Model Overview

The FoundationPose [1] model described in this model card estimates the 6D pose of a specific object passed into the network as input. The input contains the object’s image, depth map, and 3D model of the object. The output from the network is the estimated vector containing the pose and tracking of the object. When a 3D model of the object is not available, FoundationPose can use a few images of the object with corresponding depth map instead. The FoundationPose model is fully trained and does not require additional training for commercial applications.

Figure 1. FoundationPose real time pose estimates of grocery products for robot manipulation on the left, and mobile robot charging dock pose estimate on the right.

Model Architecture

FoundationPose is a DNN model trained to estimate and track the orientation and position of objects and corresponding parts. The architecture consists of three aggregated modules. The first is a pose refinement network, the second module is a pose selection network and lastly, an optional object modeling neural field to render unknown or model-free objects.\nThe object modeling neural field uses a signed distance function (SDF) [4] to approximate a neural object field for model-free objects without CAD model. The SDF is an implicit MLP network that maps any 3D point location to a signed distance value.\nDuring training, we first sample a group of coarse global poses from the input. The input is further rendered by the object modeling neural field to obtain a RGBD rendering field. The rendered field is passed to the pose refinement network together with additional crops of input observation from the camera that captures surrounding contexts around the object. Lastly, the refinement network outputs some hypotheses poses and the pose selection network selects the best pose. See Figure 1 for illustration.

How to Use this Model

Our model can be deployed in a wide variety of scenarios including robotic arm manipulation tasks, autonomous robot navigation, target tracking in delivery robots and so on. The model is intended to be run using NVIDIA hardware and software. For hardware, the model can run on any NVIDIA GPU including NVIDIA Jetson devices. The model is intended for Ubuntu 22.04 with CUDA 11.6 or Jetpack 6.0 DP and later. This model can only be used with TensorRT.

Instructions to Convert Model to TensorRT Engine Plan

This model is distributed in two formats. Version 1.0.0 contains encrypted ETLT files, while version 1.0.0_onnx contains the same model weights in an unencrypted ONNX format.

Using ONNX:\nThis model can be used in its unencrypted format as-is. However, the onnx files can also be converted to TensorRT engine plans using trtexec for better performance.

trtexec --onnx=./refine_model.onnx --saveEngine=./refine_trt_engine.plan --minShapes=input1:1x160x160x6,input2:1x160x160x6 --optShapes=input1:1x160x160x6,input2:1x160x160x6 --maxShapes=input1:252x160x160x6,input2:252x160x160x6\n\ntrtexec --onnx=./score_model.onnx --saveEngine=./score_trt_engine.plan --minShapes=input1:1x160x160x6,input2:1x160x160x6 --optShapes=input1:1x160x160x6,input2:1x160x160x6 --maxShapes=input1:252x160x160x6,input2:252x160x160x6\n

Using ETLT:\nIn order to use the model in this encrypted format, convert the two etlt files to TensorRT engine plans using TAO. The model is encrypted and will only operate with the key foundationpose. Please make sure to use this key with the TAO command to convert it into a TensorRT engine plan.

tao-converter -k foundationpose -t fp16 -e ./refine_trt_engine.plan -p input1,1x160x160x6,1x160x160x6,252x160x160x6 -p input2,1x160x160x6,1x160x160x6,252x160x160x6 -o output1,output2 refine_model.etlt\n\ntao-converter -k foundationpose -t fp16 -e ./score_trt_engine.plan -p input1,1x160x160x6,1x160x160x6,252x160x160x6 -p input2,1x160x160x6,1x160x160x6,252x160x160x6 -o output1 score_model.etlt\n

FoundationPose is accessible in Isaac-ROS under isaac_ros_foundationpose. It seamlessly integrates both the refinement and score model inference, along with all required preprocessing and postprocessing to deliver the accurate pose estimation. To run the FoudationPose ROS node, please refer to the quickstart available in isaac_ros_docs.

sample inference results

\nFigure 2. FoundationPose used to estimate the pose of multiple grocery objects on a tray for robot manipulation.\n

Training

During training, we first pretrain the neural object field, pose refine network, and pose ranking network separately. We then freeze the neural object field network and finetune the other modules in the network.\nWe model weights are updated during training to minimize the following objective loss functions: A contrastive loss and a cross-entropy loss function.

