Model Overview

SyntheticaDETR model is an efficient real-time object detection transformer based neural network, inspired by RT-DETR [1]. It is designed to take RGB images as input and detect objects within the scene with corresponding detection confidence. The output of the network are 2D bounding boxes on the detected objects. SyntheticaDETR is named for its training data, which is 100% synthetically generated providing detection accuracies that exceed similar models which include real data in their training set. The SyntheticaDETR model is fully trained for a set of objects, separated into models for robot grasping, and non-grasp commercial applications. Samples of both grasp and non-grasp objects are seen in Figure. 1 and 2 below.

\nFigure 1 (Left) SyntheticaDETR detects grocery objects for robot manipulation.
\nFigure 2 (Right) SyntheticaDETR detects a mobile robot charging station dock.\n

Architecture

SyntheticaDETR pipeline consists of four modules: A backbone module, an efficient hybrid encoder, a query selector and a decoder head. We assess two backbone architectures: a ConvNeXt-small architecture [2] and a ResNet-50 architecture. The backbone extracts multi-scale features from the image space for downstream feedforward to the hybrid encoder. The encoder tokenizes the features into embeddings and uses attention as a filter to obtain the most important features. The query selector is further used to select some image features to serve as initial object queries for the decoder. Lastly, the decoder uses auxiliary prediction heads to iteratively optimize the object queries and generate bounding boxes and associated confidence scores. We threshold the confidence value at 0.9 for the model trained on grasp objects and 0.7 for the model trained on non-grasp (AMR) objects.

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. The two models in this card are named sdetr_grasp.etlt, named to identify objects that can be grasped and sdetr_amr.etlt, which refers to the model trained on AMR - non-grasp objects.

Instructions to Convert Model to TensorRT Engine Plan

In order to use this model, convert the etlt files to TensorRT engine plans using TAO. The model is encrypted and will only operate with the key rtdetr. Please make sure to use this key with the TAO command to convert it into a TensorRT engine plan.

Convert the SyntheticaDETR pose model to an engine plan:

./tao-converter -k sdetr -t fp16 -e sdetr_grasp.plan -p images,1x3x640x640,2x3x640x640,4x3x640x640 -p orig_target_sizes,1x2,2x2,4x2  sdetr_grasp.etlt\n\n./tao-converter -k sdetr -t fp16 -e sdetr_amr.plan -p images,1x3x640x640,2x3x640x640,4x3x640x640 -p orig_target_sizes,1x2,2x2,4x2  sdetr_amr.etlt\n

SyntheticaDETR models can be run in ROS 2 using NVIDIA’s Isaac ROS RT-DETR package. The Isaac ROS SyntheticaDETR quickstart includes all required preprocessing and postprocessing to deliver accurate pose estimation in standard ROS message formats.

sample inference results

\nFigure 3 & 4. Object detection on objects in the YCB dataset.\n

Intended Use

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.

Training Dataset

We generated 13 million SDG image samples using Isaac Sim Replicator for training. Each scene is procedurally generated with random domain parameters to diversify the color, lighting, shape and other factors, leading to robust generalization of the network during training.\nTo render the scenes in Omniverse, we select 3D assets from the YCB-V [3] datasets, Google Scanned Objects (GSO)[4], random 3D assets in the wild and other custom grocery assets.

Training

During training, we use experiment with 2 backbone models: Resnet-50 and ConvNeXt-Small. We re-train the end-to-end pipeline for about 20 epochs on a large synthetically generated dataset (SDG). We used the classification loss and localization loss to formulate the training objectives. The classification loss is the log-likelihood loss and it is used to compute the difference in the predicted target object classes from corresponding groundtruth. The localization loss is averaged Intersection over Union (IoU). It is used to optimize the accuracy of predictions on bounding box coordinates. Lastly, we introduce various augmentation techniques to enhance regularization during training.

Key Performance Indicators KPIs

The key performance indicators are mean average precision and mean average recall. They are standard metrics defined for object detection and used to rank different methods on the BOP challenge dataset.

