TAO Pretrained ODISE 1.1

What is Train Adapt Optimize (TAO) Toolkit?

Train Adapt Optimize (TAO) Toolkit is a Python-based AI toolkit for taking purpose-built pre-trained AI models and customizing them with your own data. TAO adapts popular network architectures and backbones to your data, allowing you to train, fine tune, prune, and export highly optimized and accurate AI models for edge deployment.

Pre-trained models accelerate the AI training process and reduce costs associated with large scale data collection, labeling, and training models from scratch. Transfer learning with pre-trained models can be used for AI applications in smart cities, retail, healthcare, industrial inspection, and more.

Build end-to-end services and solutions for transforming pixels and sensor data to actionable insights using TAO DeepStream SDK and TensorRT. These models are suitable for object detection, classification, and segmentation.

Model Overview

Open-vocabulary DIffusion-based panoptic SEgmentation (ODISE) developed by NVIDIA Research is an open vocabulary panoptic segmentation model. It leverages the frozen representation of pre-trained text-image diffusion and discriminative models to perform panoptic segmentation of any category in the wild. ODISE 1.1 is a TAO optimized version which replaces the diffusion backbone with the ConvNext based CLIP backbone.

Model Architecture

The model in this instance is a panoptic segmentator that takes color (RGB) images and text prompts as inputs and generates segmentation masks and associated labels as outputs. The backbone feature extractor of this model is the ConvNext-L CLIP model pretrained on LAION-2B dataset.

Training

This model was trained using the ODISE 1.1 implementation in TAO Toolkit available as a developer preview with TAO 5.2.0.

Training Data

ODISE 1.1 was trained on the COCO dataset for Panoptic Segmentation. The COCO dataset contains 118K training images and 5K validation images and corresponding annotation files. The annotation includes bounding boxes and segmentation masks of the 80 thing categories from the detection task and a subset of the 91 stuff categories from the stuff task.

Performance

Evaluation Data

We test the ODISE 1.1 model on the COCO 2017 validation dataset.

Methodology and KPI

The KPI for the evaluation data are reported below.

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

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

model	precision	PQ	AP	mIoU
ODISE1.1	FP32	55.5	46.3	64.6

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 with sufficient memory (>12G). This model can only be used with TAO Toolkit.

The primary use case for these models is open-vocabulary segmentation and auto-labeling.

It is intended for training and fine-tune using Train Adapt Optimize (TAO) Toolkit and the users' dataset of object detection. High fidelity models can be trained to new use cases. A Jupyter notebook is available as a part of TAO container and can be used to re-train.

Input

Image: B X 3 X 1024 X 1024 (B C H W)
Label: Panoptic segmentation groundtruth in COCO format

Output

Segmentation mask and label for each detected object in the input image.

Instructions to Use Pretrained Models with TAO

To use these models as pretrained weights for demo or transfer learning, please refer to README

Limitations

ODISE 1.1 was trained on the COCO dataset with roughly 200 object categories and heavily relies on the frozen CLIP model. Hence, the model may not perform well on data from completely different domains. We recommend further fine tuning on the target domain to get higher PQ/mIoU/AP.

Model Versions

odise1.1_trainable_v1.0 - Pre-trained ODISE1.1 model for finetune or inference.

Reference

Citations

Xu, Jiarui and Liu, Sifei and Vahdat, Arash and Byeon, Wonmin and Wang, Xiaolong and De Mello, Shalini: Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models

Using TAO Pre-trained Models

Get TAO Container
Get other purpose-built models from NGC model registry:\n
- TrafficCamNet
- DashCamNet
- FaceDetectIR
- VehicleMakeNet
- VehicleTypeNet
- ActionRecognitionNet
- PoseClassificationNet
- ReIdentificationNet
\n

License

This work is licensed under the Creative Commons Attribution NonCommercial ShareAlike 4.0 License (CC-BY-NC-SA-4.0). To view a copy of this license, please visit this link, or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