Dataset

Key Performance Indicators (KPIs)

We compare the performance of foundation-pose with other state of the art pose estimation models. Table 1.0 shows quantitative numbers on the standard BOP challenge benchmark for 6D Object Pose Estimation. The number comparison is made on this dataset without additional finetuning on the target dataset. FoundationPose was the #1 ranked solution in March 2024 for unseen objects (no studying for the test).

\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\">

Method	Test image	core	LM-o	T-LESS	TUD-L	IC-BIN	ITODD	HB	YCB-V
Our Model	RGB-D	0.726	0.733	0.617	0.906	0.528	0.609	0.809	0.882

Table 1.0 Showing 6D localization of unseen objects – Core datasets results for FoundationPose.

Realtime Inference Performance

Furthermore, we assess real time inference performance in queries per seconds (QPS) for FoundationPose in FP16 precision. The following profiling numbers are obtained using tensorrt’s trtexec tool on Jetson Orin and RTX 4090 GPU.

\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\">

Platform	Pose Estimation (QPS)	Pose Tracking (QPS)
Jetson Orin	4.6	746.0
RTX 4090	-	-
RTX 4060Ti	8.7	1599.4

Table 2.0 Showing Inference performance numbers on NVIDIA Jetson AGX Orin, RTX 4090, and RTX 4060Ti.

Limitations

Our FoundationPose model may have poor depth prediction for highly reflective or transparent objects causing the model to predict the incorrect object poses. Although, our model is robust to blurry images, accutely blurry objects in an scene may also lead to poor pose and tracking performance.

References

[1] Bowen Wen, Wei Yang, Jan Kautz, and Stan Birchfield (2023). FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects.
\n[2] Objaverse 1.0 for 3D object models.
\n[3] Downs, Laura, Anthony Francis, Nate Koenig, Brandon Kinman, Ryan Hickman, Krista Reymann, Thomas B. McHugh, and Vincent Vanhoucke. \"Google scanned objects: A high-quality dataset of 3d scanned household items.\" In 2022 International Conference on Robotics and Automation (ICRA), pp. 2553-2560. IEEE, 2022.
\n[4] Wen, Bowen, et al. \"Bundlesdf: Neural 6-dof tracking and 3d reconstruction of unknown objects.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.

License

License to use this model is covered by the Model EULA. By downloading the public and release version of the model, you accept the terms and conditions of these licenses.

Ethical Considerations

We do not use any real images in training Foundation Pose. All training dataset were synthetically generated using Isaac Sim. Hence, the semantic content of the input data does not contain any real person, place or identifiable information. The network learns geometry, and appearance and predicts poses of objects from only the generated synthetic dataset with no personal identifiable information. Thus, there are no ethical concerns for this work..

Acknowledgements

We acknowledge and thank the sources of assets used to render scenes and objects featured in our datasets in this attribution list.

