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Field	Response
Participation considerations from adversely impacted groups (protected classes) in model design and testing:	None of the Above
Measures taken to mitigate against unwanted bias:	No measures taken to mitigate against unwanted bias.

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License Plate Detection (LPDNet) Model Card

Description:

LPDNet detects license plates from an image. This model is ready for commercial use.

References:

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: DetectNet_v2 ; YOLOv4-tiny

DetectNet_v2 models are based on NVIDIA DetectNet_v2 detector with ResNet18 as feature extractor. This architecture, also known as GridBox object detection, uses bounding-box regression on a uniform grid on the input image. Gridbox system divides an input image into a grid which predicts four normalized bounding-box parameters (xc, yc, w, h) and confidence value per output class.The raw normalized bounding-box and confidence detections needs to be post-processed by a clustering algorithm such as DBSCAN or NMS to produce final bounding-box coordinates and category labels.

YOLOv4-tiny models are based on YOLOv4-tiny detector with cspdarknet_tiny as feature extractor.

Input:

Input Type(s): Images
\nInput Format(s): Red, Green, Blue (RGB)
\nInput Parameters: 4D
\nOther Properties Related to Input:

\n
For US license plate
\n
- Color Images of resolution 640 X 480 X 3 (W x H x C)
- Channel Ordering of the Input: NCHW, where N = Batch Size, C = number of channels (3), H = Height of images (480), W = Width of the images (640)
- Input scale: 1/255.0
- Mean subtraction: None
\n
\n
For Chinese license plate: Color Images of resolution 720 X 1168 X 3
\n
- Color Images of resolution 720 X 1168 X 3 (W x H x C)
- Channel Ordering of the Input: NCHW, where N = Batch Size, C = number of channels (3), H = Height of images (1168), W = Width of the images (720)
- Input scale: 1/255.0
- Mean subtraction: None
\n

Output:

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

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):

unpruned_v1.0 - ResNet18 based pre-trained model. Intended for training.
pruned_v1.0 - ResNet18 deployment models. Contains calibration cache for GPU and DLA. DLA one is required if running inference on Jetson AGX Xavier or Xavier NX DLA.
unpruned_v2.0 - CSPDarkNet-Tiny based YOLOv4-Tiny pre-trained model. Intended for training.
pruned_v1.0 - CSPDarkNet-Tiny based YOLOv4-Tiny deployment model. Contains calibration cache for INT8 deployment on GPU and DLA.

Training & Evaluation:

Training Dataset:

Data Collection Method by dataset:

Automatic/Sensors

Labeling Method by dataset:

Human

Properties:
\nLPRNet model has been trained on two datasets:

Internal, proprietary dataset of 45,000 US license plates images taken at various angles and illumination collected from dash and side cameras of a vehicle.
172,000 images from the Chinese City Parking Dataset (CCPD) of a provincial capital of China.

Evaluation Dataset:

Data Collection Method by dataset:

Automatic/Sensors

Labeling Method by dataset:

Human
\nProperties:
\n42,000 images at different angles, illumination and sharpness from the Chinese City Parking Dataset (CCPD) of a provincial capital of China.

Methodology and KPI

The key performance indicator is the accuracy of license plate detection.The KPI for the evaluation data are reported in the table below.

Version 1.0 models (DetectNet_v2)

\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	Dataset	Accuracy
usa_unpruned_model	NVIDIA 3k LPD eval dataset	98.58%
usa_pruned_model	NVIDIA 3k LPD eval dataset	98.46%
ccpd_unpruned_model	14% of CCPD-Base dataset	99.24%
ccpd_pruned_model	14% of CCPD-Base dataset	99.22%

Version 2.0 models (YoloV4-Tiny)

\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	Dataset	Accuracy
yolov4_tiny_usa_trainable_model	NVIDIA 3k LPD eval dataset	99.53%
yolov4_tiny_usa_deployable_model	NVIDIA 3k LPD eval dataset	99.61%
yolov4_tiny_ccpd_trainable_model	14% of CCPD-Base dataset	99.99%
yolov4_tiny_ccpd_deployable_model	14% of CCPD-Base dataset	99.98%

Version 2.0 models based on YoloV4-Tiny provides accuracy improvement over the version 1.0 models

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(deployable) models at INT8 precision. On the Jetson Nano FP16 precision is used. The inference performance runs with trtexec on Jetson Nano, Jetson TX2, AGX Xavier, Xavier NX and NVIDIA T4 GPU. The Jetson devices run at Max-N configuration for maximum system performance. The performance shown below is only for inference of the usa pruned(deployable) model. The end-to-end performance with streaming video data might slightly vary depending on use cases of applications.