\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\">

Data	Model Name	mAP	mAR
Synthetic	SyntheticaDETR	0.839	0.860
Synthetic and Real	SyntheticaDETR	0.885	0.903

Table 1.0 Showing results of 2D amodal detection on the YCB-V BOP benchmark for 21 classes at 640x480 resolution. Result is shown for ConvNeXt-Small backbone.

The metrics shown in Table 1.0 indicates SyntheticaDETR sets new state-of-the-art records on the BOP Leaderboard at the time of publication.

Realtime Inference Performance

In addition, we assess the real-time inference performance of two version models at FP16 precision. The following profiling numbers are obtained using tensorrt’s trtexec tool on Jetson Orin and other NVIDIA GPUs.

\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	Thoughput (FPS, Resnet50)	Thoughput (FPS, ConvNeXt-small)
Jetson Orin	86.107	31.6705
RTX 4060Ti	278.151	104.536
RTX 4090	540.533	219.724

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

Limitations

NVIDIA SyntheticaDETR model was trained to be used as an object detector for objects. The detected objects in poor lighting conditions could be inaccurate.

References

[1] Lv, Wenyu, et al. \"Detrs beat yolos on real-time object detection.\" arXiv preprint arXiv:2304.08069 (2023).
\n[2] Liu, Zhuang, et al. \"A convnet for the 2020s.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.
\n[3] Yu Xiang, Tanner Schmidt, Venkatraman Narayanan and Dieter Fox. PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes. In Robotics: Science and Systems (RSS), 2018.
\n[4] Downs, Laura, et al. \"Google scanned objects: A high-quality dataset of 3d scanned household items.\" 2022 International Conference on Robotics and Automation (ICRA). IEEE, 2022.

Ethical Considerations

We trained SyntheticaDETR using only synthetically generated data. The network learns geometry, and semantic understanding from SDG without additional need for real data. Thus, there are no ethical concerns for this work.

Acknowledgements

We acknowledge the sources of assets used to render scenes and objects featured in our datasets. The source of our training dataset is YCB dataset. The assets were scanned from home goods and items purchased online. Please see the source list here.

APPENDIX

Object Classes

For reference, we make a list of all the target classes we used to train the SyntheticaDETR model.

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

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

Grasp Model	Object Classes	Purchase Link
	002_master_chef_can	N/A. Available Online
	003_cracker_box	N/A. Available Online
	004_sugar_box	N/A. Available Online
	005_tomato_soup_can	N/A. Available Online
YCB Models	006_mustard_bottle	N/A. Available Online
	007_tuna_fish_can	N/A. Available Online
	008_pudding_box	N/A. Available Online
	009_gelatin_box	N/A. Available Online
	010_potted_meat_can	N/A. Available Online
	011_banana	Link
	019_pitcher_base	Link
	021_bleach_cleanser	N/A. Available Online
	024_bowl	Link
	025_mug	N/A.
	035_power_drill	Link
	036_wood_block	N/A. Available Online
	037_scissors	Link
	040_large_marker	Link
	051_large_clamp	Link
	052_extra_large_clamp	Link
	061_foam_brick	Link

\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\">

Non-grasp Model	Object Classes
	Nova Carter Dock
	Nova Carter
	Pallets
	Forklifts

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

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

Grocery objects	Object Classes
	365 Mac & Cheese
	365 Facial Tissue
Grocery Objects	365 Peanut Butter
	365 Olive Can
	365 Hand Sanitizing Wipes
	Skipjack Tuna in water

