PeopleSemSegNet Model Card

Description:

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

References:

Citations

Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. \"U-net: Convolutional networks for biomedical image segmentation.\" International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.
Gamal, Mostafa, Mennatullah Siam, and Moemen Abdel-Razek. \"Shuffleseg: Real-time semantic segmentation network.\" arXiv preprint arXiv:1803.03816 (2018).

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: U-Net

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 544 X 3 (W x H x C); No minimum bit depth, alpha, or gamma.

Output:

Output Type(s): Label(s), Semantic Segmentation Mask
\nOutput Format: Label: Text String(s); Segmentation Mask: 2D
\nOther Properties Related to Output: Category Label(s): (person or background), Segmentation Mask

Software Integration:

Runtime Engine(s):

TAO - 5.2
DeepStream - 6.1 or later

Supported Hardware Architecture(s):

Ampere
Jetson
Hopper
Lovelace
Pascal
Turing
Volta

Supported Operating System(s):

Linux
Linux 4 Tegra

Model Version(s):

Vanilla UNet Dynamic:

trainable_vanilla_unet_v1.0 - Vanilla Unet Dynamic based pre-trained model.
deployable_vanilla_unet_v1.0 - Vanilla Unet Dynamic model deployable to deepstream.

There are two models for the deployable version:

peoplesemsegnet_vanilla_unet_dynamic_etlt_fp32.etlt : FP32 inference
peoplesemsegnet_vanilla_unet_dynamic_etlt_int8_fp16.etlt : FP16/ INT8 inference

The calibration cache for INT8 PTQ Vanilla UNet Dynamic UNet has been released.

This version of the model was particularly trained on extended arms people content.

ShuffleSeg UNet:

trainable_shuffleseg_unet_v1.0 - Shuffleseg Unet Dynamic based pre-trained model.
deployable_shuffleseg_unet_v1.0 - Shuffleseg UNet model deployable to deepstream.\nThe calibration cache for INT8 PTQ ShuffleSeg UNet has been released.

Training & Evaluation:

Training Dataset:

Data Collection Method by dataset:

Automatic/Sensors

Labeling Method by dataset:

Human

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

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

PeopleSemSegNet 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
\n
The segmentation masks were labeled using NVIDIA internal auto-labeling tool
\n

Evaluation Dataset:

Data Collection Method by dataset:

Automatic/Sensors

Labeling Method by dataset:

Human

Properties:
\n50000 proprietary images across a variety of environments with multiple camera types.. The frames are high resolution images 1920x1080 pixels resized to 960x544 pixels.

Methodology and KPI

The KPI for the evaluation data are reported in the table below. Model is evaluated based on Mean Intersection-Over-Union. Mean Intersection-Over-Union (MIOU) is a common evaluation metric for semantic image segmentation, which first computes the IOU for each semantic class and then computes the average over classes.

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

Model	Vanilla Unet Dynamic
Content	MIOU
5ft	91.86
10ft	91
20ft	89.7
Office use-case	95.01

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

Model	ShuffleSeg
Content	MIOU
5ft	89
10ft	87
20ft	84
Office use-case	87

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 unpruned 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.

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

Models	BS	Xavier Nx GPU FPS	BS	AGX Xavier GPU FPS	BS	Orin NX GPU FPS	BS	Orin GPU FPS
ShuffleSeg	16	199	16	356	16	289	32	703
VanillaUnet Dynamic	4	15	4	25	4	27	4	75

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

Models	BS	T4 GPU FPS	BS	A100 GPU FPS	BS	A30 GPU FPS	BS	A10 GPU FPS	BS	A2 GPU FPS
ShuffleSeg	64	1027.85	64	5745.79	64	2862.76	64	2429.62	16	631.31
VanillaUnet Dynamic	16	79.08	16	496.34	16	253.77	16	180.04	16	44.09

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 model is intended for training using TAO 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 TAO container can be used to re-train.

Primary use case intended for the model is segmenting people in a color (RGB) image. The model can be used to segment people from photos and videos by using appropriate video or image decoding and pre-processing. Note this model performs semantic segmentation and not instance based segmentation.

The model is 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 UNet model. For more information on the experiment spec file, please refer to the TAO Toolkit User Guide.

Model Config for Vanilla UNet Dynamic

model_config {\nnum_layers: 18\nmodel_input_width: 960\nmodel_input_height: 544\nmodel_input_channels: 3\nall_projections: true\narch: \"vanilla_unet_dynamic\"\nuse_batch_norm: true\ntraining_precision {\n  backend_floatx: FLOAT32\n}\n}\n

Model Config for ShuffleSeg

model_config {\nnum_layers: 18\nmodel_input_width: 960\nmodel_input_height: 544\nmodel_input_channels: 3\nall_projections: true\narch: \"shufflenet\"\nuse_batch_norm: true\ntraining_precision {\n  backend_floatx: FLOAT32\n}\n}\n

Use the following dataset config class parameters apart from the train_data_sources, val_data_sources, test_data_sources.\nPlease note that these are the default parameters used to generate the segmentation for the inferred image above. Please refer to TAO Toolkit User Guide to experiment the resize_method and resize_padding arguments to achieve the highest quality of mask on your dataset.

``py\ndataset: \"custom\"\naugment: False\ninput_image_type: \"color\"\nresize_padding: True\nresize_method: \"NEAREST_NEIGHBOR\"

\nUse the following for mapping the classes to the label id predicted. Person class is represented by id 1 and background is represented by id 0. Example `data_class_config` to be used for train/ evaluate/ inference in the experiment spec is as follows:  \n\n```py\ndata_class_config {\n  target_classes {\n    name: \"person\"\n    mapping_class: \"person\"\n    label_id: 1\n  }\n  target_classes {\n    name: \"background\"\n    mapping_class: \"background\"\n    label_id: 0\n  }\n}\n

Instructions to deploy these models with DeepStream

To create the entire end-to-end video analytics application, deploy these models with DeepStream SDK. DeepStream SDK is a streaming analytics toolkit to accelerate building AI-based video analytics 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 1 config files and 1 label file. These files are provided in NVIDIA-AI-IOT.

pgie_unet_tlt_config_peoplesemsegnet_shuffleseg.txt - File to configure inference settings for ShuffleSeg\npgie_unet_tlt_config_peoplesemsegnet_vanilla_unet_dynamic.txt - File to configure inference settings for Vanilla Unet Dynamic\n\nlabels.txt - Label file with 2 classes\n

Convert the .etlt file to engine if you want to input the model as TRT engine. Otherwise, you can input the etlt model directly to Deepstream. In order to manually convert to TRT engine, follow the example command below:

ShuffleSeg

FP16

./tao-converter -k tlt_encode -p input_2:0,1x3x544x960,1x3x544x960,1x3x544x960 -t fp16 -e ./bs1_fp16.engine ./peoplesemsegnet_shuffleseg_etlt.etlt\n

INT8

./tao-converter -k tlt_encode -p input_2:0,1x3x544x960,1x3x544x960,1x3x544x960 -t int8 -e ./bs1_int8.engine -c ./peoplesemsegnet_shuffleseg_cache.txt ./peoplesemsegnet_shuffleseg_etlt.etlt\n

VanillaUnetDynamic

FP32

./tao-converter -k tlt_encode -p input_1:0,1x3x544x960,1x3x544x960,1x3x544x960 -t fp16 -e ./bs1_fp32.engine ./peoplesemsegnet_vanilla_unet_dynamic_etlt_fp32.etlt\n

FP16

./tao-converter -k tlt_encode -p input_1:0,1x3x544x960,1x3x544x960,1x3x544x960 -t fp16 -e ./bs1_fp16.engine ./peoplesemsegnet_vanilla_unet_dynamic_etlt_int8_fp16.etlt\n

INT8

./tao-converter -k tlt_encode -p input_1:0,1x3x544x960,1x3x544x960,1x3x544x960 -t int8 -e ./bs1_int8.engine -c ./peoplesemsegnet_vanilla_unet_dynamic_etlt_int8.cache peoplesemsegnet_vanilla_unet_dynamic_etlt_int8_fp16.etlt\n

Key Parameters in pgie_unet_tao_config_peoplesemsegnet_vanilla_unet_dynamic.txt and pgie_unet_tao_config_peoplesemsegnet_shuffleseg.txt

# You can either provide the etlt model and key or trt engine obtained by using tao-converter\ntlt-model-key=tlt_encode\n# tlt-encoded-model=../../path/to/.etlt file\nmodel-engine-file=../../path/to/trt_engine\nnetwork-type=100\nnetwork-mode=2\nlabelfile-path=/path/to/labels.txt\n\n# Uncomment below if you want to use etlt file instead of engine\n# int8-calib-file=/path/to/calibration cache text file\ninfer-dims=3;544;960\nbatch-size=1\nnum-detected-classes=2\nsegmentation-output-order=1\nsegmentation-threshold=0.0\noutput-tensor-meta=1\nmodel-color-format=1 # BGR pre-processing\n

Run ds-tao-segmentation:

VanillaUnetDynamic

./apps/tao_segmentation/ds-tao-segmentation -c configs/unet_tao/pgie_unet_tao_config_peoplesemsegnet_vanilla_unet_dynamic.txt -i $DS_SRC_PATH/samples/streams/sample_720p.h264\n

ShuffleSeg UNet

./apps/tao_segmentation/ds-tao-segmentation -c configs/unet_tao/pgie_unet_tlt_config_peoplesemsegnet_shuffleseg.txt -i $DS_SRC_PATH/samples/streams/sample_720p.h264\n

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

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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 supporting 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- Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. \"U-net: Convolutional networks for biomedical image segmentation.\" International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.\n- Gamal, Mostafa, Mennatullah Siam, and Moemen Abdel-Razek. \"Shuffleseg: Real-time semantic segmentation network.\" arXiv preprint arXiv:1803.03816 (2018).\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:** U-Net
\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 544 X 3 (W x H x C); No minimum bit depth, alpha, or gamma.
\n\n

\n\n## Output:\n**Output Type(s):** Label(s), Semantic Segmentation Mask
\n**Output Format:** Label: Text String(s); Segmentation Mask: 2D
\n**Other Properties Related to Output:** Category Label(s): (person or background), Segmentation Mask
\n\n

\n\n## Software Integration:\n**Runtime Engine(s):** \n* TAO - 5.2
\n* DeepStream - 6.1 or later
\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#### Vanilla UNet Dynamic:\n\n- **trainable_vanilla_unet_v1.0** - Vanilla Unet Dynamic based pre-trained model.\n- **deployable_vanilla_unet_v1.0** - Vanilla Unet Dynamic model deployable to deepstream.\n\nThere are two models for the deployable version:\n - peoplesemsegnet_vanilla_unet_dynamic_etlt_fp32.etlt : FP32 inference\n - peoplesemsegnet_vanilla_unet_dynamic_etlt_int8_fp16.etlt : FP16/ INT8 inference\n\nThe calibration cache for INT8 PTQ Vanilla UNet Dynamic UNet has been released.\n\nThis version of the model was particularly trained on extended arms people content.\n\n#### ShuffleSeg UNet:\n\n- **trainable_shuffleseg_unet_v1.0** - Shuffleseg Unet Dynamic based pre-trained model.\n- **deployable_shuffleseg_unet_v1.0** - Shuffleseg UNet model deployable to deepstream.\nThe calibration cache for INT8 PTQ ShuffleSeg UNet has been released.\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 with more than 5 million people. The training dataset consists of a mix of camera heights, crowd-density, and field-of view (FOV) with multiple camera types. 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 PeopleSemSegNet model. If you are looking to re-train with your own dataset, please follow the guideline below for highest accuracy.\n\nPeopleSemSegNet 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\n8. The segmentation masks were labeled using NVIDIA internal auto-labeling tool\n\n## Evaluation Dataset:\n**Data Collection Method by dataset:**
\n* Automatic/Sensors
\n\n**Labeling Method by dataset:**
\n* Human
\n\n**Properties:**
\n50000 proprietary images across a variety of environments with multiple camera types.. The frames are high resolution images 1920x1080 pixels resized to 960x544 pixels.\n\n### Methodology and KPI\n\nThe KPI for the evaluation data are reported in the table below. Model is evaluated based on Mean Intersection-Over-Union. Mean Intersection-Over-Union (MIOU) is a common evaluation metric for semantic image segmentation, which first computes the IOU for each semantic class and then computes the average over classes.\n\n| Model | Vanilla Unet Dynamic |\n| ---- | ----- |\n| Content | MIOU |\n| 5ft | 91.86 |\n| 10ft | 91 |\n| 20ft | 89.7 |\n| Office use-case | 95.01 |\n\n| Model | ShuffleSeg |\n| ---- | ----- |\n| Content | MIOU |\n| 5ft | 89 |\n| 10ft | 87 |\n| 20ft | 84 |\n| Office use-case | 87 |\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 unpruned 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| Models | BS | Xavier Nx GPU FPS | BS | AGX Xavier GPU FPS | BS | Orin NX GPU FPS | BS | Orin GPU FPS |\n|---------------------|----|-------------------|----|--------------------|----|-----------------|----|--------------|\n| ShuffleSeg | 16 | 199 | 16 | 356 | 16 | 289 | 32 | 703 |\n| VanillaUnet Dynamic | 4 | 15 | 4 | 25 | 4 | 27 | 4 | 75 |\n\n| Models | BS | T4 GPU FPS | BS | A100 GPU FPS | BS | A30 GPU FPS | BS | A10 GPU FPS | BS | A2 GPU FPS |\n|---------------------|----|------------|----|--------------|----|-------------|----|-------------|----|------------|\n| ShuffleSeg | 64 | 1027.85 | 64 | 5745.79 | 64 | 2862.76 | 64 | 2429.62 | 16 | 631.31 |\n| VanillaUnet Dynamic | 16 | 79.08 | 16 | 496.34 | 16 | 253.77 | 16 | 180.04 | 16 | 44.09 |\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\nThe model is intended for training using TAO 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 TAO container can be used to re-train.