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CenterPose Model Card

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 supports two different types of backbone networks 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 distinct heads that predict the projections of 3D bounding box keypoints and relative cuboid dimensions. After detecting objects in the image space, the estimated relative cuboid dimensions enables you to utilize robust, off-the-shelf PnP algorithms for the pose estimation.

Training

This model uses a single-stage network to make all predictions and is trained using the CenterPose entry point since the TAO 5.2 version. 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 is comprised of 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.

Objects are from the following eight categories: bikes, books, bottles, cameras, cereal boxes, chairs, laptops, and shoes.

For training purposes, frames are extracted by temporally downsampling the original videos to 15 fps. For symmetric objects, such as bottles, multiple ground truth labels are produced 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
Bike	375	8,396	94	2,090
Book	1,618	31,609	404	7,885
Bottle	1,542	26,090	385	6,442
Camera	652	12,758	163	3,283
Cereal Box	1,288	22,024	321	5,428
Chair	1,555	27,608	388	6,695
Laptop	1,179	26,462	294	6,608
Shoe	1,693	30,515	423	7,859

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, the average precision (AP) is presented for azimuth and elevation with thresholds of 15° and 10° degrees, respectively.

For symmetric object categories, like bottles, the estimated bounding box is rotated 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.

For evaluation purposes, frames are exctacted by temporally downsampling the original videos to 15 fps. Evaluation data key performance indicators (KPIs) are provided in the following table. The evaluation of the pretrained models was based on FP32 precision.

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Category	Backbone Architecture	3D IoU ↑	2D MPE ↓	AP @ 15° Azimuth Error ↑	AP @ 10° Elevation Error ↑
Bike	DLA34	0.6271	0.0941	0.8667	0.8990
Book	DLA34	0.5678	0.0637	0.7380	0.8503
Bottle	DLA34	0.7939	0.0402	0.9703	0.8933
Camera	DLA34	0.7213	0.0574	0.8058	0.8402
Cereal Box	DLA34	0.8131	0.0392	0.9273	0.9350
Chair	DLA34	0.8495	0.0610	0.8787	0.8954
Laptop	DLA34	0.7398	0.0475	0.8751	0.7871
Shoe	DLA34	0.6711	0.0445	0.6838	0.7915
Bike	FAN-Small-Hybrid	0.6489	0.0885	0.8974	0.9418
Book	FAN-Small-Hybrid	0.6286	0.0597	0.7869	0.8835
Bottle	FAN-Small-Hybrid	0.8187	0.0396	0.9820	0.9056
Camera	FAN-Small-Hybrid	0.7384	0.0581	0.8360	0.8662
Cereal Box	FAN-Small-Hybrid	0.8290	0.0365	0.9418	0.9514
Chair	FAN-Small-Hybrid	0.8552	0.0587	0.8893	0.9153
Laptop	FAN-Small-Hybrid	0.7003	0.0477	0.8917	0.7714
Shoe	FAN-Small-Hybrid	0.6902	0.0442	0.7188	0.8276

Real-Time Inference Performance

The inference performance of the provided CenterPose model is evaluated at FP16 and FP32 precisions. The model's input resolution is 512x512 pixels. The performance assessment was conducted using trtexec on a range of devices including: 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 and the post-processing, might vary slightly due to potential bottlenecks in hardware and software.