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\nFigure 1 (Left) SyntheticaDETR detects grocery objects for robot manipulation.
\nFigure 2 (Right) SyntheticaDETR detects a mobile robot charging station dock.\n\n\n### Architecture \nSyntheticaDETR pipeline consists of four modules: A backbone module, an efficient hybrid encoder, a query selector and a decoder head. We assess two backbone architectures: a ConvNeXt-small architecture [2] and a ResNet-50 architecture. The backbone extracts multi-scale features from the image space for downstream feedforward to the hybrid encoder. The encoder tokenizes the features into embeddings and uses attention as a filter to obtain the most important features. The query selector is further used to select some image features to serve as initial object queries for the decoder. Lastly, the decoder uses auxiliary prediction heads to iteratively optimize the object queries and generate bounding boxes and associated confidence scores. We threshold the confidence value at 0.9 for the model trained on grasp objects and 0.7 for the model trained on non-grasp (AMR) objects.\n\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. The two models in this card are named sdetr_grasp.etlt, named to identify objects that can be grasped and sdetr_amr.etlt, which refers to the model trained on AMR - non-grasp objects.\n\n\n#### Instructions to Convert Model to TensorRT Engine Plan\nIn order to use this model, convert the etlt files to TensorRT engine plans using TAO. The model is encrypted and will only operate with the key rtdetr. Please make sure to use this key with the TAO command to convert it into a TensorRT engine plan.\n\n\n#### Convert the SyntheticaDETR pose model to an engine plan:\n```\n./tao-converter -k sdetr -t fp16 -e sdetr_grasp.plan -p images,1x3x640x640,2x3x640x640,4x3x640x640 -p orig_target_sizes,1x2,2x2,4x2 sdetr_grasp.etlt\n\n./tao-converter -k sdetr -t fp16 -e sdetr_amr.plan -p images,1x3x640x640,2x3x640x640,4x3x640x640 -p orig_target_sizes,1x2,2x2,4x2 sdetr_amr.etlt\n```\n\nSyntheticaDETR models can be run in ROS 2 using NVIDIA’s Isaac ROS RT-DETR package. The Isaac ROS SyntheticaDETR [quickstart](https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_object_detection/isaac_ros_rtdetr/index.html#quickstart) includes all required preprocessing and postprocessing to deliver accurate pose estimation in standard ROS message formats. \n\n### sample inference results\n\n

\nFigure 3 & 4. Object detection on objects in the YCB dataset.\n\n## Intended Use\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.\n\n### Training Dataset\n

\nFigure 5 (left) Training synthetic data example with RGB, and object label generated in Isaac Sim for manipulation objects.\nFigure 6 (right) We included non-grasp objects in our training dataset.