\n\nPrimary use case intended for the model is segmenting people in a color (RGB) image. The model can be used to segment people from photos and videos by using appropriate video or image decoding and pre-processing. Note this model performs semantic segmentation and not instance based segmentation.\n\nThe model is 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 UNet 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#### Model Config for Vanilla UNet Dynamic\n\n ```py\nmodel_config {\n num_layers: 18\n model_input_width: 960\n model_input_height: 544\n model_input_channels: 3\n all_projections: true\n arch: \"vanilla_unet_dynamic\"\n use_batch_norm: true\n training_precision {\n backend_floatx: FLOAT32\n }\n}\n```\n#### Model Config for ShuffleSeg\n\n ```py\nmodel_config {\n num_layers: 18\n model_input_width: 960\n model_input_height: 544\n model_input_channels: 3\n all_projections: true\n arch: \"shufflenet\"\n use_batch_norm: true\n training_precision {\n backend_floatx: FLOAT32\n }\n}\n```\n\nUse the following dataset config class parameters apart from the `train_data_sources`, `val_data_sources`, `test_data_sources`.\nPlease note that these are the default parameters used to generate the segmentation for the inferred image above. Please refer to [TAO Toolkit User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html) to experiment the `resize_method` and `resize_padding` arguments to achieve the highest quality of mask on your dataset.\n\n``py\ndataset: \"custom\"\naugment: False\ninput_image_type: \"color\"\nresize_padding: True\nresize_method: \"NEAREST_NEIGHBOR\"\n```\n\nUse the following for mapping the classes to the label id predicted. Person class is represented by id 1 and background is represented by id 0. Example `data_class_config` to be used for train/ evaluate/ inference in the experiment spec is as follows: \n\n```py\ndata_class_config {\n target_classes {\n name: \"person\"\n mapping_class: \"person\"\n label_id: 1\n }\n target_classes {\n name: \"background\"\n mapping_class: \"background\"\n label_id: 0\n }\n}\n```\n\n### Instructions to deploy these models with DeepStream\n\nTo create the entire end-to-end video analytics application, deploy these models with [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk). DeepStream SDK is a streaming analytics toolkit to accelerate building AI-based video analytics 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 1 config files and 1 label file. These files are provided in [NVIDIA-AI-IOT](https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps/tree/release/tao3.0/configs/peopleSemSegNet_tao).\n\n```sh\npgie_unet_tlt_config_peoplesemsegnet_shuffleseg.txt - File to configure inference settings for ShuffleSeg\npgie_unet_tlt_config_peoplesemsegnet_vanilla_unet_dynamic.txt - File to configure inference settings for Vanilla Unet Dynamic\n\nlabels.txt - Label file with 2 classes\n```\nConvert the .etlt file to engine if you want to input the model as TRT engine. Otherwise, you can input the etlt model directly to Deepstream. In order to manually convert to TRT engine, follow the example command below:\n\n#### ShuffleSeg\n\nFP16\n```sh\n./tao-converter -k tlt_encode -p input_2:0,1x3x544x960,1x3x544x960,1x3x544x960 -t fp16 -e ./bs1_fp16.engine ./peoplesemsegnet_shuffleseg_etlt.etlt\n```\n\nINT8\n```sh\n./tao-converter -k tlt_encode -p input_2:0,1x3x544x960,1x3x544x960,1x3x544x960 -t int8 -e ./bs1_int8.engine -c ./peoplesemsegnet_shuffleseg_cache.txt ./peoplesemsegnet_shuffleseg_etlt.etlt\n```\n\n#### VanillaUnetDynamic\n\nFP32\n```sh\n./tao-converter -k tlt_encode -p input_1:0,1x3x544x960,1x3x544x960,1x3x544x960 -t fp16 -e ./bs1_fp32.engine ./peoplesemsegnet_vanilla_unet_dynamic_etlt_fp32.etlt\n```\n\nFP16\n```sh\n./tao-converter -k tlt_encode -p input_1:0,1x3x544x960,1x3x544x960,1x3x544x960 -t fp16 -e ./bs1_fp16.engine ./peoplesemsegnet_vanilla_unet_dynamic_etlt_int8_fp16.etlt\n```\n\nINT8\n```sh\n./tao-converter -k tlt_encode -p