CenterPose Model Card - Isaac ROS

Model Overview

CenterPose is a single-stage, keypoint-based method for category-level object pose estimation. It processes unknown object instances within a recognized category using a single RGB image. The pretrained model detects the projections of 3D keypoints, estimates a 6-DoF pose, and regresses the relative 3D bounding cuboid dimensions.

Model Architecture

This model is supported two different types of backbone network as the feature extractor, including DLA34 and FAN-Small-Hybrid. The DLA34 is a standard Convolutional Neural Network (CNN) backbone, while the FAN-Small-Hybrid is a transformer-based classification backbone.

The network architecture processes a re-scaled and padded RGB image. Using the DLA34/FAN-Small-Hybrid feature extractor combined with an upsampling module, the network outputs three distinct heads that predict the 2D bounding box, projections of 3D bounding box keypoints, and cuboid dimensions.

Training

This model uses a single-stage network to make all predictions and is trained using the CenterPose entry point since TAO 5.2 in November 2023. The training algorithm optimizes the network to minimize both the focal loss and the l1 loss for all keypoints and cuboid dimensions.

Training Data

The CenterPose model was trained on the Objectron dataset, a newly introduced benchmark for monocular RGB category-level 6-DoF object pose estimation. This dataset comprises 15k annotated video clips, totaling over 4M annotated frames. Every category is marked with a 3D bounding cuboid that indicates the object's position, orientation relative to the camera, and the cuboid's dimensions.

For training and evaluation purposes, we extracted frames by temporally downsampling the original videos to 15 fps.

For symmetric objects, such as bottles, we produced multiple ground truth labels during the training phase by rotating them N times around their symmetry axis.

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Category	# of Training Videos	# of Training Images	# of Testing Videos	# of Testing Images
Cereal Box	1,288	22,024	321	5,428
Bottle	1,542	26,090	385	6,442

Accuracy and Performance

Evaluation Data

The performance of the CenterPose model during inference was evaluated using the test samples from each category in the official dataset release. These frames, originally high-resolution images of 600x800 pixels, were resized to 512x512 pixels before being processed by the CenterPose model.

Methodology and KPI

Accuracy was determined using a 3D intersection-over-union (IoU) criterion with a threshold greater than 0.5.\nThe 2D MPE (mean pixel projection error) metric calculates the average normalized distance between the projections of 3D bounding box keypoints from both the estimated and ground truth poses.\nFor viewpoint estimation, we present the average precision (AP) for azimuth and elevation with thresholds of 15° and 10° degrees, respectively.

For symmetric object categories, like bottles, we rotated the estimated bounding box around the symmetry axis N times (where N = 100) and assessed the prediction in relation to each rotated instance. The results reflect the instance that either maximizes the 3D IoU or minimizes the 2D pixel projection error.

Evaluation data key performance indicators (KPIs) are provided in the table below. The evaluation of the pretrained models was based on FP32 precision.

\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

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Category	Backbone Architecture	3D IoU ↑	2D MPE ↓	AP @ 15° Azimuth Error ↑	AP @ 10° Elevation Error ↑
Cereal Box	DLA34	0.8131	0.039	0.9273	0.9350
Cereal Box	FAN-Small-Hybrid	0.8290	0.036	0.9418	0.9514
Bottle	DLA34	0.7939	0.040	0.9703	0.8933
Bottle	FAN-Small-Hybrid	0.8187	0.039	0.9820	0.9056

Real-time Inference Performance

The inference performance of the provided CenterPose model is evaluated at both FP16 and FP32 precisions. The model's input resolution is 512x512 pixels. The performance assessment was conducted using trtexec on a range of devices: Orin Nano 8GB, Orin NX 16GB, Jetson AGX Orin 64GB, A2, A30, A100, H100, L4, L40, and Tesla T4. In the table, \"BS\" stands for \"batch size.\"

The performance data presented pertains solely to model inference. The end-to-end performance, when integrated with streaming video data, may vary slightly due to potential bottlenecks in both hardware and software.