\n\nWe generated 13 million SDG image samples using Isaac Sim [Replicator](https://docs.omniverse.nvidia.com/isaacsim/latest/manual_replicator.html) for training. Each scene is procedurally generated with random domain parameters to diversify the color, lighting, shape and other factors, leading to robust generalization of the network during training. \nTo render the scenes in Omniverse, we select 3D assets from the YCB-V [3] datasets, Google Scanned Objects (GSO)[4], random 3D assets in the wild and other custom grocery assets. \n\n## Training\nDuring training, we use experiment with 2 backbone models: Resnet-50 and ConvNeXt-Small. We re-train the end-to-end pipeline for about 20 epochs on a large synthetically generated dataset (SDG). We used the classification loss and localization loss to formulate the training objectives. The classification loss is the log-likelihood loss and it is used to compute the difference in the predicted target object classes from corresponding groundtruth. The localization loss is averaged Intersection over Union (IoU). It is used to optimize the accuracy of predictions on bounding box coordinates. Lastly, we introduce various augmentation techniques to enhance regularization during training.\n\n### Key Performance Indicators KPIs\nThe key performance indicators are mean average precision and mean average recall. They are standard metrics defined for object detection and used to rank different methods on the BOP challenge dataset.\n\n| Data | Model Name | mAP | mAR |\n|:---- |:---------- | :--- | :--- |\nSynthetic | SyntheticaDETR | 0.839 | 0.860 |\nSynthetic and Real | SyntheticaDETR | 0.885 |0.903 |\n\nTable 1.0 Showing results of 2D amodal detection on the YCB-V BOP benchmark for 21 classes at 640x480 resolution. Result is shown for ConvNeXt-Small backbone.\n\nThe metrics shown in Table 1.0 indicates SyntheticaDETR sets new state-of-the-art records on the [BOP Leaderboard](https://bop.felk.cvut.cz/leaderboards/detection-bop22/ycb-v/) at the time of publication.\n\n#### Realtime Inference Performance\nIn addition, we assess the real-time inference performance of two version models at FP16 precision. The following profiling numbers are obtained using tensorrt’s trtexec tool on Jetson Orin and other NVIDIA GPUs.\n\n| Platform | Thoughput (FPS, Resnet50) | Thoughput (FPS, ConvNeXt-small) |\n| :-------- | :------------------------- | :-------------------------------- |\n| Jetson Orin | 86.107 | 31.6705 | \n| RTX 4060Ti | 278.151 | 104.536 |\n| RTX 4090 | 540.533 | 219.724 |\n\nTable 2.0 Showing Inference performance numbers on NVIDIA Jetson AGX Orin, RTX 4090, and RTX 4060Ti.\n\n### Limitations\nNVIDIA SyntheticaDETR model was trained to be used as an object detector for objects. The detected objects in poor lighting conditions could be inaccurate.\n\n### References\n[1] Lv, Wenyu, et al. \"Detrs beat yolos on real-time object detection.\" arXiv preprint arXiv:2304.08069 (2023).
\n[2] Liu, Zhuang, et al. \"A convnet for the 2020s.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.
\n[3] Yu Xiang, Tanner Schmidt, Venkatraman Narayanan and Dieter Fox. PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes. In Robotics: Science and Systems (RSS), 2018.
\n[4] Downs, Laura, et al. \"Google scanned objects: A high-quality dataset of 3d scanned household items.\" 2022 International Conference on Robotics and Automation (ICRA). IEEE, 2022.
\n\n### Ethical Considerations\nWe trained SyntheticaDETR using only synthetically generated data. The network learns geometry, and semantic understanding from SDG without additional need for real data. Thus, there are no ethical concerns for this work.\n\n## Acknowledgements\nWe acknowledge the sources of assets used to render scenes and objects featured in our datasets. The source of our training dataset is YCB dataset. The assets were scanned from home goods and items purchased online. Please see the source list [here](https://www.ycbbenchmarks.com/object-set-purchase-links/).\n\n### APPENDIX\n#### Object Classes\nFor reference, we make a list of all the target classes we used to train the SyntheticaDETR model.