorrt).\n\nThe primary use case for this model is to detect faces from an IR (infrared) camera. The model can be used to detect faces from photos and videos by using appropriate video or image decoding and pre-processing. As a secondary use case the model can also be used to detect faces in RGB (color) images. However, this is not the main intended use for this model.\n\nThere are two flavors of the model:\n\n- unpruned\n- pruned\n\nThe `unpruned` 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.\n\nThe `pruned` model is intended for efficient deployment on the edge using DeepStream SDK or TensorRT. This model accepts `384x240x3` dimension input tensors and outputs `24x15x4` bbox coordinate tensor and `24x15x1` class confidence tensor. DeepStream provides a toolkit to create efficient video analytic pipelines to capture, decode, and pre-process the data before running inference. DeepStream will then post-process the output bbox coordinate tensor and class confidence tensors with NMS or DBScan clustering algorithm to create appropriate bounding boxes. The sample application and config file to run this model are provided in DeepStream SDK.\n\nThe `unpruned` and `pruned` models are encrypted and will only operate with the following key:\n\n- Model load key: `tlt_encode`\n\nPlease make sure to use this as the key for all TAO commands that require a model load key.\n\n### Input \n\nGray Image whose values in RGB channels are the same. 384 X 240 X 3 (W x H x C)\nChannel Ordering of the Input: NCHW, where N = Batch Size, C = number of channels (3), H = Height of images (240), W = Width of the images (384)\nInput scale: 1/255.0\nMean subtraction: None\n\n### Output \n\nCategory labels (faces) and bounding-box coordinates for each detected face in the input image.\n\n### Instructions to use unpruned model with TAO \n\nIn order, to use this model as a pretrained weights for transfer learning, please use the below mentioned snippet as template for the `model_config` component of the experiment spec file to train a DetectNet_v2 model. For more information on the experiment spec file, please refer to the [TAO Toolkit User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\n```py\nmodel_config {\n num_layers: 18\n pretrained_model_file: \"/path/to/the/model.tlt\"\n use_batch_norm: true\n objective_set {\n bbox {\n scale: 35.0\n offset: 0.5\n }\n cov {\n }\n }\n training_precision {\n backend_floatx: FLOAT32\n }\n arch: \"resnet\"\n all_projections: true\n}\n```\n\n### Instructions to deploy this model with DeepStream \n\nTo create the entire end-to-end video analytics application, deploy this model with [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk). DeepStream SDK is a streaming analytics toolkit to accelerate deployment of AI-based video analytics applications. The pruned model included here can be integrated directly into deepstream by following the instructions mentioned\nbelow.\n\n1. Run the default `deepstream-app` included in the DeepStream docker, by simply executing the commands below.\n\n ```sh\n ## Download Model:\n\n mkdir -p $HOME/facedetectir && \\\n wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/facedetectir/versions/pruned_v1.0/files/resnet18_facedetectir_pruned.etlt \\\n -O $HOME/facedetectir/resnet18_facedetectir_pruned.etlt && \\\n wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/facedetectir/versions/pruned_v1.0/files/facedetectir_int8.txt \\\n -O $HOME/facedetectir/facedetectir_int8.txt\n\n ## Run Application\n\n xhost +\n sudo docker run --gpus all -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v $HOME:/opt/nvidia/deepstream/deepstream-5.1/samples/models/tlt_pretrained_models \\\n -w /opt/nvidia/deepstream/deepstream-5.1/samples/configs/tlt_pretrained_models nvcr.io/nvidia/deepstream:5.1-21.02-samples \\\n deepstream-app -c deepstream_app_source1_labels_faceirnet.txt\n ```\n\n2. Install deepstream on your local host and run the deepstream-app.\n\n To deploy this model with [DeepStream 5.1](https://developer.nvidia.com/deepstream-sdk), please follow the instructions below:\n\n [Download](https://developer.nvidia.com/deepstream-sdk) and install DeepStream SDK. The installation instructions for DeepStream are provided in [DeepStream development guide](https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html). The config files for the purpose-built models are located in:\n\n ```sh\n /opt/nvidia/deepstream/deepstream-5.1/samples/configs/tlt_pretrained_models\n ```\n\n `/opt/nvidia/deepstream` is the default DeepStream installation directory. This path will be different if you are installing in a different directory.\n\n You will need 2 config files and 1 label file. These files are provided in the `tlt_pretrained_models` directory.