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\n\nFigure 1. FoundationPose real time pose estimates of grocery products for robot manipulation on the left, and mobile robot charging dock pose estimate on the right.\n\n### Model Architecture\nFoundationPose is a DNN model trained to estimate and track the orientation and position of objects and corresponding parts. The architecture consists of three aggregated modules. The first is a pose refinement network, the second module is a pose selection network and lastly, an optional object modeling neural field to render unknown or model-free objects. \nThe object modeling neural field uses a signed distance function (SDF) [4] to approximate a neural object field for model-free objects without CAD model. The SDF is an implicit MLP network that maps any 3D point location to a signed distance value. \nDuring training, we first sample a group of coarse global poses from the input. The input is further rendered by the object modeling neural field to obtain a RGBD rendering field. The rendered field is passed to the pose refinement network together with additional crops of input observation from the camera that captures surrounding contexts around the object. Lastly, the refinement network outputs some hypotheses poses and the pose selection network selects the best pose. See Figure 1 for illustration.\n\n### How to Use this Model\nOur model can be deployed in a wide variety of scenarios including robotic arm manipulation tasks, autonomous robot navigation, target tracking in delivery robots and so on. The model is intended to be run using NVIDIA hardware and software. For hardware, the model can run on any NVIDIA GPU including NVIDIA Jetson devices. The model is intended for Ubuntu 22.04 with CUDA 11.6 or Jetpack 6.0 DP and later. This model can only be used with TensorRT.\n\n#### Instructions to Convert Model to TensorRT Engine Plan\nThis model is distributed in two formats. Version `1.0.0` contains encrypted ETLT files, while version `1.0.0_onnx` contains the same model weights in an unencrypted ONNX format.\n\n**Using ONNX:**\nThis model can be used in its unencrypted format as-is. However, the onnx files can also be converted to TensorRT engine plans using trtexec for better performance.\n```\ntrtexec --onnx=./refine_model.onnx --saveEngine=./refine_trt_engine.plan --minShapes=input1:1x160x160x6,input2:1x160x160x6 --optShapes=input1:1x160x160x6,input2:1x160x160x6 --maxShapes=input1:252x160x160x6,input2:252x160x160x6\n\ntrtexec --onnx=./score_model.onnx --saveEngine=./score_trt_engine.plan --minShapes=input1:1x160x160x6,input2:1x160x160x6 --optShapes=input1:1x160x160x6,input2:1x160x160x6 --maxShapes=input1:252x160x160x6,input2:252x160x160x6\n```\n\n**Using ETLT:**\nIn order to use the model in this encrypted format, convert the two etlt files to TensorRT engine plans using TAO. The model is encrypted and will only operate with the key foundationpose. Please make sure to use this key with the TAO command to convert it into a TensorRT engine plan.\n\n```\ntao-converter -k foundationpose -t fp16 -e ./refine_trt_engine.plan -p input1,1x160x160x6,1x160x160x6,252x160x160x6 -p input2,1x160x160x6,1x160x160x6,252x160x160x6 -o output1,output2 refine_model.etlt\n\ntao-converter -k foundationpose -t fp16 -e ./score_trt_engine.plan -p input1,1x160x160x6,1x160x160x6,252x160x160x6 -p input2,1x160x160x6,1x160x160x6,252x160x160x6 -o output1 score_model.etlt\n```\nFoundationPose is accessible in Isaac-ROS under isaac_ros_foundationpose. It seamlessly integrates both the refinement and score model inference, along with all required preprocessing and postprocessing to deliver the accurate pose estimation. To run the FoudationPose ROS node, please refer to the [quickstart](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_pose_estimation/isaac_ros_foundationpose/index.html#quickstart) available in isaac_ros_docs. \n\n### sample inference results\n

\nFigure 2. FoundationPose used to estimate the pose of multiple grocery objects on a tray for robot manipulation.\n\n### Training \nDuring training, we first pretrain the neural object field, pose refine network, and pose ranking network separately. We then freeze the neural object field network and finetune the other modules in the network. \nWe model weights are updated during training to minimize the following objective loss functions: A contrastive loss and a cross-entropy loss function.\n\n#### Dataset\n

\nFigure 3. Training synthetic data example with RGB, and 6DOF pose label generated in Isaac Sim.
\n