\n\n| Grasp Model | Object Classes | Purchase Link\n| :----------- | :-------------- | :------\n|| 002_master_chef_can | N/A. Available Online\n|| 003_cracker_box | N/A. Available Online\n|| 004_sugar_box | N/A. Available Online\n|| 005_tomato_soup_can | N/A. Available Online \n| [YCB Models](https://www.ycbbenchmarks.com/object-models/) | 006_mustard_bottle | N/A. Available Online \n| | 007_tuna_fish_can | N/A. Available Online\n| | 008_pudding_box | N/A. Available Online\n| | 009_gelatin_box | N/A. Available Online\n| | 010_potted_meat_can | N/A. Available Online\n| | 011_banana | [Link](http://www.amazon.com/gp/product/B001613Q8A?colid=3SB5HTT9MA7Q4&coliid=I257YGSC6M0XAP&ref_=wl_it_dp_o_pC_nS_ttl) \n| | 019_pitcher_base | [Link](http://www.amazon.com/gp/product/B0010L40RM?colid=3SB5HTT9MA7Q4&coliid=I198GBMUBYWXYS&psc=1&ref_=wl_it_dp_o_pC_nS_ttl)\n| | 021_bleach_cleanser | N/A. Available Online\n| | 024_bowl | [Link](http://www.amazon.com/gp/product/B0009PUR86?colid=3SB5HTT9MA7Q4&coliid=I2IN65A7YJEL18&psc=1&ref_=wl_it_dp_o_pC_nS_ttl)\n| | 025_mug | N/A.\n| | 035_power_drill | [Link](http://www.amazon.com/gp/product/B005LTNLDI?colid=3SB5HTT9MA7Q4&coliid=IBXN4X079SFBZ&ref_=wl_it_dp_o_pC_nS_ttl)\n| | 036_wood_block | N/A. Available Online\n| | 037_scissors | [Link](http://www.amazon.com/gp/product/B000P0LNRE?colid=3SB5HTT9MA7Q4&coliid=I21HO0UKM7W21W&psc=1&ref_=wl_it_dp_o_pd_nS_ttl)\n| | 040_large_marker | [Link](http://www.amazon.com/gp/product/B00006JNK2?colid=3SB5HTT9MA7Q4&coliid=I1QGYZK6D2C9E7&psc=1&ref_=wl_it_dp_o_pC_nS_ttl)\n| | 051_large_clamp | [Link](http://www.harborfreight.com/2-12-in-nylon-spring-clamp-69290.html)\n| | 052_extra_large_clamp | [Link](http://www.harborfreight.com/2-12-in-nylon-spring-clamp-69290.html)\n| | 061_foam_brick | [Link](http://www.amazon.com/gp/product/B0076ZWQZO?colid=3SB5HTT9MA7Q4&coliid=I2QKS7MI5EQI6A&ref_=wl_it_dp_o_pC_S_ttl)\n\n| Non-grasp Model | Object Classes |\n| :---------------- | :------------------- |\n| | Nova Carter Dock |\n| | Nova Carter |\n| | Pallets |\n| | Forklifts |\n\n| Grocery objects | Object Classes | \n| :----------- | :--------- |\n| | [365 Mac & Cheese](https://www.wholefoodsmarket.com/product/365-by-whole-foods-market-macaroni-cheese-725-oz-b07zpphcj6) |\n| | [365 Facial Tissue](https://www.wholefoodsmarket.com/product/365-by-whole-foods-market-facial-tissue-sustainably-soft-160-count-2-ply-tissue-b07qlyqf58) |\n| Grocery Objects | [365 Peanut Butter](https://www.wholefoodsmarket.com/product/365-by-whole-foods-market-creamy-peanut-butter-no-sugar-added-16-oz-b074h57spt) |\n| | [365 Olive Can](https://www.wholefoodsmarket.com/product/365-by-whole-foods-market-medium-black-olives-pitted-6-oz-b074h7l7d2) |\n| | [365 Hand Sanitizing Wipes](https://www.wholefoodsmarket.com/product/365-by-whole-foods-market-lavender-hand-sanitizing-wipes-40-wipes-b07dft5s3q) |\n| | [Skipjack Tuna in water](https://www.wholefoodsmarket.com/product/seafood-skipjack-tuna-in-water-b07fywzf5c) |","displayName","SyntheticaDETR",["D",1733260471218],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/isaac/models/synthetica_detr","rawImageUrl","https://media.githubusercontent.com/media/NVIDIA-ISAAC-ROS/.github/main/resources/isaac_ngc/syntheticadetr/syntheticadetr_1.0_grasp_objects_ycb4.jpg","canGuestDownload","latestVersionSizeInBytes","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. The training data does not contain human faces, skin color, gender or any PII. Hence, unwanted bias is completely avoided.","explainability","Field|Response\n--|--\nIntended Application(s) & Domain(s): | Learning to detect objects in a scene with prediction confidence suitable for robotics and autonomous machines.\nModel Type: | Object detection model.\nIntended Users: | Developers building and/or customizing robotics applications.\nOutput: | Bounding box coordinates and confidence.\nDescribe how the model works: | Model detects objects in a scene.\nTechnical Limitations: | SyntheticaDETR is not able to accurately detect objects outside the class of objects it was trained on. \nPerformance Metrics: | Mean Average Precision (mAP)\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. \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)): | mean Averaged Precision (mAP).\nModel Application(s): | Object detection for 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. 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These cookies may store a unique ID so that our system will remember you when you return. 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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","nv_co","__uxq412__ses.88c1","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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