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Models (FP16)	Devices	Latency ↓ (ms, BS=1)	Images per Second ↑ (BS=1)	Latency ↓ (ms, BS=8)	Images per Second ↑ (BS=8)
CenterPose - DLA34	Orin Nano 8GB	52.19	19.16	167.85 (BS=4)	23.83 (BS=4)
CenterPose - DLA34	Orin NX 16GB	36.05	27.74	115.91 (BS=4)	34.51 (BS=4)
CenterPose - DLA34	AGX Orin 64GB	17.53	57.04	89.28	89.60
CenterPose - DLA34	A2	63.75	15.69	121.59	65.79
CenterPose - DLA34	A30	17.40	57.46	28.39	281.76
CenterPose - DLA34	A100	12.17	82.16	17.38	460.24
CenterPose - DLA34	H100	9.45	105.84	12.23	654.23
CenterPose - DLA34	L4	24.58	40.68	47.44	168.62
CenterPose - DLA34	L40	9.37	106.70	16.65	480.62
CenterPose - DLA34	Tesla T4	41.20	24.27	75.96	105.32
CenterPose - FAN-Small-Hybrid	Orin Nano 8GB	125.94	7.94	482.63 (BS=4)	8.29 (BS=4)
CenterPose - FAN-Small-Hybrid	Orin NX 16GB	88.12	11.35	333.54 (BS=4)	11.99 (BS=4)
CenterPose - FAN-Small-Hybrid	AGX Orin 64GB	35.68	28.03	262.80	30.44
CenterPose - FAN-Small-Hybrid	A2	172.55	5.80	315.35	25.37
CenterPose - FAN-Small-Hybrid	A30	37.41	26.73	63.66	125.66
CenterPose - FAN-Small-Hybrid	A100	20.01	49.99	32.30	247.64
CenterPose - FAN-Small-Hybrid	H100	13.11	76.26	19.48	410.74
CenterPose - FAN-Small-Hybrid	L4	53.52	18.69	111.70	71.62
CenterPose - FAN-Small-Hybrid	L40	17.65	56.65	36.43	219.60
CenterPose - FAN-Small-Hybrid	Tesla T4	102.33	9.77	187.63	42.64

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Models (FP32)	Devices	Latency ↓ (ms, BS=1)	Images per Second ↑ (BS=1)	Latency ↓ (ms, BS=8)	Images per Second ↑ (BS=8)
CenterPose - DLA34	Orin Nano 8GB	80.81	12.37	277.50 (BS=4)	14.41 (BS=4)
CenterPose - DLA34	Orin NX 16GB	55.67	17.96	192.77 (BS=4)	20.75 (BS=4)
CenterPose - DLA34	AGX Orin 64GB	25.36	39.44	150.98	52.99
CenterPose - DLA34	A2	155.43	6.43	307.11	26.05
CenterPose - DLA34	A30	40.83	24.49	74.51	107.37
CenterPose - DLA34	A100	25.17	39.74	41.79	191.44
CenterPose - DLA34	H100	16.28	61.42	25.00	320.03
CenterPose - DLA34	L4	49.99	20.00	97.38	82.15
CenterPose - DLA34	L40	18.64	53.63	33.47	239.01
CenterPose - DLA34	Tesla T4	101.42	9.86	188.23	42.50
CenterPose - FAN-Small-Hybrid	Orin Nano 8GB	208.25	4.80	832.21 (BS=4)	4.81 (BS=4)
CenterPose - FAN-Small-Hybrid	Orin NX 16GB	144.80	6.91	572.54 (BS=4)	6.99 (BS=4)
CenterPose - FAN-Small-Hybrid	AGX Orin 64GB	60.29	16.59	494.86	16.17
CenterPose - FAN-Small-Hybrid	A2	450.28	2.22	872.87	9.17
CenterPose - FAN-Small-Hybrid	A30	113.68	8.80	215.12	37.19
CenterPose - FAN-Small-Hybrid	A100	58.62	17.06	109.13	73.31
CenterPose - FAN-Small-Hybrid	H100	30.90	32.36	54.41	147.02
CenterPose - FAN-Small-Hybrid	L4	149.66	6.68	309.32	25.86
CenterPose - FAN-Small-Hybrid	L40	53.02	18.86	108.91	73.46
CenterPose - FAN-Small-Hybrid	Tesla T4	294.14	3.40	579.57	13.80

How to Use This Model

These models are designed for use with NVIDIA hardware and software. For hardware, the models are compatible with any NVIDIA GPU, including NVIDIA Jetson devices. For software, the models are specifically designed for the Train Adapt Optimize (TAO) Toolkit, or TensorRT.

The primary application of these models is to estimate an object's pose from a single RGB image. They can identify the objects in photos, given the right image decoding and pre-processing procedures.

For training, the models are intended for use with the Train Adapt Optimize (TAO) Toolkit and the your dataset. It's possible to train high-fidelity models tailored to new use cases. The Jupyter Notebook, which is included in the TAO Container, can be used for re-training.

Furthermore, these models are designed for deployment to edge devices using the TensorRT. TAO Triton apps offers capabilities to construct efficient image analytic pipelines. These pipelines can capture, decode, and pre-process data before executing inference.