\n\n ```sh\n deepstream_app_source1_faceirnet.txt - Main config file for DeepStream app\n config_infer_primary_faceirnet.txt - File to configure inference settings\n labels_faceirnet.txt - Label file with 1 class\n ```\n\n Key Parameters in `config_infer_primary_faceirnet.txt`\n\n ```sh\n tlt-model-key\n tlt-encoded-model\n labelfile-path\n int8-calib-file\n input-dims\n num-detected-classes\n ```\n\n Run `deepstream-app`:\n\n ```sh\n deepstream-app -c deepstream_app_source1_faceirnet.txt\n ```\n\n Documentation to deploy with DeepStream is provided in \"Deploying to DeepStream\" chapter of [TAO User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\n## Limitations \n\n### Small faces\n\nNVIDIA FaceIR model does not give good results on detecting small faces (generally, if the face occupies less than 10% of the image area, the face is small)\n\n## Model versions\n\n- **unpruned_v1.0** - ResNet18 based pre-trained model.\n- **pruned_v1.0** - ResNet18 deployment models. Contains common INT8 calibration cache for GPU and DLA.\n\n## References\n\n### Citations \n\nRedmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: CVPR. (2016)\nErhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks, In: CVPR. (2014)\nHe, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: CVPR (2015)\n\n### Using TAO Pre-trained Models \n\n- Get [TAO Container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit)\n- Get other Purpose-built models from NGC model registry:\n - [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- [Train like a ‘pro’ without being an AI expert using TAO AutoML](https://developer.nvidia.com/blog/training-like-an-ai-pro-using-tao-automl/)\n- [Create Custom AI models using NVIDIA TAO Toolkit with Azure Machine Learning ](https://developer.nvidia.com/blog/creating-custom-ai-models-using-nvidia-tao-toolkit-with-azure-machine-learning/)\n- [Developing and Deploying AI-powered Robots with NVIDIA Isaac Sim and NVIDIA TAO](https://developer.nvidia.com/blog/developing-and-deploying-ai-powered-robots-with-nvidia-isaac-sim-and-nvidia-tao/)\n- Learn endless ways to adapt and supercharge your AI workflows with TAO - [Whitepaper](https://developer.nvidia.com/tao-toolkit-usecases-whitepaper/1-introduction)\n- [Customize Action Recognition with TAO and deploy with DeepStream](https://developer.nvidia.com/blog/developing-and-deploying-your-custom-action-recognition-application-without-any-ai-expertise-using-tao-and-deepstream/)\n- Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - [Part 1](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-1) | [Part 2](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-2)\n- Learn how to train [real-time License plate detection and recognition app](https://developer.nvidia.com/blog/creating-a-real-time-license-plate-detection-and-recognition-app) with TAO and DeepStream.\n- Model accuracy is extremely important, learn how you can achieve [state of the art accuracy for classification and object detection models](https://developer.nvidia.com/blog/preparing-state-of-the-art-models-for-classification-and-object-detection-with-tao-toolkit/) using TAO\n\n### Suggested reading \n\n- More information on about TAO Toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/tao-toolkit)\n- [TAO documentation](https://docs.nvidia.com/tao/tao-toolkit/index.html)\n- Read the [TAO getting Started](https://docs.nvidia.com/tao/tao-toolkit/text/tao_quick_start_guide.html) guide and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html).\n- If you have any questions or feedback, please refer to the discussions on [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17)\n- Deploy your models for video analytics application using DeepStream. Learn more about [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk)\n- Deploy your models in [Riva](https://developer.nvidia.com/riva) for ConvAI use case.\n\n## License \n\nLicense to use these models is covered by the Model EULA. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of these [licenses](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/).\n\n## Ethical Considerations \n\nNVIDIA FaceIR model detects faces. 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","FaceDetectIR",["D",1731434401235],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/tao/models/facedetectir","rawImageUrl","https://raw.githubusercontent.com/kbojo/images/master/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",9532530,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[353],"displayLabels",[294,295,296,297,298,299,300,301,302,303,304,305,306,307],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_262":351,"_309":351,"_310":352,"_312":313},{"_262":348,"_309":349,"_310":350,"_312":313},{"_262":346,"_309":346,"_310":347,"_312":313},{"_262":343,"_309":344,"_310":345,"_312":313},{"_262":341,"_309":341,"_310":342,"_312":313},{"_262":337,"_309":338,"_310":339,"_312":313},{"_262":335,"_309":335,"_310":336,"_312":313},{"_262":331,"_309":332,"_310":333,"_312":313},{"_262":327,"_309":328,"_310":329,"_312":313},{"_262":325,"_309":325,"_310":326,"_312":313},{"_262":322,"_309":323,"_310":324,"_312":313},{"_262":318,"_309":319,"_310":320,"_312":313},{"_262":316,"_309":316,"_310":317,"_312":313},{"_262":308,"_309":308,"_310":311,"_312":313},"TAO Toolkit","value","filter",{"_314":315,"_309":308},"category","label","field","framework","Smart Infrastructure",{"_314":313,"_309":316},"Smart Cities / Spaces","indus_smart_cities_spaces",{"_314":321,"_309":319},"industry","Retail","indus_retail",{"_314":321,"_309":323},"Public Safety",{"_314":313,"_309":325},"Object