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

Field	Response
Participation considerations from adversely impacted groups (protected classes) in model design and testing:	Gender, Skin Color
Measures taken to mitigate against unwanted bias:	Developed customized dataset that included video from persons of different skin colors in different geographic settings.

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PeopleNet Model Card

Model Overview

The Peoplenet model detects persons, bags, and faces in an image. This model is ready for commercial use.

References

Citations

Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: CVPR. (2016)
Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks, In: CVPR. (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: CVPR (2015)

Using TAO Pre-trained Models

Get TAO Container
Get other Purpose-built models from NGC model registry:\n
- TrafficCamNet
- DashCamNet
- FaceDetectIR
- VehicleMakeNet
- VehicleTypeNet
\n

Model Architecture

Architecture Type: Convolution Neural Network (CNN)
\nNetwork Architecture: DetectNet_v2 + ResNet34 (Feature Extractor)

This model is based on NVIDIA DetectNet_v2 detector with ResNet34 as feature extractor. This architecture, also known as GridBox object detection, uses bounding-box regression on a uniform grid on the input image. Gridbox system divides an input image into a grid which predicts four normalized bounding-box parameters (xc, yc, w, h) and confidence value per output class.

The raw normalized bounding-box and confidence detections needs to be post-processed by a clustering algorithm such as DBSCAN or NMS to produce final bounding-box coordinates and category labels.

Input:

Input Type(s): Images
\nInput Format(s): Red, Green, Blue (RGB)
\nInput Parameters: 4D
\nOther Properties Related to Input: RGB Fixed Resolution: 960 X 544 X 3 (W x H x C) Channel Ordering of the Input: NCHW, where N = Batch Size, C = number of channels (3), H = Height of images (544), W = Width of the images (960) Input scale: 1/255.0 Mean subtraction: None; No minimum bit depth, alpha, or gamma.

Input image

Output:

Output Type(s): Label(s), Bounding-Box(es), Confidence Scores
\nOutput Format: Label: Text String(s); Bounding Box: (x-coordinate, y-coordinate, width, height), Confidence Scores: Floating Point
\nOther Properties Related to Output: Category Label(s): Bag, Face, Person, Bounding Box Coordinates, Confidence Scores

Output image

Software Integration:

Runtime Engine(s):

TAO 5.1
DeepStream 6.1 or later

Supported Hardware Architecture Compatibility:

Ampere
Jetson
Hopper
Lovelace
Pascal
Turing

Preferred Operating System(s):

Linux
Linux 4 Tegra

Model versions

trainable_v2.5 - Unpruned ResNet34 based pre-trained floating-point model. Intended for transfer learning.
pruned_v2.3 - Pruned ResNet34 floating point deployment model intended for applications with limited computational budget.
pruned_quantized_v2.3 – Pruned ResNet34 INT8 deployment models including deployments on DLA. Contains calibration cache and can be used to run inference on GPU as well as Jetson AGX Xavier or Xavier NX DLA. Intended for applications with limited computational budget.
deployable_quantized_v2.5 – Unpruned ResNet34 deployment models including floating point and INT8 deployments on GPU and DLA. Contains calibration cache and can be used to run inference on GPU as well as Jetson AGX Xavier or Xavier NX DLA. Intended for applications requiring best accuracy.

Training

Data Collection Method by dataset\n
- Automatic/Sensors
\n
Labeling Method by dataset:\n
- Human
\n

This model was trained using the DetectNet_v2 entrypoint in TAO. The training algorithm optimizes the network to minimize the localization and confidence loss for the objects. The training is carried out in two phases. In the first phase, the network is trained without regularization. In the second phase the network is retrained using quantization-aware training (QAT).

Training Data Properties

Internal, proprietary dataset in excess of 7.6 million images of more than 71 million people. The training dataset consists of a mix of cameras, camera heights, crowd-density, and field-of view (FOV) taken from a camera. Approximately half of the training data consisted of images captured in an indoor office environment. The camera is typically set up at approximately 10 feet height, 45-degree angle and has close field-of-view. This was done to improve accuracy of the models for convenience-store retail analytics use-case. We have also added approximately 500 thousand images with low-density scenes with people extending their hands and feet to improve the performance for use-cases where person object detection is followed by pose-estimation.

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	Training	Dataset	Object	Distribution
Category	Number of Images	Number of Persons	Number of Bags	Number of Faces
Natural	4804552	23085430	8061920	10786381
Rotated	5746323	19930535	7234679	10094039
Broadcast	566408	566408	0	358518
Broadcast Rotated	369895	369895	0	59104
Blended	24841	26041	11225	0
Blended Rotated	24335	24523	9707	0
Simulation	27417	368914	0	92916
Total	7656570	41334979	12280764	18354191

Training Data Ground-truth Labeling Guidelines

The training dataset is created by labeling ground-truth bounding-boxes and categories by human labellers. Following guidelines were used while labelling the training data for NVIDIA PeopleNet model. If you are looking to re-train with your own dataset, please follow the guideline below for highest accuracy.