Input

RGB Image of dimensions: 512 X 512 X 3 (W x H x C).\nChannel Ordering of the Input: NCHW, where N = Batch Size, C = number of channels (3), H = Height of images (512), W = Width of the images (512)

Output

The model outputs three distinct results that predict the projections of 3D bounding box keypoints, the cuboid relative dimensions, and the 6-DoF (Degrees of Freedom). The +y is up (aligned with the gravity, green line); The +x follows right hand rule (red line); The +z is the front face (blue line).

Output Image

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Bike	Book	Bottle	Camera

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Cereal Box	Chair	Laptop	Shoes

Instructions to Deploy the Model with TAO-Deploy

To use these models for object pose estimation, see the following YAML as a template for the dataset and inference sections of the experiment spec file when testing the images.

Convert the pretrained ONNX models into a TRT engine using TensorRT.
Set up the engine file path and configure the correct intrinsic matrix in the experiment spec file.
Run the TAO-Deploy inference pipeline to get the visualization results.
Note: The CenterPose model with FAN-Small-Hybrid only supports the FP16 when the TensorRT version >= 8.6.

For more information on the experiment spec file and usage instruction, see the TAO Toolkit User Guide.

dataset:\n  inference_data: /path/to/inference/images/folder\n  num_classes: 1\n  batch_size: 1\n  workers: 4\n\ninference:\n  trt_engine: /path/to/engine.trt\n  visualization_threshold: 0.3\n  principle_point_x: 298.3\n  principle_point_y: 392.1\n  focal_length_x: 651.2\n  focal_length_y: 651.2\n  skew: 0.0\n  axis_size: 0.5\n  use_pnp: True\n  save_json: True\n  save_visualization: True\n  opencv: True\n

Instructions to Deploy the Model with Triton Inference Server

To create the entire end-to-end inference application, deploy this model with Triton Inference Server. NVIDIA Triton Inference Server is an open-source inference serving software that helps standardize model deployment and execution and delivers fast and scalable AI in production. Triton supports direct integration of this model into the server and inference from a client.

To deploy this model with Triton Inference Server and end-to-end inference from images, please refer to the TAO Triton apps.

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 that 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 might 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_v1.0: decrypted ONNX files, inferencable on Isaac ROS pose estimation pipeline, TAO Triton apps, and TAO Toolkit.

References

Citations

\n
Yunzhi Lin, Jonathan Tremblay, Stephen Tyree, Patricio Vela, and Stan Birchfield. \"Single-Stage Keypoint-based Category-level Object Pose Estimation from an RGB Image.\" IEEE International Conference on Robotics and Automation (ICRA). 2022.
\n
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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
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Fisher Yu, Dequan Wang, Evan Shelhamer and Trevor Darrell. \"Deep Layer Aggregation\". Conference on Computer Vision and Pattern Recognition. 2018.
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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.
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Technical Blogs

Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - Part 1 | Part 2.
Learn how to train real-time license plate detection and recognition app with TAO and DeepStream.
Model accuracy is extremely important, learn how you can achieve state of the art accuracy for classification and object detection models using TAO.
Learn how to train an instance segmentation model using MaskRCNN with TAO.
Read the technical tutorial on how PeopleNet model can be trained with custom data using the Transfer Learning Toolkit.
Learn how to train and deploy real-time intelligent video analytics apps and services using the DeepStream SDK.

