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PeopleSegNet Model Card

Description:

PeopleSegNet detects persons in an image. This model is ready for commercial use.

References:

Citations

K. He, G. Gkioxari, P. Dollar, and R. Girshick. Mask RCNN. In ICCV, 2017. 1, 2, 4

Using TAO Pre-trained Models

Get TAO Container
Get other purpose-built models from the NGC model registry:\n
- TrafficCamNet
- PeopleNet
- PeopleNet-Transformer
- DashCamNet
- FaceDetectIR
- VehicleMakeNet
- VehicleTypeNet
- PeopleSegNet
- PeopleSemSegNet
- License Plate Detection
- License Plate Recognition
- PoseClassificationNet
- Facial Landmark
- FaceDetect
- 2D Body Pose Estimation
- ActionRecognitionNet
- People ReIdentification
- PointPillarNet
- CitySegFormer
- Retail Object Detection
- Retail Object Embedding
- Optical Inspection
- Optical Character Detection
- Optical Character Recognition
- PCB Classification
- PeopleSemSegFormer
\n

Model Architecture:

Architecture Type: Convolution Neural Network (CNN)
\nNetwork Architecture: MaskRCNN + ResNet50

Input:

Input Type(s): Images
\nInput Format(s): Red, Green, Blue (RGB)
\nInput Parameters: 3D
\nOther Properties Related to Input: RGB Fixed Resolution: 960 X 576 X 3 (W x H x C); No minimum bit depth, alpha, or gamma.

Output:

Output Type(s): Label(s), Bounding-Box(es), Segmentation Mask(s)
\nOutput Format: Label: Text String(s); Bounding Box: (x-coordinate, y-coordinate, width, height), Segmentation Mask: 2D
\nOther Properties Related to Output: Category Label(s): Person, Bounding Box Coordinates, Confidence Scores, Segmentation Mask

Software Integration:

Runtime Engine(s):

TAO - 5.2
DeepStream - 6.1

Supported Hardware Architecture(s):

Ampere
Jetson
Hopper
Lovelace
Pascal
Turing
Volta

Supported Operating System(s):

Linux
Linux 4 Tegra

Model Version(s):

trainable_v2.1 - ResNet50 based pre-trained model.
deployable_v2.0.2 - ResNet50 based model deployable to deepstream.

Training & Evaluation:

Training Dataset:

Data Collection Method by dataset:

Automatic/Sensors

Labeling Method by dataset:

Human

Properties:
\nProprietary dataset of more than 5 million people. The training dataset consists of a mix of camera heights, crowd-density, and field-of view (FOV) for multiple camera types across all samples. Approximately half of the training data consisted of images captured in an indoor office environment.

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

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

		Object
Environment	Images	Persons
5ft Indoor	108,692	1,060,960
5ft Outdoor	206,912	166,8250
10ft Indoor (Office close FOV)	413,270	4,577,870
10ft Outdoor	18,321	178,817
20ft Indoor	104,972	1,079,550
20ft Outdoor	24,783	59,623
Total	876,950	8,625,070

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 PeopleSegNet model. If you are looking to re-train with your own dataset, please follow the guideline below for highest accuracy.

PeopleSegNet 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

Evaluation Dataset:

Data Collection Method by dataset:

Automatic/Sensors

Labeling Method by dataset:

Human

Properties:
\n42000 proprietary images of people across a variety of environments with multiple camera types.

Methodology and KPI

The true positives, false positives, false negatives are calculated using intersection-over-union (IOU) criterion greater than 0.5. The KPI for the evaluation data are reported in the table below. Model is evaluated based on precision, recall and accuracy.

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

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

Model		ResNet 50
Content	Precision	Recall	Accuracy
5ft	93.69	90.36	85.45
10ft	96.13	76.22	73.95
20ft	97.58	91.88	90.52
Office use-case	88.31	94.52	86.00

Inference:

Engine: Tensor(RT)
\nTest Hardware:

Jetson AGX Xavier
Xavier NX
Orin
Orin NX
NVIDIA T4
Ampere GPU
A2
A30
L4
T4
DGX H100
DGX A100
DGX H100
L40
JAO 64GB
Orin NX16GB
Orin Nano 8GB

The inference is run on the provided pruned models at INT8 precision. On the Jetson Nano FP16 precision is used. The inference performance is run using trtexec on Jetson Nano, AGX Xavier, Xavier NX and NVIDIA T4 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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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

Platform	FPS
Nano	0.6
Xavier NX	8.5
AGX Xavier	12.2
T4	40

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.