PeopleNet project labelling guidelines:

\n
All objects that fall under one of the three classes (person, face, bag) in the image and are larger than the smallest bounding-box limit for the corresponding class (height >= 10px OR width >= 10px @1920x1080) are labeled with the appropriate class label.
\n
\n
If a person is carrying an object please mark the bounding-box to include the carried object as long as it doesn’t affect the silhouette of the person. For example, exclude a rolling bag if they are pulling it behind them and are distinctly visible as separate object. But include a backpack, purse etc. that do not alter the silhouette of the pedestrian significantly.
\n
\n
Occlusion: For partially occluded objects that do not belong a person class and are visible approximately 60% or are marked as visible objects with bounding box around visible part of the object. These objects are marked as partially occluded. Objects under 60% visibility are not annotated.
\n
\n
Occlusion for person class: If an occluded person’s head and shoulders are visible and the visible height is approximately 20% or more, then these objects are marked by the bounding box around the visible part of the person object. If the head and shoulders are not visible please follow the Occlusion guidelines in item 3 above.
\n
\n
Truncation: For an object other than a person that is at the edge of the frame with visibility of 60% or more visible are marked with the truncation flag for the object.
\n
\n
Truncation for person class: If a truncated person’s head and shoulders are visible and the visible height is approximately 20% or more mark the bounding box around the visible part of the person object. If the head and shoulders are not visible please follow the Truncation guidelines in item 5 above.
\n
\n
Each frame is not required to have an object.
\n

Performance

Evaluation Data Properties

Data Collection Method by dataset\n
- Automatic/Sensors
\n
Labeling Method by dataset:\n
- Human
\n

90000 images from an Internal, proprietary dataset in excess of 7.6 million images of more than 71 million people. The training dataset consists of a mix of cameras, camera heights, crowd-density, and field-of view (FOV) taken from a camera. Approximately half of the training data consisted of images captured in an indoor office environment. The camera is typically set up at approximately 10 feet height, 45-degree angle and has close field-of-view. This was done to improve accuracy of the models for convenience-store retail analytics use-case. We have also added approximately 500 thousand images with low-density scenes with people extending their hands and feet to improve the performance for use-cases where person object detection is followed by pose-estimation.

Methodology and KPI

The true positives, false positives, false negatives are calculated using intersection-over-union (IOU) criterion greater than 0.5. In addition, we have also added KPI with IOU criterion of greater than 0.8 for low-density and extended-hand sequences where tight bounding box is a requirement for subsequent human pose estimation algorithms. The KPI for the evaluation data are reported in the table below. Model is evaluated based on precision, recall and accuracy.

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Network	PeopleNet	V2.6	FP16	PeopleNet	V2.6	INT8
Content	Precision	Recall	Accuracy	Precision	Recall	Accuracy
Generic	91.82	88.60	82.17	92.97	85.33	80.21
Office	95.64	91.92	88.22	96.08	89.48	86.33
Café	93.38	86.97	81.92	94.62	81.81	78.18
People (IOU > 0.8)	80.41	78.13	65.71	78.71	73.90	61.68
Extended-hands (IOU > 0.8)	93.98	86.82	82.25	94.41	86.80	82.55

Real-time Inference Performance

The inference is run on the provided unpruned model at INT8 precision. The inference performance is run using trtexec on Jetson AGX Xavier, Xavier NX, Orin, Orin NX and NVIDIA T4, and Ampere GPU. The Jetson devices are running at Max-N configuration for maximum GPU frequency. The performance shown here is the inference only performance. The end-to-end performance with streaming video data might slightly vary depending on other bottlenecks in the hardware and software.

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Model Arch	Version	Inference Resolution	Precision	Xavier NX		AGX Xavier
PeopleNet	Unpruned	960x544x3	INT8	GPU	DLA1+DLA2	GPU	DLA1+DLA2
Resnet34	v2.6			79	46	136	58

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Model Arch	Version	Inference Resolution	Precision	Orin NX		AGX Orin
PeopleNet	Unpruned	960x544x3	INT8	GPU	DLA1+DLA2	GPU	DLA1+DLA2
Resnet34	v2.6			140	70	421	104

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Model Arch	Version	Inference Resolution	Precision	T4	A100	A30	A10	A2
PeopleNet	Unpruned	960x544x3	INT8	GPU	GPU	GPU	GPU	GPU
Resnet34	v2.6			465	3291	1603	1007	231

How to use this model

These models need to be used with NVIDIA Hardware and Software. For Hardware, the models can run on any NVIDIA GPU including NVIDIA Jetson devices. These models can only be used with Train Adapt Optimize (TAO) Toolkit, DeepStream SDK or TensorRT.