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.\nThe training 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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\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 distinct heads that predict the projections of 3D bounding box keypoints and relative cuboid dimensions. After detecting objects in the image space, the estimated relative cuboid dimensions enables you to utilize robust, off-the-shelf PnP algorithms for the pose estimation.\n\n## Training \n\nThis model uses a single-stage network to make all predictions and is trained using the CenterPose entry point since the [TAO](https://developer.nvidia.com/tao-toolkit) 5.2 version. 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 is comprised of 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\nObjects are from the following eight categories: bikes, books, bottles, cameras, cereal boxes, chairs, laptops, and shoes.\n\nFor training purposes, frames are extracted by temporally downsampling the original videos to 15 fps. For symmetric objects, such as bottles, multiple ground truth labels are produced 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| Bike | 375 | 8,396 | 94 | 2,090 |\n| Book | 1,618\t | 31,609 | 404 | 7,885\t|\n| Bottle | 1,542 | 26,090 | 385 | 6,442 |\n| Camera | 652\t | 12,758 | 163 | 3,283 |\n| Cereal Box | 1,288 | 22,024 | 321 | 5,428 |\t\t\n| Chair | 1,555\t | 27,608\t | 388\t | 6,695 |\n| Laptop | 1,179\t | 26,462\t | 294\t | 6,608 |\n| Shoe | 1,693\t | 30,515\t | 423\t | 7,859 |\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, the average precision (AP) is presented for azimuth and elevation with thresholds of 15° and 10° degrees, respectively. \n\nFor symmetric object categories, like bottles, the estimated bounding box is rotated 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\nFor evaluation purposes, frames are exctacted by temporally downsampling the original videos to 15 fps. Evaluation data key performance indicators (KPIs) are provided in the following table. 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| Bike | DLA34 | 0.6271 | 0.0941 | 0.8667 | 0.8990 |\n| Book | DLA34 | 0.5678 | 0.0637 | 0.7380 | 0.8503 |\n| Bottle | DLA34 | 0.7939 | 0.0402 | 0.9703 | 0.8933 |\n| Camera | DLA34 | 0.7213 | 0.0574 | 0.8058 | 0.8402 |\n| Cereal Box | DLA34 | 0.8131 | 0.0392 | 0.9273 | 0.9350 |\n| Chair | DLA34 | 0.8495 | 0.0610 | 0.8787 | 0.8954 |\n| Laptop | DLA34 | 0.7398 | 0.0475 | 0.8751 | 0.7871 |\n| Shoe | DLA34 | 0.6711 | 0.0445 | 0.6838 | 0.7915 |\n| Bike | FAN-Small-Hybrid | 0.6489 | 0.0885 | 0.8974 | 0.9418 |\n| Book | FAN-Small-Hybrid | 0.6286 | 0.0597 | 0.7869 | 0.8835 |\n| Bottle | FAN-Small-Hybrid | 0.8187 | 0.0396 | 0.9820 | 0.9056 |\n| Camera | FAN-Small-Hybrid | 0.7384 | 0.0581 | 0.8360 | 0.8662 |\n| Cereal Box | FAN-Small-Hybrid | 0.8290 | 0.0365 | 0.9418 | 0.9514 |\n| Chair | FAN-Small-Hybrid | 