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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.

\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	# 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

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

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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","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",[264,265,266,267,268,269,270,271,272,273,274,275,276,277],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_228":315,"_279":316,"_280":317,"_282":283},{"_228":311,"_279":312,"_280":313,"_282":283},{"_228":309,"_279":309,"_280":310,"_282":283},{"_228":307,"_279":307,"_280":308,"_282":283},{"_228":304,"_279":305,"_280":306,"_282":283},{"_228":300,"_279":301,"_280":302,"_282":283},{"_228":298,"_279":298,"_280":299,"_282":283},{"_228":296,"_279":296,"_280":297,"_282":283},{"_228":294,"_279":294,"_280":295,"_282":283},{"_228":292,"_279":292,"_280":293,"_282":283},{"_228":289,"_279":289,"_280":290,"_282":283},{"_228":287,"_279":287,"_280":288,"_282":283},{"_228":285,"_279":285,"_280":286,"_282":283},{"_228":278,"_279":278,"_280":281,"_282":283},"Transfer Learning Toolkit","value","filter",{"_284":283,"_279":278},"category","label","field","Transfer Learning",{"_284":283,"_279":285},"TLT",{"_284":283,"_279":287},"TAO Toolkit",{"_284":291,"_279":289},"framework","TAO",{"_284":283,"_279":292},"Smart Infrastructure",{"_284":283,"_279":294},"Smart City",{"_284":283,"_279":296},"NSPECT-FY5H-V6CO",{"_284":283,"_279":298},"Metropolis","pltfm_metropolis",{"_284":303,"_279":301},"platform","DeepStream","pltfm_deepstream",{"_284":303,"_279":305},"Deep Learning",{"_284":283,"_279":307},"CV",{"_284":283,"_279":309},"Computer Vision","soln_computer_vision",{"_284":314,"_279":312},"technology","AI","soln_ai",{"_284":314,"_279":316},{"_106":1491,"_108":1492},"ngcApps",[1444],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1425":1426,"_1427":1428,"_1429":1430,"_1431":1432,"_1433":329,"_138":353,"_1434":1435,"_1436":55,"_1437":85,"_1438":85,"_1439":1440,"_1441":85,"_1442":1443},"amplitude",{"_1422":1423,"_1025":-7,"_1424":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1416":1417,"_1418":1419,"_1420":1421},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1300":168,"_1301":1302,"_1303":1304,"_1305":1306},"announcements",[],"consentState",{"_391":1290,"_1291":85,"_1292":30,"_1293":30,"_1294":1295},"consentConfig",{"_373":374,"_375":376,"_377":378,"_379":380,"_381":382,"_383":384,"_385":386,"_387":388,"_389":390,"_391":392},"showContactAdmin",1784205662184,[],[],[367,368,369,370,371,372],{"_44":59,"_46":69,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":67,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":101,"_50":-5,"_52":-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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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. 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.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. 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.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","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. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. It is a temporary cookie used to determine if the browser accepts cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","login_session","bm_ss","This cookie is used to help protect websites from automated bot traffic.","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. 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.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. Usually used to maintain an anonymised user session by the server.","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","ApplicationGatewayAffinityCORS","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure all requests from a user during the session are sent to the same server.","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.Supports Amplitude product analytics.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","AMP_TLDTEST","c_code","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a one year lifespan and contains no personal information.","default","

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