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

Field	Response
Participation considerations from adversely impacted groups (protected classes) in model design and testing:	Not Applicable
Measures taken to mitigate against unwanted bias:	No measures taken to mitigate against unwanted bias.

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VehicleMakeNet Model Card

Model Overview

VehicleMakeNet classifies car makes in images:

Acura
Audi
BMW
Chevrolet
Chrysler
Dodge
Ford
GMC
Honda
Hyundai
Infiniti
Jeep
Kia
Lexus
Mazda
Mercedes
Nissan
Subaru
Toyota
Volkswagen

This model is ready for commercial use.

References

Citations

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 NGC model registry:\n
- PeopleNet
- TrafficCamNet
- DashCamNet
- FaceDetectIR
- VehicleMakeNet
- VehicleTypeNet
\n

Model Architecture

Architecture Type: Convolution Neural Network (CNN)
\nNetwork Architecture: ResNet18

This is a classification model with a Resnet18 backbone.

Input:

Input Type(s): Images
\nInput Format(s): Red, Green, Blue (RGB)
\nInput Parameters: 4D
\nOther Properties Related to Input: RGB Resolution: 224 X 224 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 (224), W = Width of the images (224) Input scale: None Mean subtraction: [103.939, 116.779, 123.68] No minimum bit depth, alpha, or gamma.

Output:

Output Type(s): Label(s)
\nOutput Format: Label: Text String(s)
\nOther Properties Related to Output: Category Label(s): Acura, Audi, BMW, Chevrolet, Chrysler, Dodge, Ford, GMC, Honda, Hyundai, Infiniti, Jeep, Kia, Lexus, Mazda, Mercedes, Nissan, Subaru, Toyota, and Volkswagen.

Software Integration:

Runtime Engine(s):

TAO 5.1
DeepStream 6.1 or later

Supported Hardware Architecture Compatibility:

Ampere
Jetson
Hopper
Lovelace
Pascal
Turing

Preferred Operating System(s):

Linux
Linux 4 Tegra

Model versions

unpruned_v1.0 - ResNet18 based pre-trained model.
pruned_v1.0 - ResNet18 deployment models. Contains common INT8 calibration cache for GPU and DLA.

Training

Data Collection Method by dataset\n
- Automatic/Sensors
\n
Labeling Method by dataset:\n
- Human
\n

VehicleMakeNet was trained on a proprietary dataset with more than 60K car images. The training dataset contains car crops from a mix of camera heights, camera angles, field-of view (FOV) and occlusions.

Training Data Properties

60,000 images from an internal, private dataset of cars with a mix of camera heights, camera angles, field-of view (FOV) and occlusions represented.

Performance

Evaluation Data Properties

Data Collection Method by dataset\n
- Automatic/Sensors
\n
Labeling Method by dataset:\n
- Human
\n

2000 images from an internal, private dataset of cars with a mix of camera heights, camera angles, field-of view (FOV) and occlusions represented.

Methodology and KPI

The KPI for the evaluation data are reported in the table below. Model is evaluated based on Top-1 accuracy.

\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	VehicleMakeNet
Content	Top1 Accuracy (in %)
Evaluation Set	91

Real-time Inference Performance

The inference is run on the provided pruned model at INT8 precision. On 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.

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.

There are two flavors of these models:

unpruned
pruned

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

The pruned model is intended for efficient deployment on the edge using DeepStream SDK or TensorRT. This model accepts 224x224x3 dimension input tensors and outputs a 1x20 class confidence tensor. DeepStream provides a toolkit to create efficient video analytic pipelines to capture, decode, and pre-process the data before running inference. DeepStream provides a toolkit to attach the classification model to any primary detector. The primary detector will detect the object and crop the object before sending to secondary classifiers.

The unpruned and pruned models are encrypted and will only operate with the following key:

Model load key: tlt_encode

Please make sure to use this as the key for all TAO commands that require a model load key.