\nThe foundation pose model is trained on a dataset of 678,000 images with ground truth. Each image contains multiple object poses. The dataset is augmented with 8 permutations during training for a total of 5.4 million images. The dataset was synthetically generated with Isaac Sim, using 41K object models from Objaverse [2] and 1K object models from GSO [3]. Images are generated to include textures from real photographs rendered into the scenes.\n\n\n#### Key Performance Indicators (KPIs)\n\nWe compare the performance of foundation-pose with other state of the art pose estimation models. Table 1.0 shows quantitative numbers on the standard [BOP challenge benchmark for 6D Object Pose Estimation](https://bop.felk.cvut.cz/home/). The number comparison is made on this dataset without additional finetuning on the target dataset. FoundationPose was the #1 ranked solution in March 2024 for unseen objects (no studying for the test).\n\n| Method | Test image | core | LM-o | T-LESS | TUD-L | IC-BIN | ITODD | HB | YCB-V |\n| :-----| :-----| :-----| :-----| :-----| :-----| :-----| :-----| :-----| :-----|\n| Our Model | RGB-D | 0.726 | 0.733 | 0.617 | 0.906 | 0.528 | 0.609 | 0.809 | 0.882 |\n\nTable 1.0 Showing [6D localization of unseen objects – Core datasets](https://bop.felk.cvut.cz/leaderboards/pose-estimation-unseen-bop23/core-datasets/) results for FoundationPose. \n\n#### Realtime Inference Performance\nFurthermore, we assess real time inference performance in queries per seconds (QPS) for FoundationPose in FP16 precision. The following profiling numbers are obtained using tensorrt’s trtexec tool on Jetson Orin and RTX 4090 GPU.\n\n| Platform | Pose Estimation (QPS) | Pose Tracking (QPS)|\n| :---------| :----------------| :--------------|\n| Jetson Orin | 4.6 | 746.0 |\n| RTX 4090 | - | - |\n| RTX 4060Ti | 8.7 | 1599.4 |\n\nTable 2.0 Showing Inference performance numbers on NVIDIA Jetson AGX Orin, RTX 4090, and RTX 4060Ti.\n\n## Limitations\nOur FoundationPose model may have poor depth prediction for highly reflective or transparent objects causing the model to predict the incorrect object poses. Although, our model is robust to blurry images, accutely blurry objects in an scene may also lead to poor pose and tracking performance.\n\n## References\n[1] Bowen Wen, Wei Yang, Jan Kautz, and Stan Birchfield (2023). [FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects](https://arxiv.org/abs/2312.08344).
\n[2] [Objaverse 1.0](https://objaverse.allenai.org/objaverse-1.0/) for 3D object models.
\n[3] Downs, Laura, Anthony Francis, Nate Koenig, Brandon Kinman, Ryan Hickman, Krista Reymann, Thomas B. McHugh, and Vincent Vanhoucke. \"Google scanned objects: A high-quality dataset of 3d scanned household items.\" In 2022 International Conference on Robotics and Automation (ICRA), pp. 2553-2560. IEEE, 2022.
\n[4] Wen, Bowen, et al. \"Bundlesdf: Neural 6-dof tracking and 3d reconstruction of unknown objects.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.
\n\n### License\n\nLicense to use this model is covered by the Model EULA. By downloading the public and release version of the model, you accept the terms and conditions of these [licenses](https://developer.download.nvidia.com/licenses/tao_toolkit_21-08_models_eula.pdf).\n\n## Ethical Considerations\nWe do not use any real images in training Foundation Pose. All training dataset were synthetically generated using Isaac Sim. Hence, the semantic content of the input data does not contain any real person, place or identifiable information. The network learns geometry, and appearance and predicts poses of objects from only the generated synthetic dataset with no personal identifiable information. Thus, there are no ethical concerns for this work..\n\n## Acknowledgements\nWe acknowledge and thank the sources of assets used to render scenes and objects featured in our datasets in this [attribution list](https://media.githubusercontent.com/media/NVIDIA-ISAAC-ROS/.github/main/resources/isaac_ngc/foundationpose/objaverse_lvis_cc_filtered_attributes.txt).","displayName","FoundationPose",["D",1760388323942],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/isaac/models/foundationpose","rawImageUrl","https://media.githubusercontent.com/media/NVIDIA-ISAAC-ROS/.github/main/resources/isaac_ngc/foundationpose/foundationpose_1.0_manipulation_staticcam.png","canGuestDownload","latestVersionSizeInBytes",132069352,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","Field|Response\n--|--\nParticipation considerations from adversely impacted groups [(protected class)](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | Not Applicable. Model is trained to detect a class of objects that excludes people.