The primary use case intended for these models is detecting people in a color (RGB) image. The model can be used to detect people from photos and videos by using appropriate video or image decoding and pre-processing. As a secondary use case the model can also be used to detect bags and faces from images or videos. However, these additional classes are not the main intended use for these models.

There are two flavors of these models:

trainable (unpruned)
deployable (unpruned quantized)

The trainable or unpruned models intended for training using TAO Toolkit and the user's own dataset. This can provide high fidelity models that are adapted to the use case. The Jupyter notebook available as a part of TAO container can be used to re-train.

The deployable_quantized models share the same structure as the trainable or unpruned model, however, these models have been trained by employing Quantization Aware Training and is intended for int-8 deployment.

The trainable (unpruned), deployable (unpruned quantized) models are encrypted and will only operate with the following key:

Model load key: tlt_encode

Please make sure to use this as the key for all TAO commands that require a model load key.

Instructions to use unpruned model with TAO

In order, to use these models as a pretrained weights for transfer learning, please use the snippet below as template for the model_config component of the experiment spec file to train a DetectNet_v2 model. For more information on the experiment spec file, please refer to the TAO Toolkit User Guide.

For ResNet34

model_config {\n  num_layers: 34\n  pretrained_model_file: \"/path/to/the/model.tlt\"\n  use_batch_norm: true\n  objective_set {\n    bbox {\n      scale: 35.0\n      offset: 0.5\n    }\n    cov {\n    }\n  }\n  training_precision {\n    backend_floatx: FLOAT32\n  }\n  arch: \"resnet\"\n  all_projections: true\n}\n

Instructions to deploy these models with DeepStream

To create the entire end-to-end video analytics application, deploy this model with DeepStream SDK. DeepStream SDK is a streaming analytics toolkit to accelerate deployment of AI-based video analytics applications. The pruned model included here can be integrated directly into deepstream by following the instructions mentioned\nbelow.

Run the default deepstream-app included in the DeepStream docker, by simply executing the commands below.

## Download Model:\n\nmkdir -p $HOME/peoplenet && \\\nwget https://api.ngc.nvidia.com/v2/models/nvidia/tao/peoplenet/versions/pruned_v2.1/files/resnet34_peoplenet_pruned.etlt \\\n-O $HOME/peoplenet/resnet34_peoplenet_pruned.etlt\n\n## Run Application\n\nxhost +\nsudo docker run --gpus all -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v $HOME:/opt/nvidia/deepstream/deepstream-5.1/samples/models/tlt_pretrained_models \\\n-w /opt/nvidia/deepstream/deepstream-5.1/samples/configs/tlt_pretrained_models nvcr.io/nvidia/deepstream:5.1-21.02-samples \\\ndeepstream-app -c deepstream_app_source1_peoplenet.txt\n

\n
Install deepstream on your local host and run the deepstream-app.
\n
Download and install DeepStream SDK. The installation instructions for DeepStream are provided in DeepStream development guide. The config files for the purpose-built models are located in:
\n
```
/opt/nvidia/deepstream/deepstream-5.1/samples/configs/tlt_pretrained_models\n
```
\n
/opt/nvidia/deepstream is the default DeepStream installation directory. This path will be different if you are installing in a different directory.
\n
You will need 2 config files and 1 label file. These files are provided in the tlt_pretrained_models directory.
\n
```
deepstream_app_source1_peoplenet.txt - Main config file for DeepStream app\nconfig_infer_primary_peoplenet.txt - File to configure inference settings\nlabels_peoplenet.txt - Label file with 3 classes\n
```
\n
Key Parameters in config_infer_primary_peoplenet.txt
\n
```
tlt-model-key=\ntlt-encoded-model=\nlabelfile-path=\nint8-calib-file=\ninput-dims=\nnum-detected-classes=\n
```
\n
Run deepstream-app:
\n
```
deepstream-app -c deepstream_app_source1_peoplenet.txt\n
```
\n
Documentation to deploy with DeepStream is provided in \"Deploying to DeepStream\" chapter of TAO User Guide.
\n

Limitations

Very Small Objects

NVIDIA PeopleNet model were trained to detect objects larger than 10x10 pixels. Therefore it may not be able to detect objects that are smaller than 10x10 pixels.

Occluded Objects

When objects are occluded or truncated such that less than 20% of the object is visible, they may not be detected by the PeopleNet model. For people class objects, the model will detect occluded people as long as head and shoulders are visible. However if the person’s head and/or shoulders are not visible, the object might not be detected unless more than 60% of the person is visible.

Dark-lighting, Monochrome or Infrared Camera Images

The PeopleNet model were trained on RGB images in good lighting conditions. Therefore, images captured in dark lighting conditions or a monochrome image or IR camera image may not provide good detection results.

Warped and Blurry Images

The PeopleNet models were not trained on fish-eye lense cameras or moving cameras. Therefore, the models may not perform well for warped images and images that have motion-induced or other blur.