Version 2.0 (YoloV4-tiny) models provide higher accuracy for different use cases, but comes at a cost of inference performance. The inference performance for v2.0 model is lower than v1.0 model.\nFor version 1.0 (DetectNet_v2) based models,

\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\">

Device	Precision	Batch_size	FPS
Nano	FP16	1	66
TX2	INT8	1	187
NX	INT8	1	461
Xavier	INT8	1	913
T4	INT8	1	2748

For Version 2.0 (YOLOv4-tiny) based models,

\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\">

Device	Precision	Batch_size	FPS
Nano	FP16	1	40
TX2	INT8	1	105
NX	INT8	1	225
Xavier	INT8	1	489
T4	INT8	1	1264

How to use Version 1.0 (Detectnet_v2) based 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.

For Version 1.0 (Detectnet_v2) based model, there are four models provided:

usa_unpruned.tlt
usa_pruned.etlt
ccpd_unpruned.tlt
ccpd_pruned.etlt

The unpruned models are intended for training and fine-tuning using TAO Toolkit along with the user's dataset of license plates in United States of America or China. High fidelity models can be trained and adapted to the use case. The DetectNet_v2 Jupyter notebook available as a part of TAO CV resources can be used to re-train.

The usa pruned models are intended for easy deployment to the edge using DeepStream SDK or TensorRT. They accept 640x480x3 dimension input tensors and outputs 40x30x12 bbox coordinate tensor and 40x30x3 class confidence tensor.

The ccpd pruned models are intended for easy deployment to the edge using DeepStream SDK or TensorRT. These models accept 720x1168x3 dimension input tensors and outputs 45x73x12 bbox coordinate tensor and 45x73x3 class confidence tensor.

DeepStream provides a toolkit to create efficient video analytics pipelines to capture, decode, and pre-process the data before running inference. DeepStream will then post-process the output bbox coordinate tensor and class confidence tensors with NMS or DBScan clustering algorithm to create appropriate bounding boxes. The sample application and config file to run these models are provided in DeepStream SDK.

The unpruned and pruned models are encrypted and can be decrypted 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 version 1.0 unpruned model with TAO

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

For ResNet18

model_config {\n  num_layers: 18\n  pretrained_model_file: \"/path/to/the/model.tlt\"\n  use_batch_norm: true\n  objective_set {\n    bbox {\n      scale: 35.0\n      offset: 0.5\n    }\n    cov {\n    }\n  }\n  training_precision {\n    backend_floatx: FLOAT32\n  }\n  arch: \"resnet\"\n}\n

Instructions to deploy version 1.0 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 6.0, 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/deepstream-6.0/samples/configs/tao_pretrained_models\n

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

You need to create 1 label file and 2 config files.

labels_lpdnet.txt - Label file with 1 class\ndeepstream_app_source1_trafficcamnet_lpdnet.txt - Main config file for DeepStream app\nconfig_infer_secondary_lpdnet.txt - File to configure inference settings \n

Create label file labels_lpdnet.txt

labels_lpdnet.txt\n\">

echo lpd > labels_lpdnet.txt\n

Create config file deepstream_app_source1_trafficcamnet_lpdnet.txt

cp deepstream_app_source1_trafficcamnet.txt deepstream_app_source1_trafficcamnet_lpdnet.txt\n

Modify config file deepstream_app_source1_trafficcamnet_lpdnet.txt. Add below lines in it.

[secondary-gie0]\nenable=1\nmodel-engine-file=usa_pruned.etlt_b4_gpu0_int8.engine\ngpu-id=0\nbatch-size=4\ngie-unique-id=4\noperate-on-gie-id=1\noperate-on-class-ids=0;\nconfig-file=config_infer_secondary_lpdnet.txt\n

Create config file config_infer_secondary_lpdnet.txt

\ntlt-encoded-model=\ntlt-model-key=nvidia_tlt\nint8-calib-file=\nuff-input-dims=3;480;640;0 #For us model, set to 3;480;640;0 For ccpd model, set to 3;1168;720;0\nuff-input-blob-name=input_1\nbatch-size=16\n## 0=FP32, 1=INT8, 2=FP16 mode\nnetwork-mode=1\nnum-detected-classes=1\n##1 Primary 2 Secondary\nprocess-mode=2\ninterval=0\ngie-unique-id=2\n#0 detector 1 classifier 2 segmentatio 3 instance segmentation\nnetwork-type=0\noperate-on-gie-id=1\noperate-on-class-ids=0\ncluster-mode=3\noutput-blob-names=output_cov/Sigmoid;output_bbox/BiasAdd\ninput-object-min-height=30\ninput-object-min-width=40\n#enable-dla=1\n\n[class-attrs-all]\npre-cluster-threshold=0.3\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\">