\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\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

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Models (FP16)	Devices	Latency ↓ (ms, BS=1)	Images per Second ↑ (BS=1)
CenterPose - DLA34	Orin Nano 8GB	52.19	19.16
CenterPose - DLA34	Orin NX 16GB	36.05	27.74
CenterPose - DLA34	AGX Orin 64GB	17.53	57.04
CenterPose - DLA34	A2	63.75	15.69
CenterPose - DLA34	A30	17.40	57.46
CenterPose - DLA34	A100	12.17	82.16
CenterPose - DLA34	H100	9.45	105.84
CenterPose - DLA34	L4	24.58	40.68
CenterPose - DLA34	L40	9.37	106.70
CenterPose - DLA34	Tesla T4	41.20	24.27
CenterPose - DLA34	RTX 4060Ti	21.70	46.10
CenterPose - FAN-Small-Hybrid	Orin Nano 8GB	125.94	7.94
CenterPose - FAN-Small-Hybrid	Orin NX 16GB	88.12	11.35
CenterPose - FAN-Small-Hybrid	AGX Orin 64GB	35.68	28.03
CenterPose - FAN-Small-Hybrid	A2	172.55	5.80
CenterPose - FAN-Small-Hybrid	A30	37.41	26.73
CenterPose - FAN-Small-Hybrid	A100	20.01	49.99
CenterPose - FAN-Small-Hybrid	H100	13.11	76.26
CenterPose - FAN-Small-Hybrid	L4	53.52	18.69
CenterPose - FAN-Small-Hybrid	L40	17.65	56.65
CenterPose - FAN-Small-Hybrid	Tesla T4	102.33	9.77
CenterPose - FAN-Small-Hybrid	RTX 4060Ti	48.10	20.80

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Models (FP32)	Devices	Latency ↓ (ms, BS=1)	Images per Second ↑ (BS=1)
CenterPose - DLA34	Orin Nano 8GB	80.81	12.37
CenterPose - DLA34	Orin NX 16GB	55.67	17.96
CenterPose - DLA34	AGX Orin 64GB	25.36	39.44
CenterPose - DLA34	A2	155.43	6.43
CenterPose - DLA34	A30	40.83	24.49
CenterPose - DLA34	A100	25.17	39.74
CenterPose - DLA34	H100	16.28	61.42
CenterPose - DLA34	L4	49.99	20.00
CenterPose - DLA34	L40	18.64	53.63
CenterPose - DLA34	Tesla T4	101.42	9.86
CenterPose - DLA34	RTX 4060Ti	43.40	23.10
CenterPose - FAN-Small-Hybrid	Orin Nano 8GB	208.25	4.80
CenterPose - FAN-Small-Hybrid	Orin NX 16GB	144.80	6.91
CenterPose - FAN-Small-Hybrid	AGX Orin 64GB	60.29	16.59
CenterPose - FAN-Small-Hybrid	A2	450.28	2.22
CenterPose - FAN-Small-Hybrid	A30	113.68	8.80
CenterPose - FAN-Small-Hybrid	A100	58.62	17.06
CenterPose - FAN-Small-Hybrid	H100	30.90	32.36
CenterPose - FAN-Small-Hybrid	L4	149.66	6.68
CenterPose - FAN-Small-Hybrid	L40	53.02	18.86
CenterPose - FAN-Small-Hybrid	Tesla T4	294.14	3.40
CenterPose - FAN-Small-Hybrid	RTX 4060Ti	128.90	7.80

How to use this model

These models are designed for use with NVIDIA platforms, including Jetson and x86_64 with a dGPU. To use this model in an inference pipeline in ROS 2, please consult Isaac ROS Pose Estimation.

Note: Please use FP32 for the FAN-small-hybrid backbone CenterPose model if the TensorRT version is lower than 8.6.