Face and Bag class

Although bag and face class are included in the model, the accuracy of these classes will be much lower than people class. Some re-training will be required on these classes to improve accuracy.

Technical blogs

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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

License

License to use these models is covered by the Model EULA. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of NVIDIA Open Model License.

Ethical Considerations

NVIDIA PeopleNet model detects faces. However, no additional information such as race, gender, and skin type about the faces is inferred.

Training and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies.

NVIDIA’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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This model is ready for commercial use. \n\n### References\n#### Citations \n\n- Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: CVPR. (2016)\n- Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks, In: CVPR. (2014)\n- He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: CVPR (2015)\n\n#### Using TAO Pre-trained Models \n\n- Get [TAO Container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit)\n- Get other Purpose-built models from NGC model registry:\n - [TrafficCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:trafficcamnet)\n - [DashCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:dashcamnet)\n - [FaceDetectIR](https://ngc.nvidia.com/catalog/models/nvidia:tao:facedetectir)\n - [VehicleMakeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehiclemakenet)\n - [VehicleTypeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehicletypenet)\n\n### Model Architecture \n\n**Architecture Type:** Convolution Neural Network (CNN)
\n**Network Architecture:** DetectNet_v2 + ResNet34 (Feature Extractor)\n\nThis model is based on NVIDIA DetectNet_v2 detector with ResNet34 as feature extractor. This architecture, also known as GridBox object detection, uses bounding-box regression on a uniform grid on the input image. Gridbox system divides an input image into a grid which predicts four normalized bounding-box parameters (xc, yc, w, h) and confidence value per output class.\n\nThe raw normalized bounding-box and confidence detections needs to be post-processed by a clustering algorithm such as DBSCAN or NMS to produce final bounding-box coordinates and category labels.\n\n## Input:\n\n**Input Type(s):** Images
\n**Input Format(s):** Red, Green, Blue (RGB)
\n**Input Parameters:** 4D
\n**Other Properties Related to Input:** RGB Fixed Resolution: 960 X 544 X 3 (W x H x C) Channel Ordering of the Input: NCHW, where N = Batch Size, C = number of channels (3), H = Height of images (544), W = Width of the images (960) Input scale: 1/255.0 Mean subtraction: None; No minimum bit depth, alpha, or gamma.\n\n#### Input image\n\n

\n\n## Output:\n\n**Output Type(s):** Label(s), Bounding-Box(es), Confidence Scores
\n**Output Format:** Label: Text String(s); Bounding Box: (x-coordinate, y-coordinate, width, height), Confidence Scores: Floating Point
\n**Other Properties Related to Output:** Category Label(s): Bag, Face, Person, Bounding Box Coordinates, Confidence Scores\n\n#### Output image\n\n

