CLASSIC
Models
PeopleNet

PeopleNet

Description
3 class object detection network to detect people in an image.
Publisher
NVIDIA
Latest Version
pruned_quantized_decrypted_v2.3.4
Modified
June 12, 2025
Size
8.4 MB

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

Model Architecture

Architecture Type: Convolution Neural Network (CNN)
Network 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
Input Format(s): Red, Green, Blue (RGB)
Input Parameters: 4D
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.

Input image

Output:

Output Type(s): Label(s), Bounding-Box(es), Confidence Scores
Output Format: Label: Text String(s); Bounding Box: (x-coordinate, y-coordinate, width, height), Confidence Scores: Floating Point
Other 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
    • Automatic/Sensors
  • Labeling Method by dataset:
    • Human

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.

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:

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. Each frame is not required to have an object.

Performance

Evaluation Data Properties

  • Data Collection Method by dataset
    • Automatic/Sensors
  • Labeling Method by dataset:
    • Human

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.

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.

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
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
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.

  1. For ResNet34
model_config {
  num_layers: 34
  pretrained_model_file: "/path/to/the/model.tlt"
  use_batch_norm: true
  objective_set {
    bbox {
      scale: 35.0
      offset: 0.5
    }
    cov {
    }
  }
  training_precision {
    backend_floatx: FLOAT32
  }
  arch: "resnet"
  all_projections: true
}

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 below.

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

    ## Download Model:

mkdir -p $HOME/peoplenet && \
wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/peoplenet/versions/pruned_v2.1/files/resnet34_peoplenet_pruned.etlt \
-O $HOME/peoplenet/resnet34_peoplenet_pruned.etlt

## Run Application

xhost +
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 \
-w /opt/nvidia/deepstream/deepstream-5.1/samples/configs/tlt_pretrained_models nvcr.io/nvidia/deepstream:5.1-21.02-samples \
deepstream-app -c deepstream_app_source1_peoplenet.txt

  2. Install deepstream on your local host and run the deepstream-app.

    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:

    /opt/nvidia/deepstream/deepstream-5.1/samples/configs/tlt_pretrained_models

    /opt/nvidia/deepstream is the default DeepStream installation directory. This path will be different if you are installing in a different directory.

    You will need 2 config files and 1 label file. These files are provided in the tlt_pretrained_models directory.

    deepstream_app_source1_peoplenet.txt - Main config file for DeepStream app
config_infer_primary_peoplenet.txt - File to configure inference settings
labels_peoplenet.txt - Label file with 3 classes

    Key Parameters in config_infer_primary_peoplenet.txt

    tlt-model-key=
tlt-encoded-model=
labelfile-path=
int8-calib-file=
input-dims=
num-detected-classes=

    Run deepstream-app:

    deepstream-app -c deepstream_app_source1_peoplenet.txt

    Documentation to deploy with DeepStream is provided in "Deploying to DeepStream" chapter of TAO User Guide.

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

Suggested reading

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.