\nMeasures taken to mitigate against unwanted bias: | Training dataset was synthetically-generated from assets that exclude human faces, skin color and gender. Hence data containing PII was never used to directly train our model. Thus, avoiding unwanted bias.","explainability","Field|Response\n--|--\nIntended Application(s) & Domain(s): | Learning pose and tracking vectors for robotics and autonomous machines.\nModel Type: | Pose and tracking estimation.\nIntended Users: | Developers building and/or customizing robotics applications.\nOutput: | Vector of dimension six, containing pose and tracking information.\nDescribe how the model works: | Predicts pose and tracking information of objects when input RGB or CAD is passed into the model.\nTechnical Limitations: | FoundationPose fails to work with transparent objects, glassy materials and acutely blurry images. \nPerformance Metrics: | AR score.\nPotential Known Risks: | N/A \nLicensing: | https://developer.download.nvidia.com/licenses/tao_toolkit_21-08_models_eula.pdf","privacy","Field|Response\n--|--\nGeneratable or Reverse engineerable personally-identifiable information? | Neither.\nWas consent obtained for any PII used? | Does not apply since we do not have people objects in our dataset.\nProtected classes used to create this model? | None.\nHow often is dataset reviewed? | Before Every Release.\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | Yes.\nIf PII was collected for the development of the model, was it collected by NVIDIA? | Yes.\nIf PII was collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Yes.\nIf PII was collected for the development of this AI model, was it minimised to only what was required? | Yes. Dataset does not contain audio or GPS location data.\nIs there provenance for all datasets used in training? | Synthetically generated data is completely traceable to the original 3D assets used to generate the data. Real data was only used to generate the synthetic data, and was not used in directly training the model. \nAre we able to identify and trace source of dataset? | Yes.\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes.\nIs data compliant with data subject requests for data correction or removal, if such a request was made? | Yes.\nApplicable NVIDIA Privacy Policy | [https://www.nvidia.com/en-us/about-nvidia/privacy-policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy)","safetyAndSecurity","Field|Response\n--|--\nVerified to have met prescribed quality standards? | Yes.\nTarget Key Performance Indicator(s) (KPI(s)): | Average Recall Rates (AR Score).\nModel Application(s): | Estimate pose and track motion of robotics and autonomous machines. \nDescribe life critical application (if present): | None.\nUse Case Restrictions: | Should not be used for life critical applications. \nModel and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training and dataset license constraints adhered to.","featureLabels",[],"displayLabels",[251,252,253,254,255,256,257,258],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_214":288,"_260":289,"_262":290,"_264":265},{"_214":285,"_260":286,"_262":287,"_264":265},{"_214":282,"_260":283,"_262":284,"_264":265},{"_214":278,"_260":279,"_262":280,"_264":265},{"_214":276,"_260":276,"_262":277,"_264":265},{"_214":272,"_260":273,"_262":274,"_264":265},{"_214":268,"_260":269,"_262":270,"_264":265},{"_214":259,"_260":261,"_262":263,"_264":265},"Vision AI","value","soln_vision_ai","filter",{"_266":267,"_260":261},"category","label","field","technology","Video Analytics","uscs_video_analytics",{"_266":271,"_260":269},"application","Robotics","indus_robotics",{"_266":275,"_260":273},"industry","NSPECT-RQFW-RMJA",{"_266":265,"_260":276},"Isaac","pltfm_isaac",{"_266":281,"_260":279},"platform","CUDA","pltfm_cuda",{"_266":281,"_260":283},"Computer