Detection","uscs_object_detection",{"_314":330,"_309":328},"application","NVIDIA AI","soln_nvidia_ai",{"_314":334,"_309":332},"technology","NSPECT-1DBQ-GZ4A",{"_314":313,"_309":335},"Metropolis","pltfm_metropolis",{"_314":340,"_309":338},"platform","IR",{"_314":313,"_309":341},"Healthcare","indus_healthcare",{"_314":321,"_309":344},"DetectNet_v2",{"_314":313,"_309":346},"DeepStream","pltfm_deepstream",{"_314":340,"_309":349},"CV",{"_314":313,"_309":351},{"_262":354,"_309":354,"_310":355,"_312":356},"NVIDIA AI Enterprise Supported",{"_314":313,"_309":354},"feature",{"_135":1540,"_137":1541},"ngcApps",[1493],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1475":1476,"_1477":1478,"_1479":1480,"_1481":1482,"_1483":368,"_162":392,"_1484":1485,"_1473":60,"_1486":115,"_1487":115,"_1488":1489,"_1490":115,"_1491":1492},"amplitude",{"_1471":1472,"_1473":60,"_1064":-7,"_1474":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1465":1466,"_1467":1468,"_1469":1470},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.2.3","defaultSearchSuggestions",{"_1339":194,"_1340":1341,"_1342":1343,"_1344":1345},"announcements",[],"consentState",{"_429":1329,"_1330":115,"_1331":35,"_1332":35,"_1333":1334},"consentConfig",{"_411":412,"_413":414,"_415":416,"_417":418,"_419":420,"_421":422,"_423":424,"_425":426,"_427":428,"_429":430},"showContactAdmin",1782193432920,[],[],[406,407,408,409,410],{"_49":63,"_51":74,"_53":69,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":70,"_53":69,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":64,"_53":69,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":54,"_55":56,"_57":16,"_59":60},{"_49":50,"_51":52,"_53":69,"_55":-5,"_57":-5,"_59":60},"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.",5,[580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614],[453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469],{"_470":576,"_262":576,"_193":194,"_443":577},{"_470":571,"_262":571,"_193":194,"_443":572},{"_470":567,"_262":567,"_193":194,"_443":568},{"_470":562,"_262":562,"_193":194,"_443":563},{"_470":557,"_262":557,"_193":194,"_443":558},{"_470":545,"_262":545,"_193":194,"_443":546},{"_470":533,"_262":533,"_193":194,"_443":534},{"_470":528,"_262":528,"_193":194,"_443":529},{"_470":517,"_262":517,"_193":194,"_443":518},{"_470":512,"_262":512,"_193":194,"_443":513},{"_470":507,"_262":507,"_193":194,"_443":508},{"_470":502,"_262":502,"_193":194,"_443":503},{"_470":497,"_262":497,"_193":194,"_443":498},{"_470":492,"_262":492,"_193":194,"_443":493},{"_470":487,"_262":487,"_193":194,"_443":488},{"_470":480,"_262":480,"_193":194,"_443":481},{"_470":471,"_262":471,"_193":194,"_443":472},"hostName","amazon-adsystem.com",[473,474],{"_81":479,"_476":471,"_477":35,"_193":478},{"_81":475,"_476":471,"_477":35,"_193":478},"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",[482,483],{"_81":486,"_476":480,"_477":35,"_193":485},{"_81":484,"_476":480,"_477":35,"_193":485},"IDE","This domain is owned by Doubleclick (Google). 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It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[509],{"_81":510,"_476":507,"_477":35,"_193":511},"sp","Testing","google.com",[514],{"_81":515,"_476":512,"_477":35,"_193":516},"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",[519,520,521,522],{"_81":527,"_476":517,"_477":35,"_193":524},{"_81":526,"_476":517,"_477":35,"_193":524},{"_81":525,"_476":517,"_477":35,"_193":524},{"_81":523,"_476":517,"_477":35,"_193":524},"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",[530],{"_81":531,"_476":528,"_477":35,"_193":532},"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",[535,536,537,538],{"_81":544,"_476":533,"_477":35,"_193":540},{"_81":542,"_476":533,"_477":35,"_193":543},{"_81":541,"_476":533,"_477":35,"_193":540},{"_81":539,"_476":533,"_477":35,"_193":540},"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",[547,548,549,550],{"_81":556,"_476":545,"_477":35,"_193":554},{"_81":555,"_476":545,"_477":35,"_193":554},{"_81":553,"_476":545,"_477":35,"_193":554},{"_81":551,"_476":545,"_477":35,"_193":552},"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",[559],{"_81":560,"_476":557,"_477":35,"_193":561},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[564],{"_81":565,"_476":562,"_477":35,"_193":566},"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",[569],{"_81":570,"_476":567,"_477":35,"_193":511},"R","6sc.co",[573],{"_81":574,"_476":571,"_477":35,"_193":575},"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,[703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723],[668,669,670,671,672],{"_470":562,"_262":562,"_193":194,"_443":694},{"_470":528,"_262":528,"_193":194,"_443":689},{"_470":682,"_262":682,"_193":194,"_443":683},{"_470":678,"_262":678,"_193":194,"_443":679},{"_470":673,"_262":673,"_193":194,"_443":674},"nvcrm.my.site.com",[675],{"_81":676,"_476":673,"_477":35,"_193":677},"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",[680],{"_81":681,"_476":678,"_477":35,"_193":511},"edge","github.com",[684,685],{"_81":688,"_476":682,"_477":35,"_193":687},{"_81":686,"_476":682,"_477":35,"_193":687},"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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