Primary use case intended for the model is detecting and segmenting people in a color (RGB) image. The model can be used to detect and segment people from photos and videos by using appropriate video or image decoding and pre-processing.

The model is intended for training using Transfer Learning Toolkit with the user's own dataset or using it as it is. This can provide high fidelity models that are adapted to the use case. The Jupyter notebook available as a part of TLT container can be used to re-train.

The model is encrypted and will only operate with the following key:

Model load key: nvidia_tlt

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 maskrcnn_config component of the experiment spec file to train a MaskRCNN model. For more information on the experiment spec file, please refer to the TAO Toolkit User Guide.

maskrcnn_config {\n  nlayers: 50\n  arch: \"resnet\"\n  gt_mask_size: 112\n  freeze_blocks: \"[0]\"\n  freeze_bn: True\n  # Region Proposal Network\n  rpn_positive_overlap: 0.7\n  rpn_negative_overlap: 0.3\n  rpn_batch_size_per_im: 256\n  rpn_fg_fraction: 0.5\n  rpn_min_size: 0.\n\n  # Proposal layer.\n  batch_size_per_im: 512\n  fg_fraction: 0.25\n  fg_thresh: 0.5\n  bg_thresh_hi: 0.5\n  bg_thresh_lo: 0.\n\n  # Faster-RCNN heads.\n  fast_rcnn_mlp_head_dim: 1024\n  bbox_reg_weights: \"(10., 10., 5., 5.)\"\n\n  # Mask-RCNN heads.\n  include_mask: True\n  mrcnn_resolution: 28\n\n  # training\n  train_rpn_pre_nms_topn: 2000\n  train_rpn_post_nms_topn: 1000\n  train_rpn_nms_threshold: 0.7\n\n  # evaluation\n  test_detections_per_image: 100\n  test_nms: 0.5\n  test_rpn_pre_nms_topn: 1000\n  test_rpn_post_nms_topn: 1000\n  test_rpn_nms_thresh: 0.7\n\n  # model architecture\n  min_level: 2\n  max_level: 6\n  num_scales: 1\n  aspect_ratios: \"[(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]\"\n  anchor_scale: 8\n\n  # localization loss\n  rpn_box_loss_weight: 1.0\n  fast_rcnn_box_loss_weight: 1.0\n  mrcnn_weight_loss_mask: 1.0\n}\n

Instructions to deploy these models with DeepStream

To create the entire end-to-end video analytic application, deploy these models with DeepStream SDK. DeepStream SDK is a streaming analytic toolkit to accelerate building AI-based video analytic applications. DeepStream supports direct integration of these models into the deepstream sample app.

To deploy these models with DeepStream 6.1, please follow the instructions below:

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 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 NVIDIA-AI-IOT.

deepstream_app_source1_peoplesegnet.txt - Main config file for DeepStream app\npgie_peopleSegNetv2_tao_config.txt - File to configure inference settings \npeopleSegNet_labels.txt - Label file with 1 class\n

Key Parameters in pgie_peopleSegNetv2_tao_config.txt

tlt-model-key=nvidia_tlt\ntlt-encoded-model=../../models/peopleSegNet/V2/peoplesegnet_resnet50.etlt\nmodel-engine-file=../../models/peopleSegNet/V2/peoplesegnet_resnet50.etlt_b1_gpu0_int8.engine\nnetwork-type=3 ## 3 is for instance segmentation network\nlabelfile-path=./peopleSegNet_labels.txt\nint8-calib-file=../../models/peopleSegNet/V2/peoplesegnet_resnet50_int8.txt\ninfer-dims=3;576;960\nnum-detected-classes=2\n

Run deepstream-app:

deepstream-app -c deepstream_app_source1_peoplesegnet.txt\n

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

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Developing and Deploying AI-powered Robots with NVIDIA Isaac Sim and NVIDIA TAO.
Learn endless ways to adapt and supercharge your AI workflows with TAO - Whitepaper.
Customize Action Recognition with TAO and deploy with DeepStream.
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.

Ethical Considerations:

Training and evaluation dataset is sourced from North America. More inclusive training and evaluation dataset would include content from other parts of the world.

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Promise and the Explainability, Bias, Safety & Security, and Privacy Subcards.