Output image

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Cereal Box	Bottle

Limitations

Very Small Objects

The CenterPose model was trained to identify dominant objects in the camera view. As a result, it might not detect objects who appear very small with respect to the camera view.

Occluded Objects

If objects are occluded or truncated to the extent that less than 40% of the object remains visible, the CenterPose model may not recognize them. The model can detect partially occluded objects as long as the majority of the object remains visible. Heavily occluded objects might compromise detection accuracy.

Dark-lighting, Distortion Images, Blurry Images

The CenterPose model was trained on RGB images taken under good lighting conditions and captured by a pinhole camera. Consequently, images shot in poor lighting or those exhibiting distortion or blur might not yield optimal detection results.

Model versions

Deployable: decrypted ONNX files, inferencable on Isaac ROS pipeline.

References

Citations

\n
Daquan Zhou, Zhiding Yu, Enze Xie, Chaowei Xiao, Anima Anandkumar, Jiashi Feng and Jose M. Alvarez. \"Understanding The Robustness in Vision Transformers\". International Conference on Machine Learning (ICML). 2022.
\n
\n
Fisher Yu, Dequan Wang, Evan Shelhamer and Trevor Darrell. \"Deep Layer Aggregation\". Conference on Computer Vision and Pattern Recognition. 2018.
\n
\n
Adel Ahmadyan, Liangkai Zhang, Artsiom Ablavatski, Jianing Wei, Matthias Grundmann. \"Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations\". IEEE Conference on Computer Vision and Pattern Recognition. 2021.
\n

License

License to use these models is covered by the NVIDIA Open Model License. By downloading the model, you accept the terms and conditions of these licenses.

Ethical Considerations

NVIDIA CenterPose model estimates the object pose. However, no additional information such as people and other distractors in the background are inferred.\nTraining and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies.\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.