0.8552 | 0.0587 | 0.8893 | 0.9153 |\n| Laptop | FAN-Small-Hybrid | 0.7003 | 0.0477 | 0.8917 | 0.7714 |\n| Shoe | FAN-Small-Hybrid | 0.6902 | 0.0442 | 0.7188 | 0.8276 |\n\n### Real-Time Inference Performance\n\nThe inference performance of the provided CenterPose model is evaluated at FP16 and FP32 precisions. The model's input resolution is 512x512 pixels. The performance assessment was conducted using **trtexec** on a range of devices including: 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 and the post-processing, might vary slightly due to potential bottlenecks in hardware and software.\n\n| Models (FP16) | Devices | Latency ↓ (ms, BS=1) | Images per Second ↑ (BS=1) | Latency ↓ (ms, BS=8) | Images per Second ↑ (BS=8) | \n| ---- | ---- | ---- | ---- | ---- | ---- | \n| CenterPose - DLA34 | Orin Nano 8GB | 52.19 | 19.16 | 167.85 (BS=4) | 23.83 (BS=4) |\n| CenterPose - DLA34 | Orin NX 16GB | 36.05 | 27.74 | 115.91 (BS=4) | 34.51 (BS=4) |\n| CenterPose - DLA34 | AGX Orin 64GB | 17.53 | 57.04 | 89.28 | 89.60 |\n| CenterPose - DLA34 | A2 | 63.75 | 15.69 | 121.59 | 65.79 |\n| CenterPose - DLA34 | A30 | 17.40 | 57.46 | 28.39 | 281.76 |\n| CenterPose - DLA34 | A100 | 12.17 | 82.16 | 17.38 | 460.24 |\n| CenterPose - DLA34 | H100 | 9.45 | 105.84 | 12.23 | 654.23 |\n| CenterPose - DLA34 | L4 | 24.58 | 40.68 | 47.44 | 168.62 |\n| CenterPose - DLA34 | L40 | 9.37 | 106.70 | 16.65 | 480.62 |\n| CenterPose - DLA34 | Tesla T4 | 41.20 | 24.27 | 75.96 | 105.32 |\n| CenterPose - FAN-Small-Hybrid | Orin Nano 8GB | 125.94 | 7.94 | 482.63 (BS=4) | 8.29 (BS=4) | \n| CenterPose - FAN-Small-Hybrid | Orin NX 16GB | 88.12 | 11.35 | 333.54 (BS=4) | 11.99 (BS=4) |\n| CenterPose - FAN-Small-Hybrid | AGX Orin 64GB | 35.68 | 28.03 | 262.80 | 30.44 |\n| CenterPose - FAN-Small-Hybrid | A2 | 172.55 | 5.80 | 315.35 | 25.37 |\n| CenterPose - FAN-Small-Hybrid | A30 | 37.41 | 26.73 | 63.66 | 125.66 |\n| CenterPose - FAN-Small-Hybrid | A100 | 20.01 | 49.99 | 32.30 | 247.64 |\n| CenterPose - FAN-Small-Hybrid | H100 | 13.11 | 76.26 | 19.48 | 410.74 |\n| CenterPose - FAN-Small-Hybrid | L4 | 53.52 | 18.69 | 111.70 | 71.62 |\n| CenterPose - FAN-Small-Hybrid | L40 | 17.65 | 56.65 | 36.43 | 219.60 |\n| CenterPose - FAN-Small-Hybrid | Tesla T4 | 102.33 | 9.77 | 187.63 | 42.64 |\n\n| Models (FP32) | Devices | Latency ↓ (ms, BS=1) | Images per Second ↑ (BS=1) | Latency ↓ (ms, BS=8) | Images per Second ↑ (BS=8) | \n| ---- | ---- | ---- | ---- | ---- | ---- | \n| CenterPose - DLA34 | Orin Nano 8GB | 80.81 | 12.37 | 277.50 (BS=4) | 14.41 (BS=4) |\n| CenterPose - DLA34 | Orin NX 16GB | 55.67 | 17.96 | 192.77 (BS=4) | 20.75 (BS=4) |\n| CenterPose - DLA34 | AGX Orin 64GB | 25.36 | 39.44 | 150.98 | 52.99 |\n| CenterPose - DLA34 | A2 | 155.43 | 6.43 | 307.11 | 26.05 |\n| CenterPose - DLA34 | A30 | 40.83 | 24.49 | 74.51 | 107.37 |\n| CenterPose - DLA34 | A100 | 25.17 | 39.74 | 41.79 | 191.44 |\n| CenterPose - DLA34 | H100 | 