Technical Blogs

Train like a ‘pro’ without being an AI expert using TAO AutoML
Create Custom AI models using NVIDIA TAO Toolkit with Azure Machine Learning
Developing and Deploying AI-powered Robots with NVIDIA Isaac Sim and NVIDIA TAO
Learn endless ways to adapt and supercharge your AI workflows with TAO - Whitepaper
Customize Action Recognition with TAO and deploy with DeepStream
Read the two-part blog on training and optimizing 2D body pose estimation model with TAO - Part 1 | Part 2
Learn how to train a real-time License plate detection and recognition app with TAO and DeepStream.
Model accuracy is extremely important; learn how you can achieve state of the art accuracy for classification and object detection models using TAO.

Ethical Considerations

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 developers to ensure that it meets the requirements for the relevant industry and use case, that the necessary instructions and documentation are provided to understand error rates, confidence intervals, and results, and that the model is being used under the conditions and in the manner intended.

","resourceVersions",[258,259],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_233":209,"_91":234,"_44":235,"_236":-5,"_237":238,"_94":239,"_240":241,"_242":16,"_243":26,"_244":-5,"_245":26,"_246":-5,"_247":26,"_248":-5,"_249":26,"_250":-5,"_251":26,"_252":-5,"_253":26,"_254":-5,"_255":26,"_256":257},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/tao/models/odise/-","version","odise_trainable_v1.0","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/tao/odise:odise_trainable_v1.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/tao/models/odise/odise_trainable_v1.0/version-history","accessCheckResult",{"_232":156},"hasRelatedCollections","context",{"_85":86,"_87":88},1784106849320,[],[],[225,226,227,228,229,230,231],{"_40":55,"_42":66,"_44":57,"_46":58,"_48":67,"_50":51},{"_40":55,"_42":64,"_44":57,"_46":58,"_48":65,"_50":51},{"_40":55,"_42":62,"_44":57,"_46":58,"_48":63,"_50":51},{"_40":55,"_42":60,"_44":57,"_46":58,"_48":61,"_50":51},{"_40":55,"_42":56,"_44":172,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":54,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51},"allowed","versionId","","UPLOAD_COMPLETE","releaseType","createdDate",["D",1701899764412],["D",1740840456507],"totalSizeInBytes",1656388906,"totalFileCount","isSigned","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_78":262,"_260":263,"_236":-5,"_243":26,"_245":26,"_256":261,"_247":26},{"_78":209,"_260":209,"_236":-5,"_243":26,"_245":26,"_256":261,"_247":26},"name",[],"-","Latest (odise_trainable_v1.0)","nvidia/tao/odise","nvidia/tao/models/odise","odise","Model to recognise characters from a preceding OCDNet model.","# TAO Pretrained ODISE 1.1\n\n## What is Train Adapt Optimize (TAO) Toolkit? \n\n[Train Adapt Optimize (TAO) Toolkit](https://developer.nvidia.com/tao-toolkit) is a Python-based AI toolkit for taking purpose-built pre-trained AI models and customizing them with your own data. TAO adapts popular network architectures and backbones to your data, allowing you to train, fine tune, prune, and export highly optimized and accurate AI models for edge deployment.\n\nPre-trained models accelerate the AI training process and reduce costs associated with large scale data collection, labeling, and training models from scratch. Transfer learning with pre-trained models can be used for AI applications in smart cities, retail, healthcare, industrial inspection, and more.\n\nBuild end-to-end services and solutions for transforming pixels and sensor data to actionable insights using TAO [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) and [TensorRT](https://developer.nvidia.com/tensorrt). These models are suitable for object detection, classification, and segmentation.