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It processes unknown object instances within a recognized category using a single RGB image. The pretrained model detects the projections of 3D keypoints, estimates a 6-DoF pose, and regresses the relative 3D bounding cuboid dimensions.\n\n## Model Architecture \n\nThis model is supported two different types of backbone network as the feature extractor, including [DLA34](https://arxiv.org/pdf/1707.06484.pdf) and [FAN-Small-Hybrid](https://arxiv.org/abs/2204.12451). The DLA34 is a standard Convolutional Neural Network (CNN) backbone, while the FAN-Small-Hybrid is a transformer-based classification backbone.\n\nThe network architecture processes a re-scaled and padded RGB image. Using the DLA34/FAN-Small-Hybrid feature extractor combined with an upsampling module, the network outputs three distinct heads that predict the 2D bounding box, projections of 3D bounding box keypoints, and cuboid dimensions.\n\n## Training \n\nThis model uses a single-stage network to make all predictions and is trained using the CenterPose entry point since [TAO](https://developer.nvidia.com/tao-toolkit) 5.2 in November 2023. The training algorithm optimizes the network to minimize both the focal loss and the l1 loss for all keypoints and cuboid dimensions.\n\n### Training Data \n\nThe CenterPose model was trained on the [Objectron dataset](https://github.com/google-research-datasets/Objectron), a newly introduced benchmark for monocular RGB category-level 6-DoF object pose estimation. This dataset comprises 15k annotated video clips, totaling over 4M annotated frames. Every category is marked with a 3D bounding cuboid that indicates the object's position, orientation relative to the camera, and the cuboid's dimensions.\n\nFor training and evaluation purposes, we extracted frames by temporally downsampling the original videos to 15 fps.\n\nFor symmetric objects, such as bottles, we produced multiple ground truth labels during the training phase by rotating them N times around their symmetry axis.\n\n| Category | # of Training Videos | # of Training Images | # of Testing Videos | # of Testing Images | \n| -- | -- | -- | -- | -- | \n| Cereal Box | 1,288 | 22,024 | 321 | 5,428 |\n| Bottle | 1,542 | 26,090 | 385 | 6,442 |\n\n## Accuracy and Performance\n\n### Evaluation Data \n\nThe performance of the CenterPose model during inference was evaluated using the test samples from each category in the official dataset release. These frames, originally high-resolution images of 600x800 pixels, were resized to 512x512 pixels before being processed by the CenterPose model.\n\n### Methodology and KPI\n\nAccuracy was determined using a 3D intersection-over-union (IoU) criterion with a threshold greater than 0.5. \nThe 2D MPE (mean pixel projection error) metric calculates the average normalized distance between the projections of 3D bounding box keypoints from both the estimated and ground truth poses. \nFor viewpoint estimation, we present the average precision (AP) for azimuth and elevation with thresholds of 15° and 10° degrees, respectively. \n\nFor symmetric object categories, like bottles, we rotated the estimated bounding box around the symmetry axis N times (where N = 100) and assessed the prediction in relation to each rotated instance. The results reflect the instance that either maximizes the 3D IoU or minimizes the 2D pixel projection error.\n\nEvaluation data key performance indicators (KPIs) are provided in the table below. The evaluation of the pretrained models was based on FP32 precision.