Vision","soln_computer_vision",{"_266":267,"_260":286},"AI","soln_ai",{"_266":267,"_260":289},{"_113":1477,"_115":1478},"ngcApps",[1430],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1412":1413,"_1414":1415,"_1416":1417,"_1418":1419,"_1420":302,"_140":326,"_1421":1422,"_1410":55,"_1423":92,"_1424":92,"_1425":1426,"_1427":92,"_1428":1429},"amplitude",{"_1408":1409,"_1410":55,"_1000":-7,"_1411":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1402":1403,"_1404":1405,"_1406":1407},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1275":407,"_1276":1277,"_1278":1279,"_1280":1281},"announcements",[],"consentState",{"_364":1265,"_1266":92,"_1267":31,"_1268":31,"_1269":1270},"consentConfig",{"_346":347,"_348":349,"_350":351,"_352":353,"_354":355,"_356":357,"_358":359,"_360":361,"_362":363,"_364":365},"showContactAdmin",1782582603146,[],[],[340,341,342,343,344,345],{"_45":59,"_47":70,"_49":108,"_51":-5,"_53":-5,"_54":55},{"_45":59,"_47":68,"_49":108,"_51":-5,"_53":-5,"_54":55},{"_45":59,"_47":66,"_49":108,"_51":-5,"_53":-5,"_54":55},{"_45":59,"_47":60,"_49":108,"_51":-5,"_53":-5,"_54":55},{"_45":56,"_47":57,"_49":50,"_51":52,"_53":16,"_54":55},{"_45":46,"_47":48,"_49":108,"_51":-5,"_53":-5,"_54":55},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1257":1258,"_1259":1260,"_1261":1262,"_1263":1264},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[366,367,368,369,370],{"_371":909,"_79":910,"_168":911,"_375":92,"_376":18,"_378":912,"_380":913},{"_371":699,"_79":700,"_168":701,"_375":31,"_376":58,"_378":702,"_380":703},{"_371":598,"_79":599,"_168":600,"_375":31,"_376":601,"_378":602,"_380":603},{"_371":382,"_79":383,"_168":384,"_375":31,"_376":385,"_378":386,"_380":387},{"_371":372,"_79":373,"_168":374,"_375":31,"_376":377,"_378":379,"_380":381},"optanonGroupId","C0005","Social Media Cookies","These cookies are set by a range of social media services that we have added to the site to enable you to share our content with your friends and networks. They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550],[388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404],{"_405":512,"_214":512,"_168":407,"_378":513},{"_405":507,"_214":507,"_168":407,"_378":508},{"_405":503,"_214":503,"_168":407,"_378":504},{"_405":498,"_214":498,"_168":407,"_378":499},{"_405":493,"_214":493,"_168":407,"_378":494},{"_405":481,"_214":481,"_168":407,"_378":482},{"_405":469,"_214":469,"_168":407,"_378":470},{"_405":464,"_214":464,"_168":407,"_378":465},{"_405":453,"_214":453,"_168":407,"_378":454},{"_405":448,"_214":448,"_168":407,"_378":449},{"_405":443,"_214":443,"_168":407,"_378":444},{"_405":438,"_214":438,"_168":407,"_378":439},{"_405":433,"_214":433,"_168":407,"_378":434},{"_405":428,"_214":428,"_168":407,"_378":429},{"_405":423,"_214":423,"_168":407,"_378":424},{"_405":416,"_214":416,"_168":407,"_378":417},{"_405":406,"_214":406,"_168":407,"_378":408},"hostName","amazon-adsystem.com","",[409,410],{"_79":415,"_412":406,"_413":31,"_168":414},{"_79":411,"_412":406,"_413":31,"_168":414},"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",[418,419],{"_79":422,"_412":416,"_413":31,"_168":421},{"_79":420,"_412":416,"_413":31,"_168":421},"IDE","This domain is owned by Doubleclick (Google). 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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,[639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659],[604,605,606,607,608],{"_405":498,"_214":498,"_168":407,"_378":630},{"_405":464,"_214":464,"_168":407,"_378":625},{"_405":618,"_214":618,"_168":407,"_378":619},{"_405":614,"_214":614,"_168":407,"_378":615},{"_405":609,"_214":609,"_168":407,"_378":610},"nvcrm.my.site.com",[611],{"_79":612,"_412":609,"_413":31,"_168":613},"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",[616],{"_79":617,"_412":614,"_413":31,"_168":447},"edge","github.com",[620,621],{"_79":624,"_412":618,"_413":31,"_168":623},{"_79":622,"_412":618,"_413":31,"_168":623},"logged_in","This domain is owned by Github. 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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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Name	Size	Updated	Actions
refine_model.onnx	65.01 MB	October 13, 2025 UTC	curl wget Download
score_model.onnx	60.95 MB	October 13, 2025 UTC	curl wget Download