\n\n## Model Overview \n\nOpen-vocabulary DIffusion-based panoptic SEgmentation ([ODISE](https://arxiv.org/abs/2303.04803)) developed by NVIDIA Research is an open vocabulary panoptic segmentation model. It leverages the frozen representation of pre-trained text-image diffusion and discriminative models to perform panoptic segmentation of any category in the wild. [ODISE 1.1](https://github.com/NVIDIA/tao_pytorch_backend/tree/main/nvidia_tao_pytorch/cv/odise) is a TAO optimized version which replaces the diffusion backbone with the ConvNext based CLIP backbone.\n\n## Model Architecture \n\nThe model in this instance is a panoptic segmentator that takes color (RGB) images and text prompts as inputs and generates segmentation masks and associated labels as outputs. The backbone feature extractor of this model is the ConvNext-L [CLIP](https://huggingface.co/laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup) model pretrained on LAION-2B dataset.\n\n## Training \n\nThis model was trained using the ODISE 1.1 implementation in TAO Toolkit available as a developer preview with TAO 5.2.0.\n\n### Training Data \n\nODISE 1.1 was trained on the [COCO dataset for Panoptic Segmentation](https://cocodataset.org/). The COCO dataset contains 118K training images and 5K validation images and corresponding annotation files. The annotation includes bounding boxes and segmentation masks of the 80 thing categories from the detection task and a subset of the 91 stuff categories from the stuff task.\n\n## Performance\n\n### Evaluation Data \n\nWe test the ODISE 1.1 model on the COCO 2017 validation dataset.\n\n### Methodology and KPI\n\nThe KPI for the evaluation data are reported below. \n\n|model|precision|PQ|AP|mIoU|\n|---|---|---|---|---|\n|ODISE1.1|FP32|55.5|46.3|64.6\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 with sufficient memory (>12G). This model can only be used with [TAO Toolkit](https://developer.nvidia.com/tao-toolkit).\n\nThe primary use case for these models is open-vocabulary segmentation and auto-labeling.\n\nIt is intended for training and fine-tune using Train Adapt Optimize (TAO) Toolkit and the users' dataset of object detection. High fidelity models can be trained to new use cases. A Jupyter notebook is available as a part of [TAO container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit-pytorch) and can be used to re-train.\n\n### Input\n\n- Image: B X 3 X 1024 X 1024 (B C H W)\n- Label: Panoptic segmentation groundtruth in COCO format\n\n### Output\n\nSegmentation mask and label for each detected object in the input image.\n\n### Instructions to Use Pretrained Models with TAO\n\nTo use these models as pretrained weights for demo or transfer learning, please refer to [README](https://github.com/NVIDIA/tao_pytorch_backend/tree/main/nvidia_tao_pytorch/cv/odise/README.md)\n\n## Limitations\n\nODISE 1.1 was trained on the COCO dataset with roughly 200 object categories and heavily relies on the frozen CLIP model. Hence, the model may not perform well on data from completely different domains. We recommend further fine tuning on the target domain to get higher PQ/mIoU/AP.\n\n## Model Versions\n\n- **odise1.1_trainable_v1.0** - Pre-trained ODISE1.1 model for finetune or inference.\n\n## Reference\n\n### Citations \n\n- Xu, Jiarui and Liu, Sifei and Vahdat, Arash and Byeon, Wonmin and Wang, Xiaolong and De Mello, Shalini: Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models\n\n## Using TAO Pre-trained Models \n\n- Get [TAO Container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit)\n- Get other purpose-built models from NGC model registry:\n - [TrafficCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:trafficcamnet)\n - [DashCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:dashcamnet)\n - [FaceDetectIR](https://ngc.nvidia.com/catalog/models/nvidia:tao:facedetectir)\n - [VehicleMakeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehiclemakenet)\n - [VehicleTypeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehicletypenet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [PoseClassificationNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/poseclassificationnet)\n - [ReIdentificationNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/reidentificationnet)\n\n## License \n\nThis work is licensed under the Creative Commons Attribution NonCommercial ShareAlike 4.0 License (CC-BY-NC-SA-4.0). To view a copy of this license, please visit this [link](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode), or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.\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 two-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 a [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 TAO Toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/tao-toolkit)\n- Refer to the [TAO documentation](https://docs.nvidia.com/tao/tao-toolkit/index.html)\n- Read the [TAO Toolkit Quick Start Guide](https://docs.nvidia.com/tao/tao-toolkit/text/tao_quick_start_guide.html) 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 the [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 the [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## Ethical Considerations \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 developers to ensure that it meets the requirements for the relevant industry and use case, that the necessary instructions 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.","TAO Toolkit ODISE 1.1",["D",1732745311361],"/api/image/nvidia/tao/models/odise","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png",1534219088,"bias","explainability","privacy","safetyAndSecurity",[],[283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303],[],[],[],{"_92":148,"_129":149,"_130":342,"_132":133},{"_92":340,"_129":340,"_130":341,"_132":133},{"_92":145,"_129":146,"_130":339,"_132":133},{"_92":337,"_129":337,"_130":338,"_132":133},{"_92":335,"_129":335,"_130":336,"_132":133},{"_92":333,"_129":333,"_130":334,"_132":133},{"_92":141,"_129":142,"_130":332,"_132":133},{"_92":330,"_129":330,"_130":331,"_132":133},{"_92":327,"_129":328,"_130":329,"_132":133},{"_92":325,"_129":325,"_130":326,"_132":133},{"_92":323,"_129":323,"_130":324,"_132":133},{"_92":321,"_129":321,"_130":322,"_132":133},{"_92":319,"_129":319,"_130":320,"_132":133},{"_92":317,"_129":317,"_130":318,"_132":133},{"_92":315,"_129":315,"_130":316,"_132":133},{"_92":93,"_129":93,"_130":314,"_132":133},{"_92":311,"_129":311,"_130":312,"_132":133},{"_92":309,"_129":309,"_130":310,"_132":133},{"_92":128,"_129":128,"_130":308,"_132":133},{"_92":306,"_129":306,"_130":307,"_132":133},{"_92":304,"_129":304,"_130":305,"_132":133},"Zero-Shot Segmentation",{"_134":133,"_129":304},"Transfer Learning Toolkit",{"_134":133,"_129":306},{"_134":133,"_129":128},"TLT",{"_134":133,"_129":309},"TAO Toolkit",{"_134":313,"_129":311},"framework",{"_134":133,"_129":93},"Smart Infrastructure",{"_134":133,"_129":315},"Smart City",{"_134":133,"_129":317},"Semantic Segmentation",{"_134":133,"_129":319},"Panoptic Segmentation",{"_134":133,"_129":321},"Open Vocabulary Segmentation",{"_134":133,"_129":323},"NSPECT-E99B-1B8S",{"_134":133,"_129":325},"Metropolis","pltfm_metropolis",{"_134":144,"_129":328},"Instance Segmentation",{"_134":133,"_129":330},{"_134":144,"_129":142},"Deep Learning",{"_134":133,"_129":333},"CV",{"_134":133,"_129":335},"ConvNext-L",{"_134":133,"_129":337},{"_134":138,"_129":146},"COCO",{"_134":133,"_129":340},{"_134":138,"_129":149},{"_177":1512,"_179":1513},"ngcApps",[1465],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1446":1447,"_1448":1449,"_1450":1451,"_1452":1453,"_1454":354,"_208":378,"_1455":1456,"_1457":51,"_1458":156,"_1459":156,"_1460":1461,"_1462":156,"_1463":1464},"amplitude",{"_1443":1444,"_1051":-7,"_1445":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1437":1438,"_1439":1440,"_1441":1442},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1326":234,"_1327":1328,"_1329":1330,"_1331":1332},"announcements",[],"consentState",{"_417":1316,"_1317":156,"_1318":26,"_1319":26,"_1320":1321},"consentConfig",{"_399":400,"_401":402,"_403":404,"_405":406,"_407":408,"_409":410,"_411":412,"_413":414,"_415":416,"_417":418},"showContactAdmin",1784106849219,[],[],[392,393,394,395,396,397],{"_40":55,"_42":66,"_44":172,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":172,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":62,"_44":172,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":172,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":172,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":398,"_50":51},10,"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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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,[568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602],[441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457],{"_458":564,"_92":564,"_91":234,"_431":565},{"_458":559,"_92":559,"_91":234,"_431":560},{"_458":555,"_92":555,"_91":234,"_431":556},{"_458":550,"_92":550,"_91":234,"_431":551},{"_458":545,"_92":545,"_91":234,"_431":546},{"_458":533,"_92":533,"_91":234,"_431":534},{"_458":521,"_92":521,"_91":234,"_431":522},{"_458":516,"_92":516,"_91":234,"_431":517},{"_458":505,"_92":505,"_91":234,"_431":506},{"_458":500,"_92":500,"_91":234,"_431":501},{"_458":495,"_92":495,"_91":234,"_431":496},{"_458":490,"_92":490,"_91":234,"_431":491},{"_458":485,"_92":485,"_91":234,"_431":486},{"_458":480,"_92":480,"_91":234,"_431":481},{"_458":475,"_92":475,"_91":234,"_431":476},{"_458":468,"_92":468,"_91":234,"_431":469},{"_458":459,"_92":459,"_91":234,"_431":460},"hostName","amazon-adsystem.com",[461,462],{"_260":467,"_464":459,"_465":26,"_91":466},{"_260":463,"_464":459,"_465":26,"_91":466},"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",[470,471],{"_260":474,"_464":468,"_465":26,"_91":473},{"_260":472,"_464":468,"_465":26,"_91":473},"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",[477],{"_260":478,"_464":475,"_465":26,"_91":479},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[482],{"_260":483,"_464":480,"_465":26,"_91":484},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","If you know anything about this host and its uses please add your contributions here. ","tiktok.com",[487],{"_260":488,"_464":485,"_465":26,"_91":489},"_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",[492],{"_260":493,"_464":490,"_465":26,"_91":494},"muc_ads","This domain is owned by Twitter. 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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.","tags.srv.stackadapt.com",[507,508,509,510],{"_260":515,"_464":505,"_465":26,"_91":512},{"_260":514,"_464":505,"_465":26,"_91":512},{"_260":513,"_464":505,"_465":26,"_91":512},{"_260":511,"_464":505,"_465":26,"_91":512},"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",[518],{"_260":519,"_464":516,"_465":26,"_91":520},"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",[523,524,525,526],{"_260":532,"_464":521,"_465":26,"_91":528},{"_260":530,"_464":521,"_465":26,"_91":531},{"_260":529,"_464":521,"_465":26,"_91":528},{"_260":527,"_464":521,"_465":26,"_91":528},"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",[535,536,537,538],{"_260":544,"_464":533,"_465":26,"_91":542},{"_260":543,"_464":533,"_465":26,"_91":542},{"_260":541,"_464":533,"_465":26,"_91":542},{"_260":539,"_464":533,"_465":26,"_91":540},"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",[547],{"_260":548,"_464":545,"_465":26,"_91":549},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[552],{"_260":553,"_464":550,"_465":26,"_91":554},"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",[557],{"_260":558,"_464":555,"_465":26,"_91":499},"R","6sc.co",[561],{"_260":562,"_464":559,"_465":26,"_91":563},"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.",[690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710],[655,656,657,658,659],{"_458":550,"_92":550,"_91":234,"_431":681},{"_458":516,"_92":516,"_91":234,"_431":676},{"_458":669,"_92":669,"_91":234,"_431":670},{"_458":665,"_92":665,"_91":234,"_431":666},{"_458":660,"_92":660,"_91":234,"_431":661},"nvcrm.my.site.com",[662],{"_260":663,"_464":660,"_465":26,"_91":664},"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",[667],{"_260":668,"_464":665,"_465":26,"_91":499},"edge","github.com",[671,672],{"_260":675,"_464":669,"_465":26,"_91":674},{"_260":673,"_464":669,"_465":26,"_91":674},"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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