input_1:0,1x3x544x960,1x3x544x960,1x3x544x960 -t int8 -e ./bs1_int8.engine -c ./peoplesemsegnet_vanilla_unet_dynamic_etlt_int8.cache peoplesemsegnet_vanilla_unet_dynamic_etlt_int8_fp16.etlt\n```\n\n\nKey Parameters in `pgie_unet_tao_config_peoplesemsegnet_vanilla_unet_dynamic.txt` and `pgie_unet_tao_config_peoplesemsegnet_shuffleseg.txt`\n\n```sh\n# You can either provide the etlt model and key or trt engine obtained by using tao-converter\ntlt-model-key=tlt_encode\n# tlt-encoded-model=../../path/to/.etlt file\nmodel-engine-file=../../path/to/trt_engine\nnetwork-type=100\nnetwork-mode=2\nlabelfile-path=/path/to/labels.txt\n\n# Uncomment below if you want to use etlt file instead of engine\n# int8-calib-file=/path/to/calibration cache text file\ninfer-dims=3;544;960\nbatch-size=1\nnum-detected-classes=2\nsegmentation-output-order=1\nsegmentation-threshold=0.0\noutput-tensor-meta=1\nmodel-color-format=1 # BGR pre-processing\n```\n\nRun `ds-tao-segmentation`:\n\n#### VanillaUnetDynamic\n```sh\n./apps/tao_segmentation/ds-tao-segmentation -c configs/unet_tao/pgie_unet_tao_config_peoplesemsegnet_vanilla_unet_dynamic.txt -i $DS_SRC_PATH/samples/streams/sample_720p.h264\n```\n\n#### ShuffleSeg UNet\n```sh\n./apps/tao_segmentation/ds-tao-segmentation -c configs/unet_tao/pgie_unet_tlt_config_peoplesemsegnet_shuffleseg.txt -i $DS_SRC_PATH/samples/streams/sample_720p.h264\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- [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_toolkit_quick_start_guide.html) guide and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html).\n- If you have any questions or feedback, 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## 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 supporting 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","PeopleSemSegnet",["D",1732745278524],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/peoplesemsegnet","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",3911384,"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 and for robotic navigation. |\n| Model Type: | Semantic Segmentation |\n| Intended Users: | Developers working in smart spaces, retail, and industrial applications with people. |\n| Output: | Segmentation Masks |\n| Describe how the model works: | Associates object class label for each image pixel. |\n| Technical Limitations: | Model should not be used in low-light, low contrast lighting conditions, with warped or motion-induced images, and with very small and very large objects. Model accuracy may vary with occlusion or truncation. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Accuracy, Intersection over Union(IoU), Precision, Recall |\n| Potential Known Risks: | Inaccurate segmentation for robotic navigation could lead to collisions. Not recommended for life-critical use cases. |\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| 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 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): | Semantic Segmentation |\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. 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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619],[458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474],{"_475":581,"_269":581,"_186":187,"_448":582},{"_475":576,"_269":576,"_186":187,"_448":577},{"_475":572,"_269":572,"_186":187,"_448":573},{"_475":567,"_269":567,"_186":187,"_448":568},{"_475":562,"_269":562,"_186":187,"_448":563},{"_475":550,"_269":550,"_186":187,"_448":551},{"_475":538,"_269":538,"_186":187,"_448":539},{"_475":533,"_269":533,"_186":187,"_448":534},{"_475":522,"_269":522,"_186":187,"_448":523},{"_475":517,"_269":517,"_186":187,"_448":518},{"_475":512,"_269":512,"_186":187,"_448":513},{"_475":507,"_269":507,"_186":187,"_448":508},{"_475":502,"_269":502,"_186":187,"_448":503},{"_475":497,"_269":497,"_186":187,"_448":498},{"_475":492,"_269":492,"_186":187,"_448":493},{"_475":485,"_269":485,"_186":187,"_448":486},{"_475":476,"_269":476,"_186":187,"_448":477},"hostName","amazon-adsystem.com",[478,479],{"_81":484,"_481":476,"_482":31,"_186":483},{"_81":480,"_481":476,"_482":31,"_186":483},"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",[487,488],{"_81":491,"_481":485,"_482":31,"_186":490},{"_81":489,"_481":485,"_482":31,"_186":490},"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.","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. 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