[property]\ngpu-id=0\nnet-scale-factor=0.0039215697906911373\nmodel-color-format=0\nlabelfile-path=<path to labels_lpdnet.txt>\ntlt-encoded-model=<path to etao_model>\ntlt-model-key=nvidia_tlt\nint8-calib-file=<path to calibration cache>\nuff-input-dims=3;480;640;0  #For us model, set to 3;480;640;0  For ccpd model, set to 3;1168;720;0\nuff-input-blob-name=input_1\nbatch-size=16\n## 0=FP32, 1=INT8, 2=FP16 mode\nnetwork-mode=1\nnum-detected-classes=1\n##1 Primary 2 Secondary\nprocess-mode=2\ninterval=0\ngie-unique-id=2\n#0 detector 1 classifier 2 segmentatio 3 instance segmentation\nnetwork-type=0\noperate-on-gie-id=1\noperate-on-class-ids=0\ncluster-mode=3\noutput-blob-names=output_cov/Sigmoid;output_bbox/BiasAdd\ninput-object-min-height=30\ninput-object-min-width=40\n#enable-dla=1\n\n[class-attrs-all]\npre-cluster-threshold=0.3\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 deepstream-app:

deepstream-app -c deepstream_app_source1_trafficcamnet_lpdnet.txt\n

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

How to use Version 2.0 (YOLOv4-tiny) based model

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

For YOLOv4-tiny based model, totally there are four models provided:

yolov4_tiny_usa_trainable.tlt
yolov4_tiny_usa_deployable.etlt
yolov4_tiny_ccpd_trainable.tlt
yolov4_tiny_ccpd_deployable.etlt

The unpruned models are intended for training and fine-tuning using TAO Toolkit along with the user's dataset of license plates in United States of America or China. High fidelity models can be trained and adapted to the use case. The YoloV4-Tiny Jupyter notebook available as a part of TAO CV resources can be used to re-train.

The usa deployable models are intended for easy deployment to the edge using DeepStream SDK or TensorRT.

The trainable and deployable models are encrypted and can be decrypted 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 version 2.0 trainable 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 training_config component of the experiment spec file to train a YOLOv4-tiny model. For more information on the experiment spec file, please refer to the TAO Toolkit YoloV4 Tiny Guide.

Set pretrain_model_path in training_config section

training_config {\n  pretrain_model_path: \"/path/to/the/yolov4_tiny_usa_trainable.tlt\"\n  ...\n}\n

Instructions to deploy version 2.0 models with DeepStream

To deploy these models with DeepStream 6.0, 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/deepstream-6.0/samples/configs/tao_pretrained_models\n

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

You need to create 1 label file and 2 config files.

labels_lpdnet.txt - Label file with 1 class\ndeepstream_app_source1_trafficcamnet_lpdnet.txt - Main config file for DeepStream app\nconfig_infer_secondary_lpdnet.txt - File to configure inference settings\n

Create label file labels_lpdnet.txt

labels_lpdnet.txt\n\">

echo lpd > labels_lpdnet.txt\n

Create config file deepstream_app_source1_trafficcamnet_lpdnet.txt

cp deepstream_app_source1_trafficcamnet.txt deepstream_app_source1_trafficcamnet_lpdnet.txt\n

Modify config file deepstream_app_source1_trafficcamnet_lpdnet.txt. Add below lines in it.

[secondary-gie0]\nenable=1\nmodel-engine-file=yolov4_tiny_usa_deployable.etlt_b4_gpu0_int8.engine\ngpu-id=0\nbatch-size=4\ngie-unique-id=4\noperate-on-gie-id=1\noperate-on-class-ids=0;\nconfig-file=config_infer_secondary_lpdnet.txt\n

Create config file config_infer_secondary_lpdnet.txt

\ntlt-encoded-model=\ntlt-model-key=nvidia_tlt\nint8-calib-file=\nuff-input-dims=3;480;640;0 #For ccpd models, please set to 3;1168;720;0\nuff-input-blob-name=Input\nbatch-size=16\n## 0=FP32, 1=INT8, 2=FP16 mode\nnetwork-mode=1\nnum-detected-classes=1\n##1 Primary 2 Secondary\nprocess-mode=2\ninterval=0\ngie-unique-id=2\n#0 detector 1 classifier 2 segmentatio 3 instance segmentation\nnetwork-type=0\noperate-on-gie-id=1\noperate-on-class-ids=0\ncluster-mode=3\noutput-blob-names=BatchedNMS\nparse-bbox-func-name=NvDsInferParseCustomBatchedNMSTLT\ncustom-lib-path=deepstream_tao_apps/post_processor/libnvds_infercustomparser_tao.so\n#enable-dla=1\n\n[class-attrs-all]\npre-cluster-threshold=0.3\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\">