\n\n## Software Integration:\n**Runtime Engine(s):**\n* TAO 5.1
\n* DeepStream 6.1 or later
\n\n**Supported Hardware Architecture Compatibility:**
\n* Ampere
\n* Jetson
\n* Hopper
\n* Lovelace
\n* Pascal
\n* Turing
\n\n**Preferred Operating System(s):**
\n* Linux
\n* Linux 4 Tegra
\n\n### Model versions
\n* **trainable_v2.5** - Unpruned ResNet34 based pre-trained floating-point model. Intended for transfer learning.
\n* **pruned_v2.3** - Pruned ResNet34 floating point deployment model intended for applications with limited computational budget.
\n* **pruned_quantized_v2.3** – Pruned ResNet34 INT8 deployment models including deployments on DLA. Contains calibration cache and can be used to run inference on GPU as well as Jetson AGX Xavier or Xavier NX DLA. Intended for applications with limited computational budget.
\n* **deployable_quantized_v2.5** – Unpruned ResNet34 deployment models including floating point and INT8 deployments on GPU and DLA. Contains calibration cache and can be used to run inference on GPU as well as Jetson AGX Xavier or Xavier NX DLA. Intended for applications requiring best accuracy.
\n\n### Training \n\n- **Data Collection Method by dataset**\n - Automatic/Sensors\n- **Labeling Method by dataset:**\n - Human\n \nThis model was trained using the DetectNet_v2 entrypoint in TAO. The training algorithm optimizes the network to minimize the localization and confidence loss for the objects. The training is carried out in two phases. In the first phase, the network is trained without regularization. In the second phase the network is retrained using quantization-aware training (QAT).\n\n#### Training Data Properties \n\nInternal, proprietary dataset in excess of 7.6 million images of more than 71 million people. The training dataset consists of a mix of cameras, camera heights, crowd-density, and field-of view (FOV) taken from a camera. Approximately half of the training data consisted of images captured in an indoor office environment. The camera is typically set up at approximately 10 feet height, 45-degree angle and has close field-of-view. This was done to improve accuracy of the models for convenience-store retail analytics use-case. We have also added approximately 500 thousand images with low-density scenes with people extending their hands and feet to improve the performance for use-cases where person object detection is followed by pose-estimation. \n\n| | Training | Dataset | Object | Distribution |\n| -- | -- | -- | -- | -- |\n| Category | Number of Images | Number of Persons | Number of Bags | Number of Faces |\n| Natural | 4804552 | 23085430 | 8061920 | 10786381 |\n| Rotated | 5746323 | 19930535 | 7234679 | 10094039 |\n| Broadcast | 566408 | 566408 | 0 | 358518 |\n| Broadcast Rotated | 369895 | 369895 | 0 | 59104 |\n| Blended | 24841 | 26041 | 11225 | 0 |\n| Blended Rotated | 24335 | 24523 | 9707 | 0 |\n| Simulation | 27417 | 368914 | 0 |92916 |\n| Total | 7656570 | 41334979 | 12280764 | 18354191 |\n\n##### Training Data Ground-truth Labeling Guidelines\n\nThe training dataset is created by labeling ground-truth bounding-boxes and categories by human labellers. Following guidelines were used while labelling the training data for NVIDIA PeopleNet model. If you are looking to re-train with your own dataset, please follow the guideline below for highest accuracy.\n\nPeopleNet project labelling guidelines:\n\n1. All objects that fall under one of the three classes (person, face, bag) in the image and are larger than the smallest bounding-box limit for the corresponding class (height >= 10px OR width >= 10px @1920x1080) are labeled with the appropriate class label.\n\n2. If a person is carrying an object please mark the bounding-box to include the carried object as long as it doesn’t affect the silhouette of the person. For example, exclude a rolling bag if they are pulling it behind them and are distinctly visible as separate object. But include a backpack, purse etc. that do not alter the silhouette of the pedestrian significantly.\n\n3. Occlusion: For partially occluded objects that do not belong a person class and are visible approximately 60% or are marked as visible objects with bounding box around visible part of the object. These objects are marked as partially occluded. Objects under 60% visibility are not annotated.\n\n4. Occlusion for person class: If an occluded person’s head and shoulders are visible and the visible height is approximately 20% or more, then these objects are marked by the bounding box around the visible part of the person object. If the head and shoulders are not visible please follow the Occlusion guidelines in item 3 above.\n\n5. Truncation: For an object other than a person that is at the edge of the frame with visibility of 60% or more visible are marked with the truncation flag for the object.\n\n6. Truncation for person class: If a truncated person’s head and shoulders are visible and the visible height is approximately 20% or more mark the bounding box around the visible part of the person object. If the head and shoulders are not visible please follow the Truncation guidelines in item 5 above.\n\n7. Each frame is not required to have an object.