\n\n| Category | Backbone Architecture | 3D IoU ↑| 2D MPE ↓ | AP @ 15° Azimuth Error ↑ | AP @ 10° Elevation Error ↑ |\n| ------- | ------- | ------- | ------- | ------- | ------- | \n| Cereal Box | DLA34 | 0.8131 | 0.039 | 0.9273 | 0.9350|\n| Cereal Box | FAN-Small-Hybrid | 0.8290 | 0.036 | 0.9418 | 0.9514|\n| Bottle | DLA34 | 0.7939 | 0.040 | 0.9703 | 0.8933|\n| Bottle | FAN-Small-Hybrid | 0.8187 | 0.039 | 0.9820 | 0.9056|\n\n### Real-time Inference Performance\n\nThe inference performance of the provided CenterPose model is evaluated at both FP16 and FP32 precisions. The model's input resolution is 512x512 pixels. The performance assessment was conducted using **trtexec** on a range of devices: Orin Nano 8GB, Orin NX 16GB, Jetson AGX Orin 64GB, A2, A30, A100, H100, L4, L40, and Tesla T4. In the table, \"BS\" stands for \"batch size.\"\n\nThe performance data presented pertains solely to model inference. The end-to-end performance, when integrated with streaming video data, may vary slightly due to potential bottlenecks in both hardware and software.\n\n| Models (FP16) | Devices | Latency ↓ (ms, BS=1) | Images per Second ↑ (BS=1) | \n| ---- | ---- | ---- | ---- | \n| CenterPose - DLA34 | Orin Nano 8GB | 52.19 | 19.16 |\n| CenterPose - DLA34 | Orin NX 16GB | 36.05 | 27.74 |\n| CenterPose - DLA34 | AGX Orin 64GB | 17.53 | 57.04 |\n| CenterPose - DLA34 | A2 | 63.75 | 15.69 |\n| CenterPose - DLA34 | A30 | 17.40 | 57.46 |\n| CenterPose - DLA34 | A100 | 12.17 | 82.16 |\n| CenterPose - DLA34 | H100 | 9.45 | 105.84 |\n| CenterPose - DLA34 | L4 | 24.58 | 40.68 |\n| CenterPose - DLA34 | L40 | 9.37 | 106.70 |\n| CenterPose - DLA34 | Tesla T4 | 41.20 | 24.27 |\n| CenterPose - DLA34 | RTX 4060Ti | 21.70 | 46.10 |\n| CenterPose - FAN-Small-Hybrid | Orin Nano 8GB | 125.94 | 7.94 |\n| CenterPose - FAN-Small-Hybrid | Orin NX 16GB | 88.12 | 11.35 |\n| CenterPose - FAN-Small-Hybrid | AGX Orin 64GB | 35.68 | 28.03 |\n| CenterPose - FAN-Small-Hybrid | A2 | 172.55 | 5.80 |\n| CenterPose - FAN-Small-Hybrid | A30 | 37.41 | 26.73 |\n| CenterPose - FAN-Small-Hybrid | A100 | 20.01 | 49.99 |\n| CenterPose - FAN-Small-Hybrid | H100 | 13.11 | 76.26 |\n| CenterPose - FAN-Small-Hybrid | L4 | 53.52 | 18.69 |\n| CenterPose - FAN-Small-Hybrid | L40 | 17.65 | 56.65 |\n| CenterPose - FAN-Small-Hybrid | Tesla T4 | 102.33 | 9.77 |\n| CenterPose - FAN-Small-Hybrid | RTX 4060Ti | 48.10 | 20.80 |\n\n| Models (FP32) | Devices | Latency ↓ (ms, BS=1) | Images per Second ↑ (BS=1) | \n| ---- | ---- | ---- | ---- | \n| CenterPose - DLA34 | Orin Nano 8GB | 80.81 | 12.37 |\n| CenterPose - DLA34 | Orin NX 16GB | 55.67 | 17.96 |\n| CenterPose - DLA34 | AGX Orin 64GB | 25.36 | 39.44 |\n| CenterPose - DLA34 | A2 | 155.43 | 6.43 |\n| CenterPose - DLA34 | A30 | 40.83 | 24.49 |\n| CenterPose - DLA34 | A100 | 25.17 | 39.74 |\n| CenterPose - DLA34 | H100 | 16.28 | 61.42 |\n| CenterPose - DLA34 | L4 | 49.99 | 20.00 |\n| CenterPose - DLA34 | L40 | 18.64 | 53.63 |\n| CenterPose - DLA34 | Tesla T4 | 101.42 | 9.86 |\n| CenterPose - DLA34 | RTX 4060Ti | 43.40 | 23.10 |\n| CenterPose - FAN-Small-Hybrid | Orin Nano 8GB | 208.25 | 4.80 |\n| CenterPose - FAN-Small-Hybrid | Orin NX 16GB | 144.80 | 6.91 |\n| CenterPose - FAN-Small-Hybrid | AGX Orin 64GB | 60.29 | 16.59 |\n| CenterPose - FAN-Small-Hybrid | A2 | 450.28 | 2.22 |\n| CenterPose - FAN-Small-Hybrid | A30 | 113.68 | 8.80 |\n| CenterPose - FAN-Small-Hybrid | A100 | 58.62 | 17.06 |\n| CenterPose - FAN-Small-Hybrid | H100 | 30.90 | 32.36 |\n| CenterPose - FAN-Small-Hybrid | L4 | 149.66 | 6.68 |\n| CenterPose - FAN-Small-Hybrid | L40 | 53.02 | 18.86 |\n| CenterPose - FAN-Small-Hybrid | Tesla T4 | 294.14 | 3.40 |\n| CenterPose - FAN-Small-Hybrid | RTX 4060Ti | 128.90 | 7.80 |\n\n## How to use this model \n\nThese models are designed for use with NVIDIA platforms, including Jetson and x86_64 with a dGPU. To use this model in an inference pipeline in ROS 2, please consult [Isaac ROS Pose Estimation](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_pose_estimation/tree/main).\n\n- Note: Please use FP32 for the FAN-small-hybrid backbone CenterPose model if the TensorRT version is lower than 8.6.\n\n### Output image\n\n| **Cereal Box** | **Bottle** |\n| ------ | ------ |\n|