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This model is ready for commercial use. \n\n## References:\n### Citations \n\n- K. He, G. Gkioxari, P. Dollar, and R. Girshick. Mask RCNN. In ICCV, 2017. 1, 2, 4\n\n### Using TAO Pre-trained Models \n- Get [TAO Container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit)\n- Get other purpose-built models from the NGC model registry:\n - [TrafficCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:trafficcamnet)\n - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)\n - [PeopleNet-Transformer](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet_transformer)\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 - [PeopleSegNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplesegnet)\n - [PeopleSemSegNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplesemsegnet)\n - [License Plate Detection](https://ngc.nvidia.com/catalog/models/nvidia:tao:lpdnet)\n - [License Plate Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:lprnet)\n - [PoseClassificationNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:poseclassificationnet)\n - [Facial Landmark](https://ngc.nvidia.com/catalog/models/nvidia:tao:fpenet)\n - [FaceDetect](https://ngc.nvidia.com/catalog/models/nvidia:tao:facenet)\n - [2D Body Pose Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:bodyposenet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [People ReIdentification](https://ngc.nvidia.com/catalog/models/nvidia:tao:reidentificationnet)\n - [PointPillarNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pointpillarnet)\n - [CitySegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/citysemsegformer)\n - [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection)\n - [Retail Object Embedding](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_recognition)\n - [Optical Inspection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/optical_inspection)\n - [Optical Character Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocdnet)\n - [Optical Character Recognition](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocrnet)\n - [PCB Classification](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pcb_classification)\n - [PeopleSemSegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplesemsegformer)\n\n\n## Model Architecture: \n**Architecture Type:** Convolution Neural Network (CNN)
\n**Network Architecture:** MaskRCNN + ResNet50
\n\n## Input:\n**Input Type(s):** Images
\n**Input Format(s):** Red, Green, Blue (RGB)
\n**Input Parameters:** 3D
\n**Other Properties Related to Input:** RGB Fixed Resolution: 960 X 576 X 3 (W x H x C); No minimum bit depth, alpha, or gamma.
\n\n

\n\n\n## Output:\n**Output Type(s):** Label(s), Bounding-Box(es), Segmentation Mask(s)
\n**Output Format:** Label: Text String(s); Bounding Box: (x-coordinate, y-coordinate, width, height), Segmentation Mask: 2D
\n**Other Properties Related to Output:** Category Label(s): Person, Bounding Box Coordinates, Confidence Scores, Segmentation Mask
\n\n

\n\n## Software Integration:\n**Runtime Engine(s):** \n* TAO - 5.2
\n* DeepStream - 6.1
\n\n**Supported Hardware Architecture(s):**
\n* Ampere
\n* Jetson
\n* Hopper
\n* Lovelace
\n* Pascal
\n* Turing
\n* Volta
\n\n**Supported Operating System(s):**
\n* Linux
\n* Linux 4 Tegra
\n\n## Model Version(s): \n- **trainable_v2.1** - ResNet50 based pre-trained model.\n- **deployable_v2.0.2** - ResNet50 based model deployable to deepstream.\n\n# Training & Evaluation: \n\n## Training Dataset:\n**Data Collection Method by dataset:**
\n* Automatic/Sensors
\n\n**Labeling Method by dataset:**
\n* Human
\n\n**Properties:**
\nProprietary dataset of more than 5 million people. The training dataset consists of a mix of camera heights, crowd-density, and field-of view (FOV) for multiple camera types across all samples. Approximately half of the training data consisted of images captured in an indoor office environment.\n\n| | | Object |\n| ---- | ---- | ---- |\n| Environment | Images | Persons | \n| 5ft Indoor | 108,692 | 1,060,960 |\n| 5ft Outdoor | 206,912 | 166,8250 |\n| 10ft Indoor (Office close FOV) | 413,270 | 4,577,870 |\n| 10ft Outdoor | 18,321 | 178,817 |\n| 20ft Indoor | 104,972 | 1,079,550 |\n| 20ft Outdoor | 24,783 | 59,623 |\n| Total | 876,950 | 8,625,070 |\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 PeopleSegNet model. If you are looking to re-train with your own dataset, please follow the guideline below for highest accuracy.\n\nPeopleSegNet 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## Evaluation Dataset:\n**Data Collection Method by dataset:**
\n* Automatic/Sensors
\n\n**Labeling Method by dataset:**
\n* Human
\n\n**Properties:**
\n42000 proprietary images of people across a variety of environments with multiple camera types.\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. The KPI for the evaluation data are reported in the table below. Model is evaluated based on precision, recall and accuracy.\n\n| Model | | ResNet 50 | |\n| ---- | ---- | ---- | ---- |\n| Content | Precision | Recall| Accuracy |\n| 5ft | 93.69 | 90.36 | 85.45 |\n| 10ft | 96.13 | 76.22 | 73.95 |\n| 20ft | 97.58 | 91.88 | 90.52 |\n| Office use-case | 88.31 | 94.52 | 86.00 |\n\n## Inference:\n**Engine:** Tensor(RT)
\n**Test Hardware:**
\n- Jetson AGX Xavier\n- Xavier NX\n- Orin\n- Orin NX\n- NVIDIA T4\n- Ampere GPU\n- A2\n- A30\n- L4\n- T4\n- DGX H100\n- DGX A100\n- DGX H100\n- L40\n- JAO 64GB\n- Orin NX16GB\n- Orin Nano 8GB\n\nThe inference is run on the provided pruned models at INT8 precision. On the Jetson Nano FP16 precision is used. The inference performance is run using [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/master/samples/trtexec) on Jetson Nano, AGX Xavier, Xavier NX and NVIDIA T4 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| Platform | FPS |\n|------------------------------------|------|\n| Nano | 0.6 |\n| Xavier NX | 8.5 |\n| AGX Xavier | 12.2 |\n| T4 | 40 |\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, DeepStream SDK or TensorRT.\n\nPrimary use case intended for the model is detecting and segmenting people in a color (RGB) image. The model can be used to detect and segment people from photos and videos by using appropriate video or image decoding and pre-processing.\n\nThe model is intended for training using Transfer Learning Toolkit with the user's own dataset or using it as it is. This can provide high fidelity models that are adapted to the use case. The Jupyter notebook available as a part of TLT container can be used to re-train.\n\nThe model is encrypted and will only operate with the following key:\n\n- Model load key: `nvidia_tlt`\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 `maskrcnn_config` component of the experiment spec file to train a MaskRCNN 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\n ```py\n maskrcnn_config {\n nlayers: 50\n arch: \"resnet\"\n gt_mask_size: 112\n freeze_blocks: \"[0]\"\n freeze_bn: True\n # Region Proposal Network\n rpn_positive_overlap: 0.7\n rpn_negative_overlap: 0.3\n rpn_batch_size_per_im: 256\n rpn_fg_fraction: 0.5\n rpn_min_size: 0.\n\n # Proposal layer.\n batch_size_per_im: 512\n fg_fraction: 0.25\n fg_thresh: 0.5\n bg_thresh_hi: 0.5\n bg_thresh_lo: 0.\n\n # Faster-RCNN heads.\n fast_rcnn_mlp_head_dim: 1024\n bbox_reg_weights: \"(10., 10., 5., 5.)\"\n\n # Mask-RCNN heads.\n include_mask: True\n mrcnn_resolution: 28\n\n # training\n train_rpn_pre_nms_topn: 2000\n train_rpn_post_nms_topn: 1000\n train_rpn_nms_threshold: 0.7\n\n # evaluation\n test_detections_per_image: 100\n test_nms: 0.5\n test_rpn_pre_nms_topn: 1000\n test_rpn_post_nms_topn: 1000\n test_rpn_nms_thresh: 0.7\n\n # model architecture\n min_level: 2\n max_level: 6\n num_scales: 1\n aspect_ratios: \"[(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]\"\n anchor_scale: 8\n\n # localization loss\n rpn_box_loss_weight: 1.0\n fast_rcnn_box_loss_weight: 1.0\n mrcnn_weight_loss_mask: 1.0\n}\n ```\n\n### Instructions to deploy these models with DeepStream\n\nTo create the entire end-to-end video analytic application, deploy these models with [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk). DeepStream SDK is a streaming analytic toolkit to accelerate building AI-based video analytic applications. DeepStream supports direct integration of these models into the deepstream sample app.\n\nTo deploy these models with [DeepStream 6.1](https://developer.nvidia.com/deepstream-sdk), please follow the instructions below:\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`/opt/nvidia/deepstream` is the default DeepStream installation directory. This path will be different if you are installing in a different directory. \n\nYou will need 2 config files and 1 label file. These files are provided in [NVIDIA-AI-IOT](https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps/tree/master/configs/nvinfer/peopleSegNet_tao).