\n\n### Performance\n\n#### Evaluation Data Properties \n\n- **Data Collection Method by dataset**\n - Automatic/Sensors\n- **Labeling Method by dataset:**\n - Human\n\n90000 images from an Internal, proprietary dataset in excess of 7.6 million images of more than 71 million people. The training dataset consists of a mix of cameras, camera heights, crowd-density, and field-of view (FOV) taken from a camera. Approximately half of the training data consisted of images captured in an indoor office environment. The camera is typically set up at approximately 10 feet height, 45-degree angle and has close field-of-view. This was done to improve accuracy of the models for convenience-store retail analytics use-case. We have also added approximately 500 thousand images with low-density scenes with people extending their hands and feet to improve the performance for use-cases where person object detection is followed by pose-estimation. \n\n##### Methodology and KPI\n\nThe true positives, false positives, false negatives are calculated using intersection-over-union (IOU) criterion greater than 0.5. In addition, we have also added KPI with IOU criterion of greater than 0.8 for low-density and extended-hand sequences where tight bounding box is a requirement for subsequent human pose estimation algorithms. The KPI for the evaluation data are reported in the table below. Model is evaluated based on precision, recall and accuracy.\n\n| Network | PeopleNet | V2.6 | FP16 | PeopleNet | V2.6 | INT8 |\n| ------- | ------- | -------------- | ---- | ---- | ------------------------ | ---- |\n| Content | Precision | Recall | Accuracy | Precision | Recall | Accuracy |\n| Generic | 91.82 | 88.60 | 82.17 | 92.97 | 85.33 | 80.21 |\n| Office | 95.64 | 91.92 | 88.22 | 96.08 | 89.48 | 86.33 |\n| Café | 93.38 | 86.97 | 81.92 | 94.62 | 81.81 | 78.18 |\n| People (IOU > 0.8) | 80.41 | 78.13 | 65.71 | 78.71 | 73.90 | 61.68 |\n| Extended-hands (IOU > 0.8) | 93.98 | 86.82 | 82.25 | 94.41 | 86.80 | 82.55 |\n\n#### Real-time Inference Performance \n\nThe inference is run on the provided unpruned model at INT8 precision. The inference performance is run using [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/master/samples/trtexec) on Jetson AGX Xavier, Xavier NX, Orin, Orin NX and NVIDIA T4, and Ampere GPU. The Jetson devices are running at Max-N configuration for maximum GPU frequency. The performance shown here is the inference only performance. The end-to-end performance with streaming video data might slightly vary depending on other bottlenecks in the hardware and software.\n\n| Model Arch |Version | Inference Resolution | Precision | Xavier NX | | AGX Xavier | |\n|------------|--------|----------------------|-----------|-----------|-|------------|-|\n|PeopleNet|Unpruned| 960x544x3 | INT8 | GPU | DLA1+DLA2 | GPU | DLA1+DLA2 |\n|Resnet34 | v2.6 | | |79 |46 | 136 | 58|\n\n| Model Arch |Version | Inference Resolution | Precision | Orin NX | | AGX Orin | |\n|------------|--------|----------------------|-----------|---------|-|----------|-|\n|PeopleNet|Unpruned| 960x544x3 | INT8 | GPU | DLA1+DLA2 | GPU | DLA1+DLA2 |\n|Resnet34 | v2.6 | | |140 | 70 | 421 | 104|\n\n| Model Arch |Version | Inference Resolution | Precision |T4|A100|A30|A10|A2|\n|------------|--------|----------------------|-----------|--|----|---|---|--|\n|PeopleNet|Unpruned| 960x544x3 | INT8 |GPU|GPU|GPU|GPU|GPU|\n|Resnet34 | v2.6 | | |465|3291|1603|1007|231|\n\n### How to use this model \n\nThese models need to be used with NVIDIA Hardware and Software. For Hardware, the models can run on any NVIDIA GPU including NVIDIA Jetson devices. These models can only be used with [Train Adapt Optimize (TAO) Toolkit](https://developer.nvidia.com/tao-toolkit), [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) or [TensorRT](https://developer.nvidia.com/tensorrt).\n\nThe primary use case intended for these models is detecting people in a color (RGB) image. The model can be used to detect people from photos and videos by using appropriate video or image decoding and pre-processing. As a secondary use case the model can also be used to detect bags and faces from images or videos. However, these additional classes are not the main intended use for these models.\n\nThere are two flavors of these models:\n\n- trainable (unpruned)\n- deployable (unpruned quantized)\n\nThe `trainable or unpruned` models intended for training using TAO Toolkit and the user's own dataset. This can provide high fidelity models that are adapted to the use case. The Jupyter notebook available as a part of [TAO container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit-tf) can be used to re-train.\n\nThe `deployable_quantized` models share the same structure as the `trainable or unpruned` model, however, these models have been trained by employing Quantization Aware Training and is intended for int-8 deployment.\n\nThe `trainable (unpruned)`, `deployable (unpruned quantized)` models are encrypted and will only operate with the following key:\n\n- Model load key: `tlt_encode`\n\nPlease make sure to use this as the key for all TAO commands that require a model load key.\n\n#### Instructions to use unpruned model with TAO\n\nIn order, to use these models as a pretrained weights for transfer learning, please use the snippet below as template for the `model_config` component of the experiment spec file to train a DetectNet_v2 model. For more information on the experiment spec file, please refer to the [TAO Toolkit User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\n1. For ResNet34\n\n```py\nmodel_config {\n num_layers: 34\n pretrained_model_file: \"/path/to/the/model.tlt\"\n use_batch_norm: true\n objective_set {\n bbox {\n scale: 35.0\n offset: 0.5\n }\n cov {\n }\n }\n training_precision {\n backend_floatx: FLOAT32\n }\n arch: \"resnet\"\n all_projections: true\n}\n```\n\n#### Instructions to deploy these models with DeepStream\n\nTo create the entire end-to-end video analytics application, deploy this model with [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk). DeepStream SDK is a streaming analytics toolkit to accelerate deployment of AI-based video analytics applications. The pruned model included here can be integrated directly into deepstream by following the instructions mentioned\nbelow.\n\n1. Run the default `deepstream-app` included in the DeepStream docker, by simply executing the commands below.\n\n ```sh\n ## Download Model:\n\n mkdir -p $HOME/peoplenet && \\\n wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/peoplenet/versions/pruned_v2.1/files/resnet34_peoplenet_pruned.etlt \\\n -O $HOME/peoplenet/resnet34_peoplenet_pruned.etlt\n\n ## Run Application\n\n xhost +\n sudo docker run --gpus all -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v $HOME:/opt/nvidia/deepstream/deepstream-5.1/samples/models/tlt_pretrained_models \\\n -w /opt/nvidia/deepstream/deepstream-5.1/samples/configs/tlt_pretrained_models nvcr.io/nvidia/deepstream:5.1-21.02-samples \\\n deepstream-app -c deepstream_app_source1_peoplenet.txt\n ```\n2. Install deepstream on your local host and run the deepstream-app.\n\n [Download](https://developer.nvidia.com/deepstream-sdk) and install DeepStream SDK. The installation instructions for DeepStream are provided in [DeepStream development guide](https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html). The config files for the purpose-built models are located in:\n\n ```sh\n /opt/nvidia/deepstream/deepstream-5.1/samples/configs/tlt_pretrained_models\n ```\n\n `/opt/nvidia/deepstream` is the default DeepStream installation directory. This path will be different if you are installing in a different directory.\n\n You will need 2 config files and 1 label file. These files are provided in the `tlt_pretrained_models` directory.\n\n ```sh\n deepstream_app_source1_peoplenet.txt - Main config file for DeepStream app\n config_infer_primary_peoplenet.txt - File to configure inference settings\n labels_peoplenet.txt - Label file with 3 classes\n ```\n\n Key Parameters in `config_infer_primary_peoplenet.txt`\n\n ```sh\n tlt-model-key=\n tlt-encoded-model=\n labelfile-path=\n int8-calib-file=\n input-dims=\n num-detected-classes=\n ```\n\n Run `deepstream-app`:\n\n ```sh\n deepstream-app -c deepstream_app_source1_peoplenet.txt\n ```\n\n Documentation to deploy with DeepStream is provided in \"Deploying to DeepStream\" chapter of [TAO User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\n### Limitations \n\n#### Very Small Objects\n\nNVIDIA PeopleNet model were trained to detect objects larger than 10x10 pixels. Therefore it may not be able to detect objects that are smaller than 10x10 pixels.\n\n#### Occluded Objects\n\nWhen objects are occluded or truncated such that less than 20% of the object is visible, they may not be detected by the PeopleNet model. For people class objects, the model will detect occluded people as long as head and shoulders are visible. However if the person’s head and/or shoulders are not visible, the object might not be detected unless more than 60% of the person is visible.\n\n#### Dark-lighting, Monochrome or Infrared Camera Images\n\nThe PeopleNet model were trained on RGB images in good lighting conditions. Therefore, images captured in dark lighting conditions or a monochrome image or IR camera image may not provide good detection results.\n\n#### Warped and Blurry Images\n\nThe PeopleNet models were not trained on fish-eye lense cameras or moving cameras. Therefore, the models may not perform well for warped images and images that have motion-induced or other blur.\n\n#### Face and Bag class\n\nAlthough bag and face class are included in the model, the accuracy of these classes will be much lower than people class. Some re-training will be required on these classes to improve accuracy.\n\n#### Technical blogs \n\n- [Train like a ‘pro’ without being an AI expert using TAO AutoML](https://developer.nvidia.com/blog/training-like-an-ai-pro-using-tao-automl/)\n- [Create Custom AI models using NVIDIA TAO Toolkit with Azure Machine Learning](https://developer.nvidia.com/blog/creating-custom-ai-models-using-nvidia-tao-toolkit-with-azure-machine-learning/)\n- [Developing and Deploying AI-powered Robots with NVIDIA Isaac Sim and NVIDIA TAO](https://developer.nvidia.com/blog/developing-and-deploying-ai-powered-robots-with-nvidia-isaac-sim-and-nvidia-tao/)\n- Learn endless ways to adapt and supercharge your AI workflows with TAO - [Whitepaper](https://developer.nvidia.com/tao-toolkit-usecases-whitepaper/1-introduction)\n- [Customize Action Recognition with TAO and deploy with DeepStream](https://developer.nvidia.com/blog/developing-and-deploying-your-custom-action-recognition-application-without-any-ai-expertise-using-tao-and-deepstream/)\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\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- [TAO documentation](https://docs.nvidia.com/tao/tao-toolkit/index.html)\n- Read the [TAO getting Started](https://docs.nvidia.com/tao/tao-toolkit/text/tao_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, please refer to the discussions on [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17)\n- Deploy your models for video analytics application using DeepStream. Learn more about [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk)\n- Deploy your models in [Riva](https://developer.nvidia.com/riva) for ConvAI use case.\n\n### License \n\nLicense to use these models is covered by the Model EULA. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\n### Ethical Considerations \n\nNVIDIA PeopleNet model detects faces. However, no additional information such as race, gender, and skin type about the faces is inferred.\n\nTraining and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies.\n\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","PeopleNet",["D",1749754271283],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/peoplenet","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",8805800,"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: | Gender, Skin Color |\n| Measures taken to mitigate against unwanted bias: | Developed customized dataset that included video from persons of different skin colors in different geographic settings.