CitySemSegFormer Model Card

Description:

CitySemSegFormer segments classes within an image.\nThis model segments the urban cityscapes into 19 classes which include:

road
sidewalk
building
wall
fence
pole
traffic light
traffic sign
vegetation
terrain
sky
person
rider
car
truck
bus
train
motorcycle
bicycle

This model is ready for commercial use.

References:

Citations

Xie, Enze, et al. \"SegFormer: Simple and efficient design for semantic segmentation with transformers.\" Advances in Neural Information Processing Systems 34 (2021): 12077-12090.
https://github.com/NVIDIA/semantic-segmentation

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: Transformer
\nNetwork Architecture: Segformer

Input:

Input Type(s): Image
\nInput Format(s): Red, Green, Blue (RGB)
\nInput Parameters: 3D
\nOther Properties Related to Input: RGB Fixed Resolution:1024x1024x3 (W x H x C); No minimum bit depth, alpha, or gamma.

Output:

Output Type(s): Semantic Segmentation Mask
\nOutput Format: Segmentation Mask: 2D vector

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):

trainable_mitb5_citysemsegformer_v1.0 - citySemSegformer mitB5 model trainable.
deployable_mitb5_citysemsegformer_v2.0 - citySemSegformer mitb5 deployable to Deepstream.
trainable_fan_baseHybrid_citysemsegformer_v1.0 - citySemSegformer FAN-Base-Hybrid model trainable.
deployable_fan_baseHybrid_citysemsegformer_v1.0 - citySemSegformer FAN-Base-Hybrid deployable to Deepstream.

Training & Evaluation:

Training Dataset:

Data Collection Method by dataset:

Automatic/Sensors

Labeling Method by dataset:

Human

Properties:
\nProprietary dataset of 178,000 images. Most of the training dataset was collected in-house from images from a variety of dashcams and a small seed dataset containing images from traffic cameras in a city in the US. This content was chosen to auto-label urban city classes with segmentation masks.

\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	Distribution
Environment	Images	Cars	Persons	Road Signs	Two-Wheelers
Dashcam (5ft height)	128,000	1.7M	720,000	354,127	54,000
Traffic signal content	50,000	1.1M	53500	184000	11000
Total	178,000	2.8M	773,500	538,127	65,000

Evaluation Dataset:

Data Collection Method by dataset:

Automatic/Sensors

Labeling Method by dataset:

Human

Properties:
\n300 proprietary images that were hand labeled across a variety of environments.

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.

\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\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	CitySemSegFormer
Classes	MIOU
road	73.6
sidewalk	46.88
building	69.47
wall	50.44
fence	65.57
pole	61.47
traffic light	45.87
traffic sign	72.8
vegetation	77.4
terrain	38.19
sky	96.47
person	81.78
rider	33.07
car	90.14
truck	33.43
bus	95.44
train	3.2
motorcycle	54.44
bicycle	49.52

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.

BS - Batch Size

MiT-B5 SegFormer

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

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

Platform	BS	FPS
Jetson Orin Nano	1	1.36
Orin NX 16GB	1	1.9
AGX Orin 64GB	1	4.8
A2	1	5.8
T4	1	9.4
A30	4	29.3
L4	1	17.8
L40	1	47.3
A100	8	62.2
H100	8	108

FAN-Base-Hybrid SegFormer

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

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

Platform	BS	FPS
Jetson Orin Nano	1	1.2
Orin NX 16GB	1	1.78
AGX Orin 64GB	1	4.4
A2	1	4.4
T4	1	7.3
A30	4	23.7
L4	1	15.7
L40	1	40.9
A100	8	50.4
H100	8	89.5

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 primarily deploying and doing inference using DeepStream.

Primary use case intended for the model is segmenting urban city classes in a color (RGB) image. The model can be used to segment urban city transport/ setting 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.

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](@todo : update).

nvinfer_config.txt - File to configure inference settings for CitySemSegformer\nlabels.txt - Label file with 19 classes\n

You can input the onnx model directly to Deepstream. In order to manually convert to TRT engine, follow the example command below:

Key Parameters in nvinfer_config.txt for Deepstream Inference.

# 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\nnet-scale-factor=0.01735207357279195\noffsets=123.675;116.28;103.53\n# Since the model input channel is 3, using RGB color format.\nmodel-color-format=0\nlabelfile-path=./labels.txt\ninfer-dims=3;1024;1024\nbatch-size=1\n## 0=FP32, 1=INT8, 2=FP16 mode\nnetwork-mode=2\ninterval=0\ngie-unique-id=1\ncluster-mode=2\n## 0=Detector, 1=Classifier, 2=Semantic Segmentation, 3=Instance Segmentation, 100=Other\nnetwork-type=100\noutput-tensor-meta=1\nnum-detected-classes=20\nsegmentation-output-order=1\nroi-top-offset=0\nroi-bottom-offset=0\ndetected-min-w=0\ndetected-min-h=0\ndetected-max-w=0\ndetected-max-h=0\n

Run ds-tao-segmentation:

CitySemSegformer

ds-tao-segmentation -c configs/segformer_tao/nvinfer_config.txt -i file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_720p.mp4\n

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

Technical blogs

Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - Part 1 | Part 2
Learn how to train real-time License plate detection and recognition app with TAO and DeepStream.
Model accuracy is extremely important, learn how you can achieve state of the art accuracy for classification and object detection models using TAO
Learn how to train Instance segmentation model using MaskRCNN with TAO
Learn how to improve INT8 accuracy using Quantization aware training(QAT) with TAO
Read the technical tutorial on how PeopleNet model can be trained with custom data using Transfer Learning Toolkit
Learn how to train and deploy real-time intelligent video analytics apps and services using DeepStream SDK

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 sinternal 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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[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:** Transformer
\n**Network Architecture:** Segformer
\n\n## Input:\n**Input Type(s):** Image
\n**Input Format(s):** Red, Green, Blue (RGB)
\n**Input Parameters:** 3D
\n**Other Properties Related to Input:** RGB Fixed Resolution:1024x1024x3 (W x H x C); No minimum bit depth, alpha, or gamma.
\n\n## Output:\n**Output Type(s):** Semantic Segmentation Mask
\n**Output Format:** Segmentation Mask: 2D vector
\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- **trainable_mitb5_citysemsegformer_v1.0** - citySemSegformer mitB5 model trainable.\n- **deployable_mitb5_citysemsegformer_v2.0** - citySemSegformer mitb5 deployable to Deepstream.\n- **trainable_fan_baseHybrid_citysemsegformer_v1.0** - citySemSegformer FAN-Base-Hybrid model trainable.\n- **deployable_fan_baseHybrid_citysemsegformer_v1.0** - citySemSegformer FAN-Base-Hybrid 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 178,000 images. Most of the training dataset was collected in-house from images from a variety of dashcams and a small seed dataset containing images from traffic cameras in a city in the US. This content was chosen to auto-label urban city classes with segmentation masks.\n\n| | | Object| Distribution | | |\n|--|--|--|--|--|--|\n|Environment|Images|Cars|Persons|Road Signs|Two-Wheelers|\nDashcam (5ft height)|128,000|1.7M|720,000|354,127|54,000|\nTraffic signal content|50,000|1.1M|53500|184000|11000|\nTotal|178,000|2.8M|773,500|538,127|65,000|\n\n## Evaluation Dataset:\n**Data Collection Method by dataset:**
\n* Automatic/Sensors
\n\n**Labeling Method by dataset:**
\n* Human
\n\n**Properties:**
\n300 proprietary images that were hand labeled across a variety of environments. \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 | CitySemSegFormer |\n|---------------|------------------|\n| *Classes* | *MIOU* |\n| road | 73.6 |\n| sidewalk | 46.88 |\n| building | 69.47 |\n| wall | 50.44 |\n| fence | 65.57 |\n| pole | 61.47 |\n| traffic light | 45.87 |\n| traffic sign | 72.8 |\n| vegetation | 77.4 |\n| terrain | 38.19 |\n| sky | 96.47 |\n| person | 81.78 |\n| rider | 33.07 |\n| car | 90.14 |\n| truck | 33.43 |\n| bus | 95.44 |\n| train | 3.2 |\n| motorcycle | 54.44 |\n| bicycle | 49.52 |\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**BS** - Batch Size\n\nMiT-B5 SegFormer\n\n| Platform | BS | FPS |\n|------------------|----|------|\n| Jetson Orin Nano | 1 | 1.36 |\n| Orin NX 16GB | 1 | 1.9 |\n| AGX Orin 64GB | 1 | 4.8 |\n| A2 | 1 | 5.8 |\n| T4 | 1 | 9.4 |\n| A30 | 4 | 29.3 |\n| L4 | 1 | 17.8 |\n| L40 | 1 | 47.3 |\n| A100 | 8 | 62.2 |\n| H100 | 8 | 108 |\n\nFAN-Base-Hybrid SegFormer\n\n| Platform | BS | FPS |\n|------------------|----|------|\n| Jetson Orin Nano | 1 | 1.2 |\n| Orin NX 16GB | 1 | 1.78 |\n| AGX Orin 64GB | 1 | 4.4 |\n| A2 | 1 | 4.4 |\n| T4 | 1 | 7.3 |\n| A30 | 4 | 23.7 |\n| L4 | 1 | 15.7 |\n| L40 | 1 | 40.9 |\n| A100 | 8 | 50.4 |\n| H100 | 8 | 89.5 |\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 primarily deploying and doing inference using DeepStream.\n\nPrimary use case intended for the model is segmenting urban city classes in a color (RGB) image. The model can be used to segment urban city transport/ setting 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\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](@todo : update).\n\n```sh\nnvinfer_config.txt - File to configure inference settings for CitySemSegformer\nlabels.txt - Label file with 19 classes\n```\nYou can input the onnx model directly to Deepstream. In order to manually convert to TRT engine, follow the example command below:\n\n\nKey Parameters in `nvinfer_config.txt` for Deepstream Inference.\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\nnet-scale-factor=0.01735207357279195\noffsets=123.675;116.28;103.53\n# Since the model input channel is 3, using RGB color format.\nmodel-color-format=0\nlabelfile-path=./labels.txt\ninfer-dims=3;1024;1024\nbatch-size=1\n## 0=FP32, 1=INT8, 2=FP16 mode\nnetwork-mode=2\ninterval=0\ngie-unique-id=1\ncluster-mode=2\n## 0=Detector, 1=Classifier, 2=Semantic Segmentation, 3=Instance Segmentation, 100=Other\nnetwork-type=100\noutput-tensor-meta=1\nnum-detected-classes=20\nsegmentation-output-order=1\nroi-top-offset=0\nroi-bottom-offset=0\ndetected-min-w=0\ndetected-min-h=0\ndetected-max-w=0\ndetected-max-h=0\n```\n\nRun `ds-tao-segmentation`:\n\n#### CitySemSegformer\n```sh\nds-tao-segmentation -c configs/segformer_tao/nvinfer_config.txt -i file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_720p.mp4\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- Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - [Part 1](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-1) | [Part 2](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-2)\n- Learn how to train [real-time License plate detection and recognition app](https://developer.nvidia.com/blog/creating-a-real-time-license-plate-detection-and-recognition-app) with TAO and DeepStream.\n- Model accuracy is extremely important, learn how you can achieve [state of the art accuracy for classification and object detection models](https://developer.nvidia.com/blog/preparing-state-of-the-art-models-for-classification-and-object-detection-with-tao-toolkit/) using TAO\n- Learn how to train [Instance segmentation model using MaskRCNN with TAO](https://developer.nvidia.com/blog/training-instance-segmentation-models-using-maskrcnn-on-tao-toolkit/)\n- Learn how to improve INT8 accuracy using [Quantization aware training(QAT) with TAO](https://developer.nvidia.com/blog/improving-int8-accuracy-using-quantization-aware-training-and-tao-toolkit/)\n- Read the technical tutorial on how [PeopleNet model can be trained with custom data using Transfer Learning Toolkit](https://devblogs.nvidia.com/training-custom-pretrained-models-using-tlt/)\n- Learn how to [train and deploy real-time intelligent video analytics apps and services using DeepStream SDK](https://devblogs.nvidia.com/building-iva-apps-using-deepstream-5.0/)\n\n## Suggested reading \n\n- More information on about TAO Toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/tao-toolkit)\n- Read the [TAO 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 model on the edge using DeepStream. Learn more about [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk)\n\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 sinternal 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.","CitySemSegFormer",["D",1732745253358],"/api/image/nvidia/tao/models/citysemsegformer","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","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: | None of the Above |\n| Measures taken to mitigate against unwanted bias: | No measures taken to mitigate against unwanted bias.|","explainability","| Field | Response |\n| -- | -- |\n| Intended Application(s) & Domain(s): | Segmenting city scapes and and urban city classes. |\n| Model Type: | Semantic Segmentation |\n| Intended Users: | Developers working in geo-spatial and transportation applications. |\n| Output: | Segmentation Masks |\n| Describe how the model works: | Associates urban cityscapes label for each image pixel. |\n| Technical Limitations: | Model may not detect classes of persons (passengers, riders), trucks, trains, motorcycles, and other out-of-road elements found in intelligent transportation system datasets. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Accuracy, Mean Intersection over Union (IoU), Precision, Recall |\n| Potential Known Risks: | Inaccurate segmentation for navigation could lead to collisions. |\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, all data 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/data-center/products/nvidia-ai-enterprise/eula/](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/) |","safetyAndSecurity","| Field | Response |\n| -- | -- |\n| Model Application(s): | Semantic Segmentation |\n| Describe the life-critical application (if present). | Potential for life-critical impact in navigating traversable 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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This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. 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This function remembers how far the user has scrolled on the current session across the website's sub-pages.","dmn_chk_-xxxxxxxxxxxx","com","Posthog Analytics","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_cs_id","blogs.nvidia.com","_session_id","resources.nvidia.com","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. 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