Instructions to use unpruned model with TAO

In order, to use this model as a pretrained weights for transfer learning, please use the below mentioned snippet as template for the model_config and traning_config component of the experiment spec file to train a Classification model. For more information on the experiment spec file, please refer to the TAO Toolkit User Guide.

  model_config {\n    arch: \"resnet\"\n    n_layers: 18\n    use_bias: True\n    use_batch_norm: True\n    all_projections: True\n    use_pooling: False\n    freeze_bn: False\n    freeze_blocks: 0\n    freeze_blocks: 1\n    input_image_size: \"3,224,224\"\n  }\n\n  training_config{\n    train_dataset_path: \"/path/to/your/train/data\"\n    val_dataset_path: \"/path/to/your/val/data\"\n    pretrained_model_path: \"/path/to/your/pretrained/model\"\n    optimizer {\n      sgd {\n        lr: 0.01\n        decay: 0.0\n        momentum: 0.9\n        nesterov: False\n      }\n    }\n    batch_size_per_gpu: 256\n    n_epochs: 80\n    n_workers: 16\n    reg_config {\n      type: \"L2\"\n      scope: \"Conv2D,Dense\"\n      weight_decay: 0.00005\n    }\n  }\n

Instructions to deploy these models 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 below:

Run the default deepstream-app included in the DeepStream docker, by simply executing the commands below.

  ## Download Model:\n  mkdir -p $HOME/vehiclemakenet && \\\n  wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/vehiclemakenet/versions/pruned_v1.0/files/resnet18_vehiclemakenet_pruned.etlt \\\n  -O $HOME/vehiclemakenet/resnet18_vehiclemakenet_pruned.etlt && \\\n  wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/vehiclemakenet/versions/pruned_v1.0/files/vehiclemakenet_int8.txt \\\n  -O $HOME/vehiclemakenet/vehiclemakenet_int8.txt\n  ## Run Application\n\n  xhost +\n  sudo docker run --gpus all -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v $HOME:/opt/nvidia/deepstream/deepstream-5.1/samples/models/tlt_pretrained_models \\\n  -w /opt/nvidia/deepstream/deepstream-5.1/samples/configs/tlt_pretrained_models nvcr.io/nvidia/deepstream:5.1-21.02-samples \\\n  deepstream-app -c deepstream_app_source1_dashcamnet_vehiclemakenet_vehicletypenet.txt\n

\n
Install deepstream on your local host and run the deepstream-app.
\n

To deploy this model with DeepStream, please follow the instructions below:\nDownload 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-5.1/samples/configs/tlt_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 will need multiple config files and 1 label file. These files are provided in the tlt_pretrained_models directory.

deepstream_app_source1_dashcamnet_vehiclemakenet_vehicletypenet.txt - Main config file for DeepStream app\nconfig_infer_primary_dashcamnet.txt - File to configure primary detection (DashCamNet)\nconfig_infer_secondary_vehiclemakenet.txt - File to configure Vehicle make classifier\nlabels_dashcamnet.txt - Label file with 3 for object detection\nlabels_vehiclemakenet.txt - Label file with 20 classes of Vehicle make\n

Note: The deepstream_app_source1_dashcamnet_vehiclemakenet_vehicletypenet.txt configures 3 models: DashCamNet as primary detector, and VehicleMakeNet and VehicleTypeNet as secondary classifiers. The classification models are typically used after initial object detection.

Key Parameters in config_infer_secondary_vehiclemakenet.txt

tlt-model-key\ntlt-encoded-model\nlabelfile-path\nint8-calib-file\ninput-dims\nnum-detected-classes\n

Run deepstream-app:

deepstream-app -c deepstream_app_source1_dashcamnet_vehiclemakenet_vehicletypenet.txt\n

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

Technical blogs

Access the latest in Vision AI development workflows with NVIDIA TAO Toolkit 5.0
Improve accuracy and robustness of vision ai models with vision transformers and NVIDIA TAO
Train like a ‘pro’ without being an AI expert using TAO AutoML
Create Custom AI models using NVIDIA TAO Toolkit with Azure Machine Learning
Developing and Deploying AI-powered Robots with NVIDIA Isaac Sim and NVIDIA TAO
Learn endless ways to adapt and supercharge your AI workflows with TAO - Whitepaper
Customize Action Recognition with TAO and deploy with DeepStream
Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - Part 1 | Part 2
Learn how to train real-time License plate detection and recognition app with TAO and DeepStream.
Model accuracy is extremely important, learn how you can achieve state of the art accuracy for classification and object detection models using TAO

Ethical Considerations

Training and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies.