|","explainability","| Field | Response |\n| -- | -- |\n| Intended Application(s) & Domain(s): | Detecting people in smart spaces, retail, industrial applications |\n| Model Type: | Object Detection specified for people detection |\n| Intended Users: | Persons working in smart spaces, retail, and industrial applications. |\n| Output: | Bounding box |\n| Describe how the model works: | People detected when shown in camera field of view |\n| Technical Limitations: | Model should not be used in low-light, low contrast lighting condition and with very small and very large objects. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Precision, Recall and Accuracy. |\n| Potential Known Risks: | Not recommended for life-critical use cases |\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? | None |\n| Was consent obtained for any PII used? | Yes |\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? | Yes |\n| If PII collected for the development of the model, was it collected directly by NVIDIA? | Yes, PII was collected by NVIDIA. |\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| Yes |\n| If PII collected for the development of this AI model, was it minimized to only what was required? | Yes |\n| Is data in dataset traceable?\t| No |\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? | Yes |\n| Applicable NVIDIA Privacy Policy\t| [https://www.nvidia.com/en-us/about-nvidia/privacy-policy/](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) |","safetyAndSecurity","| Field | Response |\n| -- | -- |\n| Model Application(s): | Object Detection |\n| Describe the life-critical application (if present). | Detecting presence of people in floor space. |\n| Use Case Restrictions: | Should not be used for safety critical use cases |\n| Explicit model and dataset restrictions: | Dataset access restrictions. |\n| Describe access restrictions (if any): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |","featureLabels",[],"displayLabels",[250,251],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_213":259,"_253":259,"_254":260,"_256":257},{"_213":252,"_253":252,"_254":255,"_256":257},"NSPECT-ITZN-CAOK","value","filter",{"_258":257,"_253":252},"category","label","field","DetectNet_v2 Architecture",{"_258":257,"_253":259},"appConfig",{"_1447":1448,"_295":1449},"ngcApps",[1400],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1382":1383,"_1384":1385,"_1386":1387,"_1388":1389,"_1390":273,"_73":300,"_1391":1392,"_1380":44,"_1393":84,"_1394":84,"_1395":1396,"_1397":84,"_1398":1399},"amplitude",{"_1378":1379,"_1380":44,"_973":-7,"_1381":20},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1372":1373,"_1374":1375,"_1376":1377},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","https://api.ngc.nvidia.com","ngcHelmOrigin","https://helm.ngc.nvidia.com","origin","https://catalog.ngc.nvidia.com","user","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1248":107,"_1249":1250,"_1251":1252,"_1253":1254},"announcements",[],"consentState",{"_338":1238,"_1239":84,"_1240":20,"_1241":20,"_1242":1243},"consentConfig",{"_320":321,"_322":323,"_324":325,"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339},"showContactAdmin",1782567705312,[],[],[314,315,316,317,318,319],{"_33":48,"_35":53,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":52,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":51,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":49,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":45,"_35":46,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":117,"_43":44},"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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They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",[488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522],[361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377],{"_378":484,"_213":484,"_106":107,"_352":485},{"_378":479,"_213":479,"_106":107,"_352":480},{"_378":475,"_213":475,"_106":107,"_352":476},{"_378":470,"_213":470,"_106":107,"_352":471},{"_378":465,"_213":465,"_106":107,"_352":466},{"_378":453,"_213":453,"_106":107,"_352":454},{"_378":441,"_213":441,"_106":107,"_352":442},{"_378":436,"_213":436,"_106":107,"_352":437},{"_378":425,"_213":425,"_106":107,"_352":426},{"_378":420,"_213":420,"_106":107,"_352":421},{"_378":415,"_213":415,"_106":107,"_352":416},{"_378":410,"_213":410,"_106":107,"_352":411},{"_378":405,"_213":405,"_106":107,"_352":406},{"_378":400,"_213":400,"_106":107,"_352":401},{"_378":395,"_213":395,"_106":107,"_352":396},{"_378":388,"_213":388,"_106":107,"_352":389},{"_378":379,"_213":379,"_106":107,"_352":380},"hostName","amazon-adsystem.com",[381,382],{"_158":387,"_384":379,"_385":20,"_106":386},{"_158":383,"_384":379,"_385":20,"_106":386},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[390,391],{"_158":394,"_384":388,"_385":20,"_106":393},{"_158":392,"_384":388,"_385":20,"_106":393},"IDE","This domain is owned by Doubleclick (Google). 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630],[575,576,577,578,579],{"_378":470,"_213":470,"_106":107,"_352":601},{"_378":436,"_213":436,"_106":107,"_352":596},{"_378":589,"_213":589,"_106":107,"_352":590},{"_378":585,"_213":585,"_106":107,"_352":586},{"_378":580,"_213":580,"_106":107,"_352":581},"nvcrm.my.site.com",[582],{"_158":583,"_384":580,"_385":20,"_106":584},"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",[587],{"_158":588,"_384":585,"_385":20,"_106":419},"edge","github.com",[591,592],{"_158":595,"_384":589,"_385":20,"_106":594},{"_158":593,"_384":589,"_385":20,"_106":594},"logged_in","This domain is owned by Github. 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