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

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DashCamNet Model Card

Model Overview

DashCamNet detects bicycles, cars, persons, and road signs in an image. This model is ready for commercial use.

References

Citations

Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: CVPR. (2016)
Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks, In: CVPR. (2014)
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
- FaceDetectIR
- VehicleMakeNet
- VehicleTypeNet
\n

Model Architecture

Architecture Type: Convolution Neural Network (CNN)
\nNetwork Architecture: DetectNet_v2 + ResNet18 (Feature Extractor)

The model is based on NVIDIA DetectNet_v2 detector with ResNet18 as a 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.

Input:

Input Type(s): Images
\nInput Format(s): Red, Green, Blue (RGB)
\nInput Parameters: 4D
\nOther Properties Related to Input: RGB Resolution: 960 X 544 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 (544), W = Width of the images (960) Input scale: 1/255.0 Mean subtraction: None; No minimum bit depth, alpha, or gamma.

Input image

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): bicycles, persons, road signs; Bounding Box Coordinate(s); Confidence Score(s)

Output image

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.
trainable_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

This model was trained using the DetectNet_v2 entrypoint in TAO. The training algorithm optimizes the network to minimize the localization and confidence loss for the objects. The training is carried out in two phases. In the first phase, the network is trained with regularization to facilitate pruning. Following the first phase, we prune the network removing channels whose kernel norms are below the pruning threshold. In the second phase the pruned network is retrained. Regularization is not included during the second phase.

Training Data Properties

Internal, proprietary dataset with more than 2 million images of cars. The majority of the training dataset was collected and labeled in-house from images from a variety of dashcams and the remaining were taken from traffic cameras in a city in the US.

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

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

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

Training Data Ground-truth Labeling Guidelines

All objects that fall under one of the four classes (car, person two-wheeler, road_sign) in the image and are larger than the smallest bounding-box limit for the corresponding class (height >= 10px OR width >= 10px @1920x1080) are labeled with the appropriate class label.
Occlusion: For partially occluded objects that are visible approximately 60% or are marked as visible objects with bounding box around visible part of the object. These objects are marked as partially occluded. Objects under 60% visibility are not annotated.
Truncation: For an object that is at the edge of the frame with visibility of 60% or more visible are marked with the truncation flag for the object.
Each frame is not required to have an object.

Performance

Evaluation Data Properties

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

19,000 internal proprietary images identical in character to those from the training datasets referenced above.

Methodology and KPI

The true positives, false positives, false negatives are calculated using intersection-over-union (IOU) criterion greater than 0.5. The KPI for the evaluation data are reported in the table below. Model is evaluated based on precision, recall and accuracy.

The intended use of this model is to detect cars from a moving vehicle. With that in mind the model evaluation is conducted and key performance indicators (KPI) are calculated for car class only. The other classes - road signs, two-wheelers and persons are not factored in the model evaluation.

\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		DashcamNet
Content	Precision	Recall	Accuracy
Dashcam	83.65	88.45	80

Real-time Inference Performance

The inference is run on a pruned model at INT8 precision. On the Jetson Nano, FP16 precision is used. The inference is run on Jetson Nano, AGX Xavier and NVIDIA T4 GPU. The Jetson Nano and AGX Xavier are running at Max-N configuration for maximum GPU frequency. The inference is measured at batch size equal to 1 to get the lowest inference latency and batch size equal N for maximum real-time inference, where N is the maximum batch size that keeps inference time per batch at under 33ms or 30fps.

\n $\"alt$ \n

How to use this model

These models need to be used with NVIDIA Hardware and Software. For Hardware, the models can run on any NVIDIA GPU including NVIDIA Jetson devices. These models can only be used with Train Adapt Optimize (TAO) Toolkit, DeepStream SDK or TensorRT.

The primary use case intended for these models is detecting people in a color (RGB) image. The model can be used to detect people from photos and videos by using appropriate video or image decoding and pre-processing. As a secondary use case the model can also be used to detect bags and faces from images or videos. However, these additional classes are not the main intended use for these models.