|\"\n\n## Limitations \n\n### Very Small Objects\n\nThe CenterPose model was trained to identify dominant objects in the camera view. As a result, it might not detect objects who appear very small with respect to the camera view.\n\n### Occluded Objects\n\nIf objects are occluded or truncated to the extent that less than 40% of the object remains visible, the CenterPose model may not recognize them. The model can detect partially occluded objects as long as the majority of the object remains visible. Heavily occluded objects might compromise detection accuracy.\n\n### Dark-lighting, Distortion Images, Blurry Images\n\nThe CenterPose model was trained on RGB images taken under good lighting conditions and captured by a pinhole camera. Consequently, images shot in poor lighting or those exhibiting distortion or blur might not yield optimal detection results.\n\n## Model versions\n\n- Deployable: decrypted [ONNX](https://onnx.ai/) files, inferencable on Isaac ROS pipeline.\n\n## References\n\n### Citations \n\n- Daquan Zhou, Zhiding Yu, Enze Xie, Chaowei Xiao, Anima Anandkumar, Jiashi Feng and Jose M. Alvarez. \"Understanding The Robustness in Vision Transformers\". *International Conference on Machine Learning (ICML).* 2022.\n\n- Fisher Yu, Dequan Wang, Evan Shelhamer and Trevor Darrell. \"Deep Layer Aggregation\". *Conference on Computer Vision and Pattern Recognition.* 2018.\n\n- Adel Ahmadyan, Liangkai Zhang, Artsiom Ablavatski, Jianing Wei, Matthias Grundmann. \"Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations\". *IEEE Conference on Computer Vision and Pattern Recognition*. 2021.\n\n## License \n\nLicense to use these models is covered by the NVIDIA Open Model License. By downloading the model, you accept the terms and conditions of these [licenses](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\n## Ethical Considerations \n\nNVIDIA CenterPose model estimates the object pose. However, no additional information such as people and other distractors in the background are inferred.\nTraining and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies.\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","CenterPose - ISAAC Ros",["D",1731434064837],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/centerpose_ros","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",79361021,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","| Field | Response |\n| -- | -- |\n|Participation considerations from adversely impacted groups [(protected classes)](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None of the Above |\n| Measures taken to mitigate against unwanted bias: | No measures taken to mitigate against unwanted bias.|","explainability","| Field | Response |\n| -- | -- |\n| Intended Application(s) & Domain(s): | Detects the projections of 3D keypoints, estimates a 6-Degrees of Freedom (DoF) pose |\n| Model Type: | Category-Level Object Pose Estimation |\n| Intended Users: | Developers working in smart spaces, retail, and industrial applications |\n| Output: | 3D Bounding-Box, Cuboid Dimensions and 6 Degrees of Freedom (DoF) |\n| Describe how the model works: | Single RGB imagedetects the projections of 3D keypoints, estimates a 6-DoF pose, and regresses the relative 3D bounding cuboid dimensions within a single RGB image. |\n| Technical Limitations: | Model should not be used in low-light, low contrast lighting conditions, with warped or motion-induced images, and with very small and very large objects. Model accuracy may vary with occlusion or truncation. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | 3D Intersection-over-Union (IoU), 2D MPE (Mean Pixel Projection Error), Average Precision (AP) Azimuth Error, Average Precision (AP) Elevation Error |\n| Potential Known Risks: | This model may fail to detect projections of 3D keypoints. |\n| Licensing: | [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) |","privacy","| Field | Response |\n| -- | -- |\n| Generatable or reverse engineerable personally-identifiable information (PII)? | None |\n| Protected classes used to create this model? | Not Applicable (No PII) |\n| Was consent obtained for any PII used? | Not Applicable (No PII) |\n| How often is dataset reviewed? | \tBefore Release |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | No |\n| If PII collected for the development of the model, was it collected directly by NVIDIA? | No, all data was collected by Google Research Organization. |\n| If PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects?\t| Not Applicable |\n| If PII collected for the development of this AI model, was it minimized to only what was required? | Not Applicable |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data. |\n| Applicable NVIDIA Privacy Policy\t| [https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/) |","safetyAndSecurity","| Field | Response |\n| -- | -- |\n| Model Application(s): | Category-Level Object Pose Estimation |\n| Describe the life-critical application (if present). | None: Not within Operational Design Domain |\n| Use Case Restrictions: | Abide by [https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/\"](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/). |\n| Describe access restrictions (if any): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. |","featureLabels",[],"displayLabels",[266,267,268,269,270,271,272,273,274,275,276,277,278,279],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_229":317,"_281":318,"_282":319,"_284":285},{"_229":313,"_281":314,"_282":315,"_284":285},{"_229":311,"_281":311,"_282":312,"_284":285},{"_229":309,"_281":309,"_282":310,"_284":285},{"_229":306,"_281":307,"_282":308,"_284":285},{"_229":302,"_281":303,"_282":304,"_284":285},{"_229":300,"_281":300,"_282":301,"_284":285},{"_229":298,"_281":298,"_282":299,"_284":285},{"_229":296,"_281":296,"_282":297,"_284":285},{"_229":294,"_281":294,"_282":295,"_284":285},{"_229":291,"_281":291,"_282":292,"_284":285},{"_229":289,"_281":289,"_282":290,"_284":285},{"_229":287,"_281":287,"_282":288,"_284":285},{"_229":280,"_281":280,"_282":283,"_284":285},"Transfer Learning Toolkit","value","filter",{"_286":285,"_281":280},"category","label","field","Transfer Learning",{"_286":285,"_281":287},"TLT",{"_286":285,"_281":289},"TAO Toolkit",{"_286":293,"_281":291},"framework","TAO",{"_286":285,"_281":294},"Smart Infrastructure",{"_286":285,"_281":296},"Smart City",{"_286":285,"_281":298},"NSPECT-FY5H-V6CO",{"_286":285,"_281":300},"Metropolis","pltfm_metropolis",{"_286":305,"_281":303},"platform","DeepStream","pltfm_deepstream",{"_286":305,"_281":307},"Deep Learning",{"_286":285,"_281":309},"CV",{"_286":285,"_281":311},"Computer Vision","soln_computer_vision",{"_286":316,"_281":314},"technology","AI","soln_ai",{"_286":316,"_281":318},{"_107":1494,"_109":1495},"ngcApps",[1447],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1428":1429,"_1430":1431,"_1432":1433,"_1434":1435,"_1436":331,"_139":355,"_1437":1438,"_1439":55,"_1440":86,"_1441":86,"_1442":1443,"_1444":86,"_1445":1446},"amplitude",{"_1425":1426,"_1028":-7,"_1427":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1419":1420,"_1421":1422,"_1423":1424},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1303":169,"_1304":1305,"_1306":1307,"_1308":1309},"announcements",[],"consentState",{"_394":1293,"_1294":86,"_1295":30,"_1296":30,"_1297":1298},"consentConfig",{"_376":377,"_378":379,"_380":381,"_382":383,"_384":385,"_386":387,"_388":389,"_390":391,"_392":393,"_394":395},"showContactAdmin",1783956335010,[],[],[369,370,371,372,373,374],{"_44":59,"_46":70,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":68,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":375,"_54":55},4,"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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If you do not allow these cookies, then some or all of these services may not function properly.",[667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687],[632,633,634,635,636],{"_435":527,"_229":527,"_168":169,"_408":658},{"_435":493,"_229":493,"_168":169,"_408":653},{"_435":646,"_229":646,"_168":169,"_408":647},{"_435":642,"_229":642,"_168":169,"_408":643},{"_435":637,"_229":637,"_168":169,"_408":638},"nvcrm.my.site.com",[639],{"_78":640,"_441":637,"_442":30,"_168":641},"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",[644],{"_78":645,"_441":642,"_442":30,"_168":476},"edge","github.com",[648,649],{"_78":652,"_441":646,"_442":30,"_168":651},{"_78":650,"_441":646,"_442":30,"_168":651},"logged_in","This domain is owned by Github. 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