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.

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\n**Network Architecture:** ResNet18\n\nThis is a classification model with a Resnet18 backbone.\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:** RGB Resolution: 224 X 224 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 (224), W = Width of the images (224) Input scale: None Mean subtraction: [103.939, 116.779, 123.68] No minimum bit depth, alpha, or gamma.\n\n## Output:\n\n**Output Type(s):** Label(s)
\n**Output Format:** Label: Text String(s)
\n**Other Properties Related to Output:** Category Label(s): Acura, Audi, BMW, Chevrolet, Chrysler, Dodge, Ford, GMC, Honda, Hyundai, Infiniti, Jeep, Kia, Lexus, Mazda, Mercedes, Nissan, Subaru, Toyota, and Volkswagen.\n\n\n## Software Integration:\n**Runtime Engine(s):**\n* TAO 5.1
\n* DeepStream 6.1 or later
\n\n**Supported Hardware Architecture Compatibility:**
\n* Ampere
\n* Jetson
\n* Hopper
\n* Lovelace
\n* Pascal
\n* Turing
\n\n**Preferred Operating System(s):**
\n* Linux
\n* Linux 4 Tegra
\n\n### Model versions
\n* **unpruned_v1.0** - ResNet18 based pre-trained model.
\n* **pruned_v1.0** - ResNet18 deployment models. Contains common INT8 calibration cache for GPU and DLA.
\n\n### Training \n\n- **Data Collection Method by dataset**\n - Automatic/Sensors\n- **Labeling Method by dataset:**\n - Human\n \nVehicleMakeNet was trained on a proprietary dataset with more than 60K car images. The training dataset contains car crops from a mix of camera heights, camera angles, field-of view (FOV) and occlusions.\n\n#### Training Data Properties \n\n60,000 images from an internal, private dataset of cars with a mix of camera heights, camera angles, field-of view (FOV) and occlusions represented.\n\n### Performance\n\n#### Evaluation Data Properties \n\n- **Data Collection Method by dataset**\n - Automatic/Sensors\n- **Labeling Method by dataset:**\n - Human\n\n2000 images from an internal, private dataset of cars with a mix of camera heights, camera angles, field-of view (FOV) and occlusions represented.\n\n##### Methodology and KPI\n\nThe KPI for the evaluation data are reported in the table below. Model is evaluated based on Top-1 accuracy.\n\n| Model | VehicleMakeNet |\n| ---- | ----|\n|Content| Top1 Accuracy (in %) |\n|Evaluation Set| 91 |\n\n#### Real-time Inference Performance \n\nThe inference is run on the provided pruned model at INT8 precision. On 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

\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](https://developer.nvidia.com/tao-toolkit), [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) or [TensorRT](https://developer.nvidia.com/tensorrt).\n\nThere are two flavors of these models:\n\n- unpruned\n- pruned\n\nThe `unpruned` model is intended for training using TAO Toolkit and the user's own dataset. This can provide high fidelity models that are adapted to the use case. The Jupyter notebook available as a part of [TAO container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit-tf) can be used to re-train.\n\nThe `pruned` model is intended for efficient deployment on the edge using DeepStream SDK or TensorRT. This model accepts `224x224x3` dimension input tensors and outputs a `1x20` class confidence tensor. DeepStream provides a toolkit to create efficient video analytic pipelines to capture, decode, and pre-process the data before running inference. DeepStream provides a toolkit to attach the classification model to any primary detector. The primary detector will detect the object and crop the object before sending to secondary classifiers.\n\nThe `unpruned` and `pruned` models are encrypted and will only operate with the following key:\n\n- Model load key: `tlt_encode`\n\nPlease make sure to use this as the key for all TAO commands that require a model load key.\n\n#### Instructions to use unpruned model with TAO\n\nIn order, to use this model as a pretrained weights for transfer learning, please use the below mentioned snippet as template for the `model_config` and `traning_config` component of the experiment spec file to train a Classification model. For more information on the experiment spec file, please refer to the [TAO Toolkit User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\n```py\n model_config {\n arch: \"resnet\"\n n_layers: 18\n use_bias: True\n use_batch_norm: True\n all_projections: True\n use_pooling: False\n freeze_bn: False\n freeze_blocks: 0\n freeze_blocks: 1\n input_image_size: \"3,224,224\"\n }\n\n training_config{\n train_dataset_path: \"/path/to/your/train/data\"\n val_dataset_path: \"/path/to/your/val/data\"\n pretrained_model_path: \"/path/to/your/pretrained/model\"\n optimizer {\n sgd {\n lr: 0.01\n decay: 0.0\n momentum: 0.9\n nesterov: False\n }\n }\n batch_size_per_gpu: 256\n n_epochs: 80\n n_workers: 16\n reg_config {\n type: \"L2\"\n scope: \"Conv2D,Dense\"\n weight_decay: 0.00005\n }\n }\n```\n\n#### Instructions to deploy these models 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 below:\n\n1. Run the default `deepstream-app` included in the DeepStream docker, by simply executing the commands below.\n\n ```sh\n ## Download Model:\n mkdir -p $HOME/vehiclemakenet && \\\n wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/vehiclemakenet/versions/pruned_v1.0/files/resnet18_vehiclemakenet_pruned.etlt \\\n -O $HOME/vehiclemakenet/resnet18_vehiclemakenet_pruned.etlt && \\\n wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/vehiclemakenet/versions/pruned_v1.0/files/vehiclemakenet_int8.txt \\\n -O $HOME/vehiclemakenet/vehiclemakenet_int8.txt\n ## Run Application\n\n xhost +\n sudo docker run --gpus all -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v $HOME:/opt/nvidia/deepstream/deepstream-5.1/samples/models/tlt_pretrained_models \\\n -w /opt/nvidia/deepstream/deepstream-5.1/samples/configs/tlt_pretrained_models nvcr.io/nvidia/deepstream:5.1-21.02-samples \\\n deepstream-app -c deepstream_app_source1_dashcamnet_vehiclemakenet_vehicletypenet.txt\n ```\n\n2. Install deepstream on your local host and run the deepstream-app.\n\n \n To deploy this model with [DeepStream](https://developer.nvidia.com/deepstream-sdk), please follow the instructions below:\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 ```sh\n /opt/nvidia/deepstream/deepstream-5.1/samples/configs/tlt_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 will need multiple config files and 1 label file. These files are provided in the `tlt_pretrained_models` directory.\n\n```sh\ndeepstream_app_source1_dashcamnet_vehiclemakenet_vehicletypenet.txt - Main config file for DeepStream app\nconfig_infer_primary_dashcamnet.txt - File to configure primary detection (DashCamNet)\nconfig_infer_secondary_vehiclemakenet.txt - File to configure Vehicle make classifier\nlabels_dashcamnet.txt - Label file with 3 for object detection\nlabels_vehiclemakenet.txt - Label file with 20 classes of Vehicle make\n```\n\n***Note:*** The `deepstream_app_source1_dashcamnet_vehiclemakenet_vehicletypenet.txt` configures 3 models: [DashCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:dashcamnet) as primary detector, and VehicleMakeNet and [VehicleTypeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehicletypenet) as secondary classifiers. The classification models are typically used after initial object detection.\n\nKey Parameters in `config_infer_secondary_vehiclemakenet.txt`\n\n```sh\ntlt-model-key\ntlt-encoded-model\nlabelfile-path\nint8-calib-file\ninput-dims\nnum-detected-classes\n```\n\nRun `deepstream-app`:\n\n```sh\ndeepstream-app -c deepstream_app_source1_dashcamnet_vehiclemakenet_vehicletypenet.txt\n```\n\nDocumentation to deploy with DeepStream is provided in \"Deploying to DeepStream\" chapter of [TAO User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\n#### Technical blogs \n\n- [Access the latest in Vision AI development workflows with NVIDIA TAO Toolkit 5.0](https://developer.nvidia.com/blog/access-the-latest-in-vision-ai-model-development-workflows-with-nvidia-tao-toolkit-5-0/)\n- [Improve accuracy and robustness of vision ai models with vision transformers and NVIDIA TAO](https://developer.nvidia.com/blog/improve-accuracy-and-robustness-of-vision-ai-apps-with-vision-transformers-and-nvidia-tao/)\n- [Train like a ‘pro’ without being an AI expert using TAO AutoML](https://developer.nvidia.com/blog/training-like-an-ai-pro-using-tao-automl/)\n- [Create Custom AI models using NVIDIA TAO Toolkit with Azure Machine Learning ](https://developer.nvidia.com/blog/creating-custom-ai-models-using-nvidia-tao-toolkit-with-azure-machine-learning/)\n- [Developing and Deploying AI-powered Robots with NVIDIA Isaac Sim and NVIDIA TAO](https://developer.nvidia.com/blog/developing-and-deploying-ai-powered-robots-with-nvidia-isaac-sim-and-nvidia-tao/)\n- Learn endless ways to adapt and supercharge your AI workflows with TAO - [Whitepaper](https://developer.nvidia.com/tao-toolkit-usecases-whitepaper/1-introduction)\n- [Customize Action Recognition with TAO and deploy with DeepStream](https://developer.nvidia.com/blog/developing-and-deploying-your-custom-action-recognition-application-without-any-ai-expertise-using-tao-and-deepstream/)\n- Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - [Part 1](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-1) | [Part 2](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-2)\n- Learn how to train [real-time License plate detection and recognition app](https://developer.nvidia.com/blog/creating-a-real-time-license-plate-detection-and-recognition-app) with TAO and DeepStream.\n- Model accuracy is extremely important, learn how you can achieve [state of the art accuracy for classification and object detection models](https://developer.nvidia.com/blog/preparing-state-of-the-art-models-for-classification-and-object-detection-with-tao-toolkit/) using TAO\n\n#### Suggested reading \n\n- More information on about TAO Toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/tao-toolkit)\n- [TAO documentation](https://docs.nvidia.com/tao/tao-toolkit/index.html)\n- Read the [TAO getting Started](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html) guide and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html).\n- If you have any questions or feedback, please refer to the discussions on [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17)\n- Deploy your models for video analytics application using DeepStream. Learn more about [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk)\n- Deploy your models in [Riva](https://developer.nvidia.com/riva) for ConvAI use case.\n\n### Ethical Considerations \n\nTraining and evaluation dataset mostly consists of North American content. An ideal training and evaluation dataset would additionally include content from other geographies.\n\nNVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.","displayName","VehicleMakeNet",["D",1732745320602],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/vehiclemakenet","rawImageUrl","https://raw.githubusercontent.com/kbojo/images/master/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",7408442,"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: | Not Applicable |\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 vehicle make |\n| Model Type: | Image Classification |\n| Intended Users: | Developers working in smart city and transportation applications |\n| Output: | Vehicle Make Labels |\n| Describe how the model works: | Classifies the following vehicle makes: Acura, Audi, BMW, Chevrolet, Chrysler, Dodge, Ford, GMC, Honda, Hyundai, Infiniti, Jeep, Kia, Lexus, Mazda, Mercedes, Nissan, Subaru, Toyota, and Volkswagen |\n| Technical Limitations: | Model accuracy may be reduced for occluded or top views of vehicles |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Accuracy |\n| Potential Known Risks: | This model may misclassify vehicle make. |\n| Licensing: | [https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/) |","privacy","| Field | Response |\n| -- | -- |\n| Generatable or reverse engineerable personally-identifiable information (PII)? | None |\n| Protected classes used to create this model? | None |\n| Was consent obtained for any PII used? | Yes |\n| How often is dataset reviewed? | \tBefore Release |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | Yes |\n| If PII collected for the development of the model, was it collected directly by NVIDIA? | Yes, PII was collected by NVIDIA. |\n| If PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects?\t| Yes |\n| If PII collected for the development of this AI model, was it minimized to only what was required? | Yes |\n| Is there provenance for all datasets used in training? Yes| |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | Yes |\n| Applicable NVIDIA Privacy Policy\t| [https://www.nvidia.com/en-us/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): | Image Classification |\n| Describe the life-critical application (if present). | Not Applicable |\n| Use Case Restrictions: | Should not be used in \"Critical Applications;\" [reference](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/). |\n| Describe access restrictions (if any): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |","featureLabels",[248],"displayLabels",[198,199,200,201,202,203,204,205,206,207,208,209],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_161":245,"_211":246,"_212":247,"_214":215},{"_161":243,"_211":243,"_212":244,"_214":215},{"_161":240,"_211":241,"_212":242,"_214":215},{"_161":236,"_211":237,"_212":238,"_214":215},{"_161":234,"_211":234,"_212":235,"_214":215},{"_161":230,"_211":231,"_212":232,"_214":215},{"_161":228,"_211":228,"_212":229,"_214":215},{"_161":224,"_211":225,"_212":226,"_214":215},{"_161":222,"_211":222,"_212":223,"_214":215},{"_161":219,"_211":219,"_212":220,"_214":215},{"_161":217,"_211":217,"_212":218,"_214":215},{"_161":210,"_211":210,"_212":213,"_214":215},"Vehicle Classification","value","filter",{"_216":215,"_211":210},"category","label","field","Traffic",{"_216":215,"_211":217},"TAO Toolkit",{"_216":221,"_211":219},"framework","Smart Infrastructure",{"_216":215,"_211":222},"Smart Cities / Spaces","indus_smart_cities_spaces",{"_216":227,"_211":225},"industry","Public Safety",{"_216":215,"_211":228},"NVIDIA AI","soln_nvidia_ai",{"_216":233,"_211":231},"technology","NSPECT-P5IV-UMDM",{"_216":215,"_211":234},"Metropolis","pltfm_metropolis",{"_216":239,"_211":237},"platform","DeepStream","pltfm_deepstream",{"_216":239,"_211":241},"CV",{"_216":215,"_211":243},"Computer Vision","soln_computer_vision",{"_216":233,"_211":246},{"_161":249,"_211":249,"_212":250,"_214":251},"NVIDIA AI Enterprise Supported",{"_216":215,"_211":249},"feature","appConfig",{"_1429":1430,"_286":1431},"ngcApps",[1382],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1364":1365,"_1366":1367,"_1368":1369,"_1370":1371,"_1372":264,"_76":291,"_1373":1374,"_1362":44,"_1375":87,"_1376":87,"_1377":1378,"_1379":87,"_1380":1381},"amplitude",{"_1360":1361,"_1362":44,"_963":-7,"_1363":20},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1354":1355,"_1356":1357,"_1358":1359},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","https://api.ngc.nvidia.com","ngcHelmOrigin","https://helm.ngc.nvidia.com","origin","https://catalog.ngc.nvidia.com","user","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1238":107,"_1239":1240,"_1241":1242,"_1243":1244},"announcements",[],"consentState",{"_329":1228,"_1229":87,"_1230":20,"_1231":20,"_1232":1233},"consentConfig",{"_311":312,"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330},"showContactAdmin",1783317764630,[],[],[305,306,307,308,309,310],{"_33":48,"_35":57,"_37":65,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":55,"_37":65,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":53,"_37":65,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":49,"_37":65,"_39":-5,"_41":-5,"_43":44},{"_33":45,"_35":46,"_37":65,"_39":-5,"_41":-5,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":47,"_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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It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","tags.srv.stackadapt.com",[418,419,420,421],{"_139":426,"_375":416,"_376":20,"_106":423},{"_139":425,"_375":416,"_376":20,"_106":423},{"_139":424,"_375":416,"_376":20,"_106":423},{"_139":422,"_375":416,"_376":20,"_106":423},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[429],{"_139":430,"_375":427,"_376":20,"_106":431},"bcookie","This domain is owned by LinkedIn, the business networking platform. 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