[property]\ngpu-id=0\nnet-scale-factor=1.0\noffsets=103.939;116.779;123.68\nmodel-color-format=1\nlabelfile-path=<path to labels_lpdnet.txt>\ntlt-encoded-model=<path to deployable_model>\ntlt-model-key=nvidia_tlt\nint8-calib-file=<path to calibration cache>\nuff-input-dims=3;480;640;0  #For ccpd models, please set to 3;1168;720;0\nuff-input-blob-name=Input\nbatch-size=16\n## 0=FP32, 1=INT8, 2=FP16 mode\nnetwork-mode=1\nnum-detected-classes=1\n##1 Primary 2 Secondary\nprocess-mode=2\ninterval=0\ngie-unique-id=2\n#0 detector 1 classifier 2 segmentatio 3 instance segmentation\nnetwork-type=0\noperate-on-gie-id=1\noperate-on-class-ids=0\ncluster-mode=3\noutput-blob-names=BatchedNMS\nparse-bbox-func-name=NvDsInferParseCustomBatchedNMSTLT\ncustom-lib-path=deepstream_tao_apps/post_processor/libnvds_infercustomparser_tao.so\n#enable-dla=1\n\n[class-attrs-all]\npre-cluster-threshold=0.3\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

Build libnvds_infercustomparser_tao.so

git clone -b release/tao3.0  https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git\ncd deepstream_tao_apps/post_processor/\nexport CUDA_VER=xy.z                  // xy.z is CUDA version, e.g. 11.4\nmake\ncd -\n

Run deepstream-app:

deepstream-app -c deepstream_app_source1_trafficcamnet_lpdnet.txt\n

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

Technical blogs

Learn how to train real-time License plate detection and recognition app with TAO and DeepStream.
Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - Part 1 | Part 2
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:

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\n#### Using TAO Pre-trained Models \n\n- Get [TAO Container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit)\n- Get other purpose-built models from 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### Model Architecture:\n\n**Architecture Type:** Convolution Neural Network (CNN)
\n**Network Architecture:** DetectNet_v2 ; YOLOv4-tiny
\n\nDetectNet_v2 models are based on NVIDIA DetectNet_v2 detector with ResNet18 as feature extractor. This architecture, also known as GridBox object detection, uses bounding-box regression on a uniform grid on the input image. Gridbox system divides an input image into a grid which predicts four normalized bounding-box parameters (xc, yc, w, h) and confidence value per output class.The raw normalized bounding-box and confidence detections needs to be post-processed by a clustering algorithm such as DBSCAN or NMS to produce final bounding-box coordinates and category labels.\n\nYOLOv4-tiny models are based on YOLOv4-tiny detector with cspdarknet_tiny as feature extractor.\n\n### Input:\n\n**Input Type(s):** Images
\n**Input Format(s):** Red, Green, Blue (RGB)
\n**Input Parameters:** 4D
\n**Other Properties Related to Input:**
\n* For US license plate\n\n - Color Images of resolution 640 X 480 X 3 (W x H x C)\n - Channel Ordering of the Input: NCHW, where N = Batch Size, C = number of channels (3), H = Height of images (480), W = Width of the images (640)\n - Input scale: 1/255.0\n - Mean subtraction: None\n* For Chinese license plate: Color Images of resolution 720 X 1168 X 3\n\n - Color Images of resolution 720 X 1168 X 3 (W x H x C)\n - Channel Ordering of the Input: NCHW, where N = Batch Size, C = number of channels (3), H = Height of images (1168), W = Width of the images (720)\n - Input scale: 1/255.0\n - Mean subtraction: None\n\n### Output:\n\n**Output Type(s):** Label(s), Bounding-Box(es), Confidence Scores
\n**Output Format:** Label: Text String(s); Bounding Box: (x-coordinate, y-coordinate, width, height), Confidence Scores: Floating Point
\n**Other Properties Related to Output:** Category Label(s): License Plate characters, Bounding Box Coordinates, Confidence Scores
\n\n### Software Integration:\n\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\n- **unpruned_v1.0** - ResNet18 based pre-trained model. Intended for training.\n- **pruned_v1.0** - ResNet18 deployment models. Contains calibration cache for GPU and DLA. DLA one is required if running inference on Jetson AGX Xavier or Xavier NX DLA.\n- **unpruned_v2.0** - CSPDarkNet-Tiny based YOLOv4-Tiny pre-trained model. Intended for training.\n- **pruned_v1.0** - CSPDarkNet-Tiny based YOLOv4-Tiny deployment model. Contains calibration cache for INT8 deployment on GPU and DLA.\n\n### Training & Evaluation:\n\n### Training Dataset:\n\n**Data Collection Method by dataset:**
\n* Automatic/Sensors
\n\n**Labeling Method by dataset:**
\n* Human
\n\n**Properties:**
\nLPRNet model has been trained on two datasets:\n* Internal, proprietary dataset of 45,000 US license plates images taken at various angles and illumination collected from dash and side cameras of a vehicle.\n* 172,000 images from the [Chinese City Parking Dataset (CCPD)](https://github.com/detectRecog/CCPD) of a provincial capital of China.\n\n### Evaluation Dataset:\n\n**Data Collection Method by dataset:**
\n* Automatic/Sensors
\n\n**Labeling Method by dataset:**
\n* Human
\n **Properties:**
\n 42,000 images at different angles, illumination and sharpness from the [Chinese City Parking Dataset (CCPD)](https://github.com/detectRecog/CCPD) of a provincial capital of China.\n\n#### Methodology and KPI\n\nThe key performance indicator is the accuracy of license plate detection.The KPI for the evaluation data are reported in the table below.\n\nVersion 1.0 models (DetectNet_v2)\n\n| Model | Dataset | Accuracy |\n| ---- | ---- | ---- |\n| usa_unpruned_model | NVIDIA 3k LPD eval dataset |98.58%|\n| usa_pruned_model | NVIDIA 3k LPD eval dataset | 98.46% |\n| ccpd_unpruned_model | 14% of CCPD-Base dataset | 99.24%|\n| ccpd_pruned_model | 14% of CCPD-Base dataset | 99.22% |\n\nVersion 2.0 models (YoloV4-Tiny)\n\n| Model | Dataset | Accuracy |\n| ---- | ---- | ---- |\n| yolov4_tiny_usa_trainable_model | NVIDIA 3k LPD eval dataset |99.53%|\n| yolov4_tiny_usa_deployable_model | NVIDIA 3k LPD eval dataset | 99.61% |\n| yolov4_tiny_ccpd_trainable_model | 14% of CCPD-Base dataset |99.99%|\n| yolov4_tiny_ccpd_deployable_model | 14% of CCPD-Base dataset | 99.98% |\n\nVersion 2.0 models based on YoloV4-Tiny provides accuracy improvement over the version 1.0 models\n\n### Inference:\n\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(deployable) models at INT8 precision. On the Jetson Nano FP16 precision is used. The inference performance runs with [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/master/samples/trtexec) on Jetson Nano, Jetson TX2, AGX Xavier, Xavier NX and NVIDIA T4 GPU. The Jetson devices run at Max-N configuration for maximum system performance. The performance shown below is only for inference of the usa pruned(deployable) model. The end-to-end performance with streaming video data might slightly vary depending on use cases of applications.\n\nVersion 2.0 (YoloV4-tiny) models provide higher accuracy for different use cases, but comes at a cost of inference performance. The inference performance for v2.0 model is lower than v1.0 model.\nFor version 1.0 (DetectNet_v2) based models,\n\n| Device | Precision | Batch_size | FPS |\n| ---- | ---- | ---- | --- |\n| Nano | FP16 |1| 66 |\n| TX2 | INT8 | 1 | 187 |\n| NX | INT8 | 1 | 461|\n| Xavier | INT8 | 1 | 913 |\n| T4 | INT8 | 1 | 2748 |\n\nFor Version 2.0 (YOLOv4-tiny) based models,\n\n| Device | Precision | Batch_size | FPS |\n| ---- | ---- | ---- | --- |\n| Nano | FP16 |1| 40 |\n| TX2 | INT8 | 1 | 105 |\n| NX | INT8 | 1 | 225|\n| Xavier | INT8 | 1 | 489 |\n| T4 | INT8 | 1 | 1264 |\n\n### How to use Version 1.0 (Detectnet_v2) based model \n\nThese models need to be used with NVIDIA Hardware and Software. For Hardware, the models can run on any NVIDIA GPU including NVIDIA Jetson devices. These models can only be used with [Train Adapt Optimize (TAO) Toolkit](https://developer.nvidia.com/tao-toolkit), [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) or [TensorRT](https://developer.nvidia.com/tensorrt).\n\nFor Version 1.0 (Detectnet_v2) based model, there are four models provided:\n\n- usa_unpruned.tlt\n- usa_pruned.etlt\n- ccpd_unpruned.tlt\n- ccpd_pruned.etlt\n\nThe `unpruned` models are intended for training and fine-tuning using TAO Toolkit along with the user's dataset of license plates in United States of America or China. High fidelity models can be trained and adapted to the use case. The DetectNet_v2 Jupyter notebook available as a part of [TAO CV resources](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/resources/cv_samples) can be used to re-train.\n\nThe usa `pruned` models are intended for easy deployment to the edge using DeepStream SDK or TensorRT. They accept `640x480x3` dimension input tensors and outputs `40x30x12` bbox coordinate tensor and `40x30x3` class confidence tensor.\n\nThe ccpd `pruned` models are intended for easy deployment to the edge using DeepStream SDK or TensorRT. These models accept `720x1168x3` dimension input tensors and outputs `45x73x12` bbox coordinate tensor and `45x73x3` class confidence tensor.\n\nDeepStream provides a toolkit to create efficient video analytics pipelines to capture, decode, and pre-process the data before running inference. DeepStream will then post-process the output bbox coordinate tensor and class confidence tensors with NMS or DBScan clustering algorithm to create appropriate bounding boxes. The sample application and config file to run these models are provided in DeepStream SDK.\n\nThe `unpruned` and `pruned` models are encrypted and can be decrypted 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 version 1.0 unpruned model with TAO\n\nIn order to use these models as a pretrained weights for transfer learning, please use the snippet below as template for the `model_config` component of the experiment spec file to train a DetectNet_v2 model. For more information on the experiment spec file, please refer to the [TAO Toolkit DetectNet_v2 Guide](https://docs.nvidia.com/tao/tao-toolkit/text/object_detection/detectnet_v2.html).\n\n1. For ResNet18\n\n```py\nmodel_config {\n num_layers: 18\n pretrained_model_file: \"/path/to/the/model.tlt\"\n use_batch_norm: true\n objective_set {\n bbox {\n scale: 35.0\n offset: 0.5\n }\n cov {\n }\n }\n training_precision {\n backend_floatx: FLOAT32\n }\n arch: \"resnet\"\n}\n```\n\n#### Instructions to deploy version 1.0 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 6.0](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```sh\n/opt/nvidia/deepstream/deepstream-6.0/samples/configs/tao_pretrained_models\n```\n\n`/opt/nvidia/deepstream` is the default DeepStream installation directory. This path will be different if you are installing in a different directory.\n\nYou need to create 1 label file and 2 config files.\n\n```sh\nlabels_lpdnet.txt - Label file with 1 class\ndeepstream_app_source1_trafficcamnet_lpdnet.txt - Main config file for DeepStream app\nconfig_infer_secondary_lpdnet.txt - File to configure inference settings \n```\n\nCreate label file `labels_lpdnet.txt`\n\n```sh\necho lpd > labels_lpdnet.txt\n```\n\nCreate config file `deepstream_app_source1_trafficcamnet_lpdnet.txt`\n\n```sh\ncp deepstream_app_source1_trafficcamnet.txt deepstream_app_source1_trafficcamnet_lpdnet.txt\n```\n\nModify config file `deepstream_app_source1_trafficcamnet_lpdnet.txt`. Add below lines in it.\n\n```sh\n[secondary-gie0]\nenable=1\nmodel-engine-file=usa_pruned.etlt_b4_gpu0_int8.engine\ngpu-id=0\nbatch-size=4\ngie-unique-id=4\noperate-on-gie-id=1\noperate-on-class-ids=0;\nconfig-file=config_infer_secondary_lpdnet.txt\n```\n\nCreate config file `config_infer_secondary_lpdnet.txt`\n\n```sh\n[property]\ngpu-id=0\nnet-scale-factor=0.0039215697906911373\nmodel-color-format=0\nlabelfile-path=\ntlt-encoded-model=\ntlt-model-key=nvidia_tlt\nint8-calib-file=\nuff-input-dims=3;480;640;0 #For us model, set to 3;480;640;0 For ccpd model, set to 3;1168;720;0\nuff-input-blob-name=input_1\nbatch-size=16\n## 0=FP32, 1=INT8, 2=FP16 mode\nnetwork-mode=1\nnum-detected-classes=1\n##1 Primary 2 Secondary\nprocess-mode=2\ninterval=0\ngie-unique-id=2\n#0 detector 1 classifier 2 segmentatio 3 instance segmentation\nnetwork-type=0\noperate-on-gie-id=1\noperate-on-class-ids=0\ncluster-mode=3\noutput-blob-names=output_cov/Sigmoid;output_bbox/BiasAdd\ninput-object-min-height=30\ninput-object-min-width=40\n#enable-dla=1\n\n[class-attrs-all]\npre-cluster-threshold=0.3\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 `deepstream-app`:\n\n```sh\ndeepstream-app -c deepstream_app_source1_trafficcamnet_lpdnet.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### How to use Version 2.0 (YOLOv4-tiny) based model \n\nThese models need to be used with NVIDIA Hardware and Software. For Hardware, the models can run on any NVIDIA GPU including NVIDIA Jetson devices. These models can only be used with [Train Adapt Optimize (TAO) Toolkit](https://developer.nvidia.com/tao-toolkit), [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) or [TensorRT](https://developer.nvidia.com/tensorrt).\n\nPrimary use case intended for these models is detecting license plates in a color (RGB) image. The model can be used to detect license plates from photos and videos by using appropriate video or image decoding and pre-processing.\n\nFor YOLOv4-tiny based model, totally there are four models provided:\n\n- yolov4_tiny_usa_trainable.tlt\n- yolov4_tiny_usa_deployable.etlt\n- yolov4_tiny_ccpd_trainable.tlt\n- yolov4_tiny_ccpd_deployable.etlt\n\nThe `unpruned` models are intended for training and fine-tuning using TAO Toolkit along with the user's dataset of license plates in United States of America or China. High fidelity models can be trained and adapted to the use case. The YoloV4-Tiny Jupyter notebook available as a part of [TAO CV resources](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/resources/cv_samples) can be used to re-train.\n\nThe usa `deployable` models are intended for easy deployment to the edge using DeepStream SDK or TensorRT.\n\nThe `trainable` and `deployable` models are encrypted and can be decrypted 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 version 2.0 trainable 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 `training_config` component of the experiment spec file to train a YOLOv4-tiny model. For more information on the experiment spec file, please refer to the [TAO Toolkit YoloV4 Tiny Guide](https://docs.nvidia.com/tao/tao-toolkit/text/object_detection/yolo_v4_tiny.html).\n\n1. Set pretrain_model_path in training_config section\n\n```py\ntraining_config {\n pretrain_model_path: \"/path/to/the/yolov4_tiny_usa_trainable.tlt\"\n ...\n}\n```\n\n#### Instructions to deploy version 2.0 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 6.0](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```sh\n/opt/nvidia/deepstream/deepstream-6.0/samples/configs/tao_pretrained_models\n```\n\n`/opt/nvidia/deepstream` is the default DeepStream installation directory. This path will be different if you are installing in a different directory.\n\nYou need to create 1 label file and 2 config files.\n\n```sh\nlabels_lpdnet.txt - Label file with 1 class\ndeepstream_app_source1_trafficcamnet_lpdnet.txt - Main config file for DeepStream app\nconfig_infer_secondary_lpdnet.txt - File to configure inference settings\n```\n\nCreate label file `labels_lpdnet.txt`\n\n```sh\necho lpd > labels_lpdnet.txt\n```\n\nCreate config file `deepstream_app_source1_trafficcamnet_lpdnet.txt`\n\n```sh\ncp deepstream_app_source1_trafficcamnet.txt deepstream_app_source1_trafficcamnet_lpdnet.txt\n```\n\nModify config file `deepstream_app_source1_trafficcamnet_lpdnet.txt`. Add below lines in it.\n\n```sh\n[secondary-gie0]\nenable=1\nmodel-engine-file=yolov4_tiny_usa_deployable.etlt_b4_gpu0_int8.engine\ngpu-id=0\nbatch-size=4\ngie-unique-id=4\noperate-on-gie-id=1\noperate-on-class-ids=0;\nconfig-file=config_infer_secondary_lpdnet.txt\n```\n\nCreate config file `config_infer_secondary_lpdnet.txt`\n\n```sh\n[property]\ngpu-id=0\nnet-scale-factor=1.0\noffsets=103.939;116.779;123.68\nmodel-color-format=1\nlabelfile-path=\ntlt-encoded-model=\ntlt-model-key=nvidia_tlt\nint8-calib-file=\nuff-input-dims=3;480;640;0 #For ccpd models, please set to 3;1168;720;0\nuff-input-blob-name=Input\nbatch-size=16\n## 0=FP32, 1=INT8, 2=FP16 mode\nnetwork-mode=1\nnum-detected-classes=1\n##1 Primary 2 Secondary\nprocess-mode=2\ninterval=0\ngie-unique-id=2\n#0 detector 1 classifier 2 segmentatio 3 instance segmentation\nnetwork-type=0\noperate-on-gie-id=1\noperate-on-class-ids=0\ncluster-mode=3\noutput-blob-names=BatchedNMS\nparse-bbox-func-name=NvDsInferParseCustomBatchedNMSTLT\ncustom-lib-path=deepstream_tao_apps/post_processor/libnvds_infercustomparser_tao.so\n#enable-dla=1\n\n[class-attrs-all]\npre-cluster-threshold=0.3\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\nBuild libnvds_infercustomparser_tao.so\n\n```sh\ngit clone -b release/tao3.0 https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git\ncd deepstream_tao_apps/post_processor/\nexport CUDA_VER=xy.z // xy.z is CUDA version, e.g. 11.4\nmake\ncd -\n```\n\nRun `deepstream-app`:\n\n```sh\ndeepstream-app -c deepstream_app_source1_trafficcamnet_lpdnet.txt\n```\n\nDocumentation to deploy with DeepStream is provided in [Deploying to DeepStream](https://docs.nvidia.com/tao/tao-toolkit/text/ds_tao/deepstream_tao_integration.html) section of TAO User Guide.\n\n### Technical blogs \n\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- 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- 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/) 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### Ethical Considerations:\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","LPDNet",["D",1731434119217],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/lpdnet","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",1783847,"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: | 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): | Detecting vehicle license plates for vehicle identification and traffic management |\n| Model Type: | Object Detection |\n| Intended Users: | Developers working in smart city and transportation applications. |\n| Output: | Bounding Box(es), Category Label(s), Confidence Score(s) |\n| Describe how the model works: | Detects and one or more license plates within an image and returns respective bounding box coordinates, labels, and confidence scores. |\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 is not as accurate with monochrome or infrared images. Model accuracy may vary with occlusion or truncation. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Precision |\n| Potential Known Risks: | This model may fail to detect vehicle license plates. |\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, some data was collected by NVIDIA. Some data was supplemented by external resources. |\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, for data collected by NVIDIA. No, for all externally-sourced data. |\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): | Vehicle License Plate Detection |\n| Describe the life-critical application (if present). | None |\n| Use Case Restrictions: | Should not be used in \"Critical Applications;\" reference the [NVIDIA AI Enterprise End User License Agreement](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",[],"displayLabels",[224,225,226,227,228,229],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_186":246,"_231":247,"_232":248,"_234":235},{"_186":244,"_231":244,"_232":245,"_234":235},{"_186":242,"_231":242,"_232":243,"_234":235},{"_186":240,"_231":240,"_232":241,"_234":235},{"_186":238,"_231":238,"_232":239,"_234":235},{"_186":230,"_231":230,"_232":233,"_234":235},"TAO Toolkit","value","filter",{"_236":237,"_231":230},"category","label","field","framework","TAO",{"_236":235,"_231":238},"NSPECT-2A4D-MPST",{"_236":235,"_231":240},"Deep Learning",{"_236":235,"_231":242},"CV",{"_236":235,"_231":244},"Computer Vision","soln_computer_vision",{"_236":249,"_231":247},"technology","appConfig",{"_1386":1387,"_284":1388},"ngcApps",[1339],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1320":1321,"_1322":1323,"_1324":1325,"_1326":1327,"_1328":262,"_79":291,"_1329":1330,"_1331":44,"_1332":93,"_1333":93,"_1334":1335,"_1336":93,"_1337":1338},"amplitude",{"_1317":1318,"_926":-7,"_1319":20},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0yJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1311":1312,"_1313":1314,"_1315":1316},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","https://api.ngc.nvidia.com","ngcHelmOrigin","https://helm.ngc.nvidia.com","origin","https://catalog.ngc.nvidia.com","user","usesMocks","theme","year",2026,"now",1794506674199,"1.31.0","defaultSearchSuggestions",{"_1196":115,"_1197":1198,"_1199":1200,"_1201":1202},"announcements",[],"consentState",{"_328":1186,"_1187":20,"_1188":20,"_1189":93,"_1190":1191},"consentConfig",{"_310":311,"_312":313,"_314":315,"_316":317,"_318":319,"_320":321,"_322":323,"_324":325,"_326":327,"_328":329},"showContactAdmin",[],[],[304,305,306,307,308,309],{"_33":48,"_35":57,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":55,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":51,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":49,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":45,"_35":46,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_43":44},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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The main business activity is: Doubleclick is Googles real time bidding advertising exchange","test_cookie","bing.com",[389],{"_155":390,"_376":387,"_377":20,"_114":391},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[394,395],{"_155":398,"_376":392,"_377":20,"_114":397},{"_155":396,"_376":392,"_377":20,"_114":397},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_*","If you know anything about this host and its uses please add your contributions here. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","tiktok.com",[401],{"_155":402,"_376":399,"_377":20,"_114":403},"_ttp","TikTok is a social media platform owned by ByteDance, known for short-form video sharing. The service is used to create and share videos, and it utilizes cookies for various purposes including advertising and analytics.","t.co",[406],{"_155":407,"_376":404,"_377":20,"_114":408},"muc_ads","This domain is owned by Twitter. 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YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","demdex.net",[449],{"_155":450,"_376":447,"_377":20,"_114":451},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[454],{"_155":455,"_376":452,"_377":20,"_114":456},"receive-cookie-deprecation","This domain is owned by im-apps.net which provides information and resources about various instant messaging (IM) applications to help users make informed decisions based on their communication needs.","influ2.com",[459],{"_155":460,"_376":457,"_377":20,"_114":413},"R","6sc.co",[463],{"_155":464,"_376":461,"_377":20,"_114":465},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","AMP_*","build.nvidia.com","Supports Amplitude product analytics.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. 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