\n\n```sh\ndeepstream_app_source1_peoplesegnet.txt - Main config file for DeepStream app\npgie_peopleSegNetv2_tao_config.txt - File to configure inference settings \npeopleSegNet_labels.txt - Label file with 1 class\n```\n\nKey Parameters in `pgie_peopleSegNetv2_tao_config.txt`\n\n```sh\ntlt-model-key=nvidia_tlt\ntlt-encoded-model=../../models/peopleSegNet/V2/peoplesegnet_resnet50.etlt\nmodel-engine-file=../../models/peopleSegNet/V2/peoplesegnet_resnet50.etlt_b1_gpu0_int8.engine\nnetwork-type=3 ## 3 is for instance segmentation network\nlabelfile-path=./peopleSegNet_labels.txt\nint8-calib-file=../../models/peopleSegNet/V2/peoplesegnet_resnet50_int8.txt\ninfer-dims=3;576;960\nnum-detected-classes=2\n```\n\nRun `deepstream-app`:\n\n```sh\ndeepstream-app -c deepstream_app_source1_peoplesegnet.txt\n```\n\nDocumentation 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## Technical blogs \n\n- [Access the latest in Vision AI development workflows with NVIDIA TAO Toolkit 5.0](https://developer.nvidia.com/blog/access-the-latest-in-vision-ai-model-development-workflows-with-nvidia-tao-toolkit-5-0/).\n- [Improve accuracy and robustness of vision ai models with vision transformers and NVIDIA TAO](https://developer.nvidia.com/blog/improve-accuracy-and-robustness-of-vision-ai-apps-with-vision-transformers-and-nvidia-tao/).\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- Read the [TAO User Guide](https://docs.nvidia.com/tao/tao-toolkit/text/overview.html) guide and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html#transfer-learning-toolkit-v3-0l). \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 model on the edge using DeepStream. Learn more about DeepStream SDK https://developer.nvidia.com/deepstream-sdk.\n\n## Ethical Considerations:\nTraining and evaluation dataset is sourced from North America. More inclusive training and evaluation dataset would include content from other parts of the world.\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Promise and the Explainability, Bias, Safety & Security, and Privacy Subcards.","displayName","PeopleSegNet",["D",1732745297520],"publisher","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/peoplesegnet","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",73969636,"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: | Race, Age, 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. Unwanted bias is mitigated by the collection of data in different urban areas at different times of the day during typical working hours and outside of typical working times to get a diversity of races, skin-tones, ages, and gender.|","explainability","| Field | Response |\n| -- | -- |\n| Intended Application(s) & Domain(s): | Segmenting people in smart spaces, retail, industrial applications. |\n| Model Type: | Instance segmentation |\n| Intended Users: | Developers working in smart spaces, retail, and industrial applications with people. |\n| Output: | Bounding Box(es), Category Label(s), Confidence Score(s) |\n| Describe how the model works: | Detects and labels bags, faces, and persons within an image and returns bounding box coordinates, labels, and confidence scores for each category. |\n| Technical Limitations: | Model should not be used in low-light, low contrast lighting conditions, with warped or motion-induced images, or very small and very large objects. Model accuracy may vary with occlusion or truncation. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Accuracy, Intersection over Union(IoU), Precision, Recall |\n| Potential Known Risks: | This model may fail to properly detect people. |\n| Licensing: | [https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/) |","privacy","| Field | Response |\n| -- | -- |\n| Generatable or reverse engineerable personally-identifiable information (PII)? | 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 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): | Single class instance segmentation network to detect and segment instances of people in an image. |\n| Describe the life-critical application (if present). | Potential for life-critical impact in detecting the presence of people in floor space. |\n| Use Case Restrictions: | Should not be used in \"Critical Applications;\" [reference](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/.) |\n| Describe access restrictions (if any): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |","featureLabels",[275],"displayLabels",[258,259,260],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_222":272,"_262":273,"_264":274,"_266":267},{"_222":270,"_262":270,"_264":271,"_266":267},{"_222":261,"_262":263,"_264":265,"_266":267},"NVIDIA AI","value","soln_nvidia_ai","filter",{"_268":269,"_262":263},"category","label","field","technology","NSPECT-G9P3-PL45",{"_268":267,"_262":270},"AI","soln_ai",{"_268":269,"_262":273},{"_222":276,"_262":276,"_264":277,"_266":278},"NVIDIA AI Enterprise Supported",{"_268":267,"_262":276},"feature",{"_105":1454,"_107":1455},"ngcApps",[1407],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1388":1389,"_1390":1391,"_1392":1393,"_1394":1395,"_1396":290,"_137":314,"_1397":1398,"_1399":55,"_1400":84,"_1401":84,"_1402":1403,"_1404":84,"_1405":1406},"amplitude",{"_1385":1386,"_987":-7,"_1387":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1379":1380,"_1381":1382,"_1383":1384},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1262":165,"_1263":1264,"_1265":1266,"_1267":1268},"announcements",[],"consentState",{"_353":1252,"_1253":84,"_1254":30,"_1255":30,"_1256":1257},"consentConfig",{"_335":336,"_337":338,"_339":340,"_341":342,"_343":344,"_345":346,"_347":348,"_349":350,"_351":352,"_353":354},"showContactAdmin",1783956346605,[],[],[328,329,330,331,332,333],{"_44":58,"_46":68,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":67,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":65,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":59,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":334,"_54":55},4,"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. 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