16.28 | 61.42 | 25.00 | 320.03 |\n| CenterPose - DLA34 | L4 | 49.99 | 20.00 | 97.38 | 82.15 |\n| CenterPose - DLA34 | L40 | 18.64 | 53.63 | 33.47 | 239.01 |\n| CenterPose - DLA34 | Tesla T4 | 101.42 | 9.86 | 188.23 | 42.50 |\n| CenterPose - FAN-Small-Hybrid | Orin Nano 8GB | 208.25 | 4.80 | 832.21 (BS=4) | 4.81 (BS=4) |\n| CenterPose - FAN-Small-Hybrid | Orin NX 16GB | 144.80 | 6.91 | 572.54 (BS=4) | 6.99 (BS=4) |\n| CenterPose - FAN-Small-Hybrid | AGX Orin 64GB | 60.29 | 16.59 | 494.86 | 16.17 |\n| CenterPose - FAN-Small-Hybrid | A2 | 450.28 | 2.22 | 872.87 | 9.17 |\n| CenterPose - FAN-Small-Hybrid | A30 | 113.68 | 8.80 | 215.12 | 37.19 |\n| CenterPose - FAN-Small-Hybrid | A100 | 58.62 | 17.06 | 109.13 | 73.31 |\n| CenterPose - FAN-Small-Hybrid | H100 | 30.90 | 32.36 | 54.41 | 147.02 |\n| CenterPose - FAN-Small-Hybrid | L4 | 149.66 | 6.68 | 309.32 | 25.86 |\n| CenterPose - FAN-Small-Hybrid | L40 | 53.02 | 18.86 | 108.91 | 73.46 |\n| CenterPose - FAN-Small-Hybrid | Tesla T4 | 294.14 | 3.40 | 579.57 | 13.80 |\n\n## How to Use This Model \n\nThese models are designed for use with NVIDIA hardware and software. For hardware, the models are compatible with any NVIDIA GPU, including NVIDIA Jetson devices. For software, the models are specifically designed for the [Train Adapt Optimize (TAO) Toolkit](https://developer.nvidia.com/tao-toolkit), or [TensorRT](https://developer.nvidia.com/tensorrt).\n\nThe primary application of these models is to estimate an object's pose from a single RGB image. They can identify the objects in photos, given the right image decoding and pre-processing procedures.\n\nFor training, the models are intended for use with the Train Adapt Optimize (TAO) Toolkit and the your dataset. It's possible to train high-fidelity models tailored to new use cases. The Jupyter Notebook, which is included in the [TAO Container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit), can be used for re-training.\n\nFurthermore, these models are designed for deployment to edge devices using the TensorRT. [TAO Triton apps](https://github.com/NVIDIA-AI-IOT/tao-toolkit-triton-apps) offers capabilities to construct efficient image analytic pipelines. These pipelines can capture, decode, and pre-process data before executing inference.\n\n### Input\n\nRGB Image of dimensions: 512 X 512 X 3 (W x H x C). \nChannel Ordering of the Input: NCHW, where N = Batch Size, C = number of channels (3), H = Height of images (512), W = Width of the images (512)\n\n### Output\n\nThe model outputs three distinct results that predict the projections of 3D bounding box keypoints, the cuboid relative dimensions, and the 6-DoF (Degrees of Freedom). The `+y` is up (aligned with the gravity, green line); The `+x` follows right hand rule (red line); The `+z` is the front face (blue line).\n\n### Output Image\n\n| **Bike** | **Book** | **Bottle** | **Camera** |\n| ------ | ------ | ------ | ------ |\n|

|\n\n| **Cereal Box** | **Chair** | **Laptop** | **Shoes** |\n| ------ | ------ | ------ | ------ |\n|

|\n\n### Instructions to Deploy the Model with TAO-Deploy\n\nTo use these models for object pose estimation, see the following YAML as a template for the dataset and inference sections of the experiment spec file when testing the images.\n\n* Convert the pretrained ONNX models into a TRT engine using TensorRT.\n* Set up the engine file path and configure the correct intrinsic matrix in the experiment spec file.\n* Run the TAO-Deploy inference pipeline to get the visualization results. \n* Note: The CenterPose model with FAN-Small-Hybrid only supports the FP16 when the TensorRT version >= 8.6. \n\nFor more information on the experiment spec file and usage instruction, see the [TAO Toolkit User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\n```yaml\ndataset:\n inference_data: /path/to/inference/images/folder\n num_classes: 1\n batch_size: 1\n workers: 4\n\ninference:\n trt_engine: /path/to/engine.trt\n visualization_threshold: 0.3\n principle_point_x: 298.3\n principle_point_y: 392.1\n focal_length_x: 651.2\n focal_length_y: 651.2\n skew: 0.0\n axis_size: 0.5\n use_pnp: True\n save_json: True\n save_visualization: True\n opencv: True\n```\n\n### Instructions to Deploy the Model with Triton Inference Server\n\nTo create the entire end-to-end inference application, deploy this model with [Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server). NVIDIA Triton Inference Server is an open-source inference serving software that helps standardize model deployment and execution and delivers fast and scalable AI in production. Triton supports direct integration of this model into the server and inference from a client.\n\nTo deploy this model with [Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server) and end-to-end inference from images, please refer to the [TAO Triton apps](https://github.com/NVIDIA-AI-IOT/tao-toolkit-triton-apps). \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 that 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 might 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_v1.0**: decrypted [ONNX](https://onnx.ai/) files, inferencable on [Isaac ROS pose estimation](https://github.com/NVIDIA-ISAAC-ROS/isaac_ros_pose_estimation) pipeline, [TAO Triton apps](https://github.com/NVIDIA-AI-IOT/tao-toolkit-triton-apps), and [TAO Toolkit](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\n## References\n\n### Citations \n\n- Yunzhi Lin, Jonathan Tremblay, Stephen Tyree, Patricio Vela, and Stan Birchfield. \"Single-Stage Keypoint-based Category-level Object Pose Estimation from an RGB Image.\" *IEEE International Conference on Robotics and Automation (ICRA)*. 2022.\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### Technical Blogs \n\n- Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - [Part 1](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-1) | [Part 2](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-2).\n- Learn how to train [real-time license plate detection and recognition app](https://developer.nvidia.com/blog/creating-a-real-time-license-plate-detection-and-recognition-app) with TAO and DeepStream.\n- Model accuracy is extremely important, learn how you can achieve [state of the art accuracy for classification and object detection models](https://developer.nvidia.com/blog/preparing-state-of-the-art-models-for-classification-and-object-detection-with-tao-toolkit/) using TAO.\n- Learn how to train [an instance segmentation model using MaskRCNN with TAO](https://developer.nvidia.com/blog/training-instance-segmentation-models-using-maskrcnn-on-tao-toolkit/).\n- Read the technical tutorial on how [PeopleNet model can be trained with custom data using the Transfer Learning Toolkit](https://devblogs.nvidia.com/training-custom-pretrained-models-using-tlt/).\n- Learn how to [train and deploy real-time intelligent video analytics apps and services using the DeepStream SDK](https://devblogs.nvidia.com/building-iva-apps-using-deepstream-5.0/).\n\n### Suggested Reading \n\n- More information on about TAO Toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/tao-toolkit).\n- Read the [TAO Quick Start](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html) guide and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html).\n- If you have any questions or feedback, see the discussions on the [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17).\n- Deploy your model on the edge using DeepStream. Learn more about [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk).\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.\nThe training 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",["D",1731434054064],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/centerpose","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",343890379,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[247,248,249,250,251,252,253,254,255,256,257,258,259,260],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_214":298,"_262":299,"_263":300,"_265":266},{"_214":294,"_262":295,"_263":296,"_265":266},{"_214":292,"_262":292,"_263":293,"_265":266},{"_214":290,"_262":290,"_263":291,"_265":266},{"_214":287,"_262":288,"_263":289,"_265":266},{"_214":283,"_262":284,"_263":285,"_265":266},{"_214":281,"_262":281,"_263":282,"_265":266},{"_214":279,"_262":279,"_263":280,"_265":266},{"_214":277,"_262":277,"_263":278,"_265":266},{"_214":275,"_262":275,"_263":276,"_265":266},{"_214":272,"_262":272,"_263":273,"_265":266},{"_214":270,"_262":270,"_263":271,"_265":266},{"_214":268,"_262":268,"_263":269,"_265":266},{"_214":261,"_262":261,"_263":264,"_265":266},"Transfer Learning Toolkit","value","filter",{"_267":266,"_262":261},"category","label","field","Transfer Learning",{"_267":266,"_262":268},"TLT",{"_267":266,"_262":270},"TAO Toolkit",{"_267":274,"_262":272},"framework","TAO",{"_267":266,"_262":275},"Smart Infrastructure",{"_267":266,"_262":277},"Smart City",{"_267":266,"_262":279},"NSPECT-FY5H-V6CO",{"_267":266,"_262":281},"Metropolis","pltfm_metropolis",{"_267":286,"_262":284},"platform","DeepStream","pltfm_deepstream",{"_267":286,"_262":288},"Deep Learning",{"_267":266,"_262":290},"CV",{"_267":266,"_262":292},"Computer Vision","soln_computer_vision",{"_267":297,"_262":295},"technology","AI","soln_ai",{"_267":297,"_262":299},{"_107":1277,"_109":1278},"ngcApps",[1230],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1211":1212,"_1213":1214,"_1215":1216,"_1217":1218,"_1219":312,"_138":338,"_1220":1221,"_1222":55,"_1223":86,"_1224":86,"_1225":1226,"_1227":86,"_1228":1229},"amplitude",{"_1208":1209,"_850":-7,"_1210":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1202":1203,"_1204":1205,"_1206":1207},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787555894353,"1.12.0","defaultSearchSuggestions",{"_1090":166,"_1091":1092,"_1093":1094,"_1095":1096},"announcements",[],"consentState",{"_376":1080,"_1081":86,"_1082":30,"_1083":30,"_1084":1085},"consentConfig",{"_358":359,"_360":361,"_362":363,"_364":365,"_366":367,"_368":369,"_370":371,"_372":373,"_374":375,"_376":377},"showContactAdmin",1787555894354,[],[],[352,353,354,355,356,357],{"_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":102,"_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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