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 960x544x3 dimension input tensors and outputs 60x34x16 bbox coordinate tensor and 60x34x4 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 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 this model are provided in DeepStream SDK.

The unpruned andpruned 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 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 User Guide.

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  all_projections: true\n}\n

Instructions to deploy these models with DeepStream

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  all_projections: true\n}\n

Instructions to deploy this model with DeepStream

To create the entire end-to-end video analytics application, deploy this model with DeepStream SDK. DeepStream SDK is a streaming analytics toolkit to accelerate deployment of AI-based video analytics applications. The pruned model included here can be integrated directly into deepstream by following the instructions mentioned\nbelow.

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

  ## Download Model:\n\n  mkdir -p $HOME/dashcamnet && \\\n  wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/dashcamnet/versions/pruned_v1.0/files/resnet18_dashcamnet_pruned.etlt \\\n  -O $HOME/dashcamnet/resnet18_dashcamnet_pruned.etlt && \\\n  wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/dashcamnet/versions/pruned_v1.0/files/dashcamnet_int8.txt \\\n  -O $HOME/dashcamnet/dashcamnet_int8.txt\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  mkdir -p $HOME/vehicletypenet && \\\n  wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/vehicletypenet/versions/pruned_v1.0/files/resnet18_vehicletypenet_pruned.etlt \\\n  -O $HOME/vehicletypenet/resnet18_vehicletypenet_pruned.etlt && \\\n  wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/vehicletypenet/versions/pruned_v1.0/files/vehicletypenet_int8.txt \\\n  -O $HOME/vehicletypenet/vehicletypenet_int8.txt\n\n  ## Run Application\n\n  xhost +\n  sudo docker run --gpus all -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$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
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:
\n
```
  /opt/nvidia/deepstream/deepstream-5.1/samples/configs/tlt_pretrained_models\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
You will need 2 config files and 1 label file. These files are provided in the tlt_pretrained_models directory.
\n
```
  deepstream_app_source1_dashcamnet_vehiclemakenet_vehicletypenet.txt - Main config file for DeepStream app\n  config_infer_primary_dashcamnet.txt - File to configure inference settings\n  labels_dashcamnet.txt - Label file with 3 classes\n
```
\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. To disable the secondary classifiers, set the enable flag under secondar_gie* to 0
\n
```
  [secondary-gie0]\n  enable=0\n  ...\n\n  [secondary-gie1]\n  enable=0\n
```
\n
Key Parameters in config_infer_primary_dashcamnet.txt
\n
```
  tlt-model-key\n  tlt-encoded-model\n  labelfile-path\n  int8-calib-file\n  input-dims\n  num-detected-classes\n
```
\n
Run deepstream-app:
\n
```
  deepstream-app -c deepstream_app_source1_dashcamnet_vehiclemakenet_vehicletypenet.txt\n
```
\n
Documentation to deploy with DeepStream is provided in \"Deploying to DeepStream\" chapter of TAO User Guide.
\n

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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:** DetectNet_v2 + ResNet18 (Feature Extractor)\n\nThe model is based on NVIDIA DetectNet_v2 detector with ResNet18 as a 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\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: 960 X 544 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 (544), W = Width of the images (960) Input scale: 1/255.0 Mean subtraction: None; No minimum bit depth, alpha, or gamma.\n\n#### Input image\n\n

\n\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): bicycles, persons, road signs; Bounding Box Coordinate(s); Confidence Score(s)\n\n#### Output image\n\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* **trainable_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 \nThis model was trained using the DetectNet_v2 entrypoint in TAO. The training algorithm optimizes the network to minimize the localization and confidence loss for the objects. The training is carried out in two phases. In the first phase, the network is trained with regularization to facilitate pruning. Following the first phase, we prune the network removing channels whose kernel norms are below the pruning threshold. In the second phase the pruned network is retrained. Regularization is not included during the second phase.