License Plate Recognition (LPRNet) Model Card

Description:

LPRNet recognizes characters in license plates from cropped images. This model is ready for commercial use.

References:

Citations

Graves, Alex, et al. \"Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks.\" In: Proceedings of the 23rd international conference on Machine learning (2006)
He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: CVPR (2015)

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: ResNet

Input:

Input Type(s): Image
\nInput Format(s): Red, Green, Blue (RGB)
\nInput Parameters: 3D
\nOther Properties Related to Input: RGB Fixed Resolution: 3 X 48 X 96 (C H W) Channel Ordering of the Input: CHW, where C = number of channels (3), H = Height of images (48), W = Width of the images (96); No minimum bit depth, alpha, or gamma.

Output:

Output Type(s): Label(s)
\nOutput Format: Label: Text String
\nOther Properties Related to Output: Category Label(s): license plate alpha-numeric characters

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_v1.0 - Pre-trained models for US and China license plates.
deployable_v1.0 - Models for US and China license plates deployable to DeepStream.

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 310,000 US license plates images taken at various angles and illumination collected from dash and side cameras of a vehicle.
Approximately 100,000 images from the Chinese City Parking Dataset (CCPD) of a provincial capital of China.

Characters distribution:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\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\">

character	number
0	100688
1	117499
2	98599
3	111220
4	127387
5	148325
6	175541
7	231298
8	105170
9	111234
A	36350
B	33677
C	40292
D	39447
E	36787
F	34734
G	40474
H	38751
I	12645
J	34155
K	37397
L	40900
M	36544
N	40431
P	38198
Q	11086
R	40899
S	38820
T	41155
U	45471
V	35998
W	38096
X	37468
Y	34454
Z	31963

\n
Illumination: sunny, cloudy, rainy, bright, dim.
\n
\n
Locations of dataset collection: US roads and parking lots, mainly in California.
\n
\n
Camera mounting location: mainly the dash camera and the side camera in cars.
\n
\n
Camera angles:\nAssume camera sensor is in the camera coordinate center. The X-axis is horizontal and points to the right, the Y-axis is vertical and points up and the Z-axis points towards the outside. In this coordinate system, the license plates in following position are choosen:
\n
- Roll: within -30 degree to +30 degree
- Pitch: within -30 degree to +30 degree
- Yaw: within -15 degree to +15 degree
- Distance to license plate: From the distances that the license plates in images are larger than 16x16 pixels
\n
\n
License plates images shapes:
\n\n\n\n\n\n\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\">
min max avg
height 17 1924 54
width 35 3896 109
aspect-ratio (width/height) 0.9 3.6 2.0
\n

	min	max	avg
height	17	1924	54
width	35	3896	109
aspect-ratio (width/height)	0.9	3.6	2.0

Some sample images (before cropping the license plates) can be found in output annotated images section of LPD's model card.

Data Format

The data format must be in the following format.

/Dataset_01\n    /images\n        0000.jpg\n        0001.jpg\n        0002.jpg\n        ...\n        ...\n        ...\n        N.jpg\n    /labels \n        0000.txt\n        0001.txt\n        0002.txt\n        ...\n        ...\n        ...\n        N.txt\n/characters_list.txt\n

Each cropped license plate image has a corresponding label text file which contains one line of characters in the specific license plate. There is a characters_list.txt which has all the characters found in license plate dataset. Each character takes one line.

Evaluation Dataset:

Data Collection Method by dataset:

Automatic/Sensors

Labeling Method by dataset:

Human

Properties:
\nModel evaluated on approximately 100,000 images from Chinese City Parking Dataset (CCPD) of a provincial capital of China.

Methodology and KPI

The key performance indicator is the accuracy of license plate recognition. The accurate recognition means all the characters in a license plate are recognized correctly.The KPI for the evaluation data are reported below.

\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
us_lprnet_baseline18_unpruned	NVIDIA LPR eval dataset	97.49%
ch_lprnet_baseline18_unpruned	CCPD_base_val	99.67%

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 uses FP16 precision. The inference performance runs with trtexec on Jetson Nano, Xavier NX, AGX Xavier and NVIDIA T4 GPU. The Jetson devices run at Max-N configuration for maximum system performance. The data is the inference only performance. The end-to-end performance with streaming video data might slightly vary depending on use cases of applications.

\n\n\n\n\n\n\n\n\n\n\n\n\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
Jetson Nano	FP16	32	16
Jetson NX	FP16	32	600
Jetson Xavier	FP16	64	1021
T4	FP16	128	3821

How to use this model

This model needs to be used with NVIDIA Hardware and Software. For Hardware, the model can run on any NVIDIA GPU including NVIDIA Jetson devices. This model can only be used with Transfer Learning Toolkit (TLT), DeepStream SDK or TensorRT.

Primary use case intended for this model is to recognize the license plate from the cropped RGB license plate image.

There are two models provided:

us_lprnet_baseline18
ch_lprnet_baseline18

They are intended for training and fine-tune using Transfer Learning Toolkit and the users' dataset of license plates in United States of America or China. High fidelity models can be trained to the new use cases. The Jupyter notebook available as a part of TLT container can be used to re-train.

These models are also intended for easy deployment to the edge using DeepStream SDK or TensorRT. They accept 3x48x96 dimension input tensors and output the predicted sequence characters id. DeepStream provides facility to create efficient video analytic pipelines to capture, decode and pre-process the data before running inference.

The 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 TLT commands that require a model load key.

Instructions to deploy the model with DeepStream

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

To deploy this model with DeepStream 5.1, please follow the instructions in this repository.

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Learn endless ways to adapt and supercharge your AI workflows with TAO - Whitepaper.
Customize Action Recognition with TAO and deploy with DeepStream
Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - Part 1 | Part 2.
Learn how to train real-time License plate detection and recognition app with TAO and DeepStream.
Model accuracy is extremely important; learn how you can achieve state of the art accuracy for classification and object detection models using TAO.

Ethical Considerations:

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

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This model is ready for commercial use. \n\n## References:\n### Citations \n\n- Graves, Alex, et al. \"Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks.\" In: Proceedings of the 23rd international conference on Machine learning (2006)\n- He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: CVPR (2015)\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## Model Architecture: \n**Architecture Type:** Convolution Neural Network (CNN)
\n**Network Architecture:** ResNet
\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: 3 X 48 X 96 (C H W) Channel Ordering of the Input: CHW, where C = number of channels (3), H = Height of images (48), W = Width of the images (96); No minimum bit depth, alpha, or gamma.
\n\n## Output:\n**Output Type(s):** Label(s)
\n**Output Format:** Label: Text String
\n**Other Properties Related to Output:** Category Label(s): license plate alpha-numeric characters
\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_v1.0** - Pre-trained models for US and China license plates.\n- **deployable_v1.0** - Models for US and China license plates 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:**
\nLPRNet model has been trained on two datasets:\n* Internal, proprietary dataset of 310,000 US license plates images taken at various angles and illumination collected from dash and side cameras of a vehicle.\n* Approximately 100,000 images from the [Chinese City Parking Dataset (CCPD)](https://github.com/detectRecog/CCPD) of a provincial capital of China.\n\n- Characters distribution:\n\n |character|number|\n |:---:|:---:|\n |0|100688|\n |1|117499|\n |2|98599|\n |3|111220|\n |4|127387|\n |5|148325|\n |6|175541|\n |7|231298|\n |8|105170|\n |9|111234|\n |A|36350|\n |B|33677|\n |C|40292|\n |D|39447|\n |E|36787|\n |F|34734|\n |G|40474|\n |H|38751|\n |I|12645|\n |J|34155|\n |K|37397|\n |L|40900|\n |M|36544|\n |N|40431|\n |P|38198|\n |Q|11086|\n |R|40899|\n |S|38820|\n |T|41155|\n |U|45471|\n |V|35998|\n |W|38096|\n |X|37468|\n |Y|34454|\n |Z|31963|\n\n- Illumination: sunny, cloudy, rainy, bright, dim.\n- Locations of dataset collection: US roads and parking lots, mainly in California.\n- Camera mounting location: mainly the dash camera and the side camera in cars.\n- Camera angles:\n Assume camera sensor is in the camera coordinate center. The X-axis is horizontal and points to the right, the Y-axis is vertical and points up and the Z-axis points towards the outside. In this coordinate system, the license plates in following position are choosen:\n - Roll: within -30 degree to +30 degree\n - Pitch: within -30 degree to +30 degree\n - Yaw: within -15 degree to +15 degree\n - Distance to license plate: From the distances that the license plates in images are larger than 16x16 pixels\n- License plates images shapes:\n\n ||min|max|avg|\n |:---:|:---:|:---:|:---:|\n |height|17|1924|54|\n |width|35|3896|109|\n |aspect-ratio (width/height)|0.9|3.6|2.0|\nSome sample images (before cropping the license plates) can be found in `output annotated images` section of LPD's [model card](https://ngc.nvidia.com/catalog/models/nvidia:tao:lpdnet).\n\n#### Data Format\n\nThe data format must be in the following format.\n```py\n/Dataset_01\n /images\n 0000.jpg\n 0001.jpg\n 0002.jpg\n ...\n ...\n ...\n N.jpg\n /labels \n 0000.txt\n 0001.txt\n 0002.txt\n ...\n ...\n ...\n N.txt\n/characters_list.txt\n```\nEach cropped license plate image has a corresponding label text file which contains one line of characters in the specific license plate. There is a `characters_list.txt` which has all the characters found in license plate dataset. Each character takes one line. \n\n## Evaluation Dataset:\n**Data Collection Method by dataset:**
\n* Automatic/Sensors
\n\n**Labeling Method by dataset:**
\n* Human
\n\n**Properties:**
\nModel evaluated on approximately 100,000 images from [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 recognition. The accurate recognition means all the characters in a license plate are recognized correctly.The KPI for the evaluation data are reported below. \n\n|model|dataset|accuracy|\n|---|---|---|\n|us_lprnet_baseline18_unpruned|NVIDIA LPR eval dataset|97.49%|\n|ch_lprnet_baseline18_unpruned|CCPD_base_val|99.67%|\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 uses FP16 precision. The inference performance runs with [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/master/samples/trtexec) on Jetson Nano, Xavier NX, AGX Xavier and NVIDIA T4 GPU. The Jetson devices run at Max-N configuration for maximum system performance. The data is the inference only performance. The end-to-end performance with streaming video data might slightly vary depending on use cases of applications.\n\n|Device|precision|batch_size|FPS|\n|---|---|---|---|\n|Jetson Nano|FP16|32|16|\n|Jetson NX|FP16|32|600|\n|Jetson Xavier|FP16|64|1021|\n|T4|FP16|128|3821|\n\n## How to use this model \n\nThis model needs to be used with NVIDIA Hardware and Software. For Hardware, the model can run on any NVIDIA GPU including NVIDIA Jetson devices. This model can only be used with [Transfer Learning Toolkit](https://developer.nvidia.com/tao-toolkit) (TLT), [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) or [TensorRT](https://developer.nvidia.com/tensorrt).\n\nPrimary use case intended for this model is to recognize the license plate from the cropped RGB license plate image. \n\nThere are two models provided:\n- us_lprnet_baseline18\n- ch_lprnet_baseline18\n\nThey are intended for training and fine-tune using Transfer Learning Toolkit and the users' dataset of license plates in United States of America or China. High fidelity models can be trained to the new use cases. The Jupyter notebook available as a part of [TLT container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit-tf) can be used to re-train.\n\nThese models are also intended for easy deployment to the edge using DeepStream SDK or TensorRT. They accept `3x48x96` dimension input tensors and output the predicted sequence characters id. DeepStream provides facility to create efficient video analytic pipelines to capture, decode and pre-process the data before running inference.\n\nThe 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 TLT commands that require a model load key.\n\n## Instructions to deploy the model with DeepStream\n\nTo create the entire end-to-end video analytic application, deploy this model with [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk). DeepStream SDK is a streaming analytic toolkit to accelerate building AI-based video analytic applications. DeepStream supports direct integration of this model into the deepstream sample app.\n\nTo deploy this model with [DeepStream 5.1](https://developer.nvidia.com/deepstream-sdk), please follow the instructions in this [repository](https://github.com/NVIDIA-AI-IOT/deepstream_lpr_app).\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\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\n## Ethical Considerations:\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","License Plate Recognition",["D",1732745263593],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/lprnet","rawImageUrl","https://raw.githubusercontent.com/kbojo/images/master/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",115441532,"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): | Classifying characters in vehicle license plates for vehicle identification and traffic management |\n| Model Type: | Character Recognition |\n| Intended Users: | Developers working in smart city and transportation applications. |\n| Output: | Alphanumeric String (Characters in Sequence) |\n| Describe how the model works: | Recognizes characters in license plates from cropped images and outputs the character sequence. |\n| Technical Limitations: | Model may not recognize characters win truncated license plates or if the license plate is crooked (>30 degrees from horizontal). Model is trained with data from the United States and China and may not work well for characters seen outside of the English and Mandarin contexts. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Precision |\n| Potential Known Risks: | This model may fail to detect alphanumeric characters in 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| 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, 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 Character Recognition |\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 AI 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. 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Name	Size	Updated	Actions
ch_lp_characters.txt	200 B	March 1, 2025 UTC	curl wget Download
ch_lprnet_baseline18_trainable.tlt	110.59 MB	March 1, 2025 UTC	curl wget Download
us_lp_characters.txt	70 B	March 1, 2025 UTC	curl wget Download
us_lprnet_baseline18_trainable.tlt	110.47 MB	March 1, 2025 UTC	curl wget Download