\n\n#### Training Data Properties \n\nInternal, proprietary dataset with more than 2 million images of cars. The majority of the training dataset was collected and labeled in-house from images from a variety of dashcams and the remaining were taken from traffic cameras in a city in the US.\n\n| | | Object| Distribution | | |\n|--|--|--|--|--|--|\n|Environment|Images|Cars|Persons|Road Signs|Two-Wheelers|\nDashcam (5ft height)|128,000|1.7M|720,000|354,127|54,000|\nTraffic signal content|50,000|1.1M|53500|184000|11000|\nTotal|178,000|2.8M|773,500|538,127|65,000|\n\n\n##### Training Data Ground-truth Labeling Guidelines\n\nTraining Data Ground-truth Labeling Guidelines\n* All objects that fall under one of the four classes (car, person two-wheeler, road_sign) in the image and are larger than the smallest bounding-box limit for the corresponding class (height >= 10px OR width >= 10px @1920x1080) are labeled with the appropriate class label.\n* Occlusion: For partially occluded objects that are visible approximately 60% or are marked as visible objects with bounding box around visible part of the object. These objects are marked as partially occluded. Objects under 60% visibility are not annotated.\n* Truncation: For an object that is at the edge of the frame with visibility of 60% or more visible are marked with the truncation flag for the object.\n* Each frame is not required to have an object.\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\n19,000 internal proprietary images identical in character to those from the training datasets referenced above. \n\n##### Methodology and KPI\n\nThe true positives, false positives, false negatives are calculated using intersection-over-union (IOU) criterion greater than 0.5. The KPI for the evaluation data are reported in the table below. Model is evaluated based on precision, recall and accuracy.\n\nThe intended use of this model is to detect cars from a moving vehicle. With that in mind the model evaluation is conducted and key performance indicators (KPI) are calculated for car class only. The other classes - road signs, two-wheelers and persons are not factored in the model evaluation.\n\n|Model||DashcamNet||\n|-----|-----------|------|--------|\n|Content|Precision|Recall|Accuracy|\n|Dashcam|83.65|88.45|80|\n\n\n#### Real-time Inference Performance \n\nThe inference is run on a pruned model at INT8 precision. On the Jetson Nano, FP16 precision is used. The inference is run on Jetson Nano, AGX Xavier and NVIDIA T4 GPU. The Jetson Nano and AGX Xavier are running at Max-N configuration for maximum GPU frequency. The inference is measured at batch size equal to 1 to get the lowest inference latency and batch size equal N for maximum real-time inference, where N is the maximum batch size that keeps inference time per batch at under 33ms or 30fps.\n\n $\"alt$ \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\nThe primary use case intended for these models is detecting people in a color (RGB) image. The model can be used to detect people from photos and videos by using appropriate video or image decoding and pre-processing. As a secondary use case the model can also be used to detect bags and faces from images or videos. However, these additional classes are not the main intended use for these models.\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 960x544x3 dimension input tensors and outputs 60x34x16 bbox coordinate tensor and 60x34x4 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 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 this model are provided in DeepStream SDK.\n\nThe unpruned andpruned models are encrypted and will only operate with the following key:\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` 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 User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\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 all_projections: true\n}\n```\n\n#### Instructions to deploy these models with DeepStream\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` 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 User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\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 all_projections: true\n}\n```\n\n### Instructions to deploy this model with DeepStream \n\nTo create the entire end-to-end video analytics application, deploy this model with [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk). DeepStream SDK is a streaming analytics toolkit to accelerate deployment of AI-based video analytics applications. The pruned model included here can be integrated directly into deepstream by following the instructions mentioned\nbelow.\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\n mkdir -p $HOME/dashcamnet && \\\n wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/dashcamnet/versions/pruned_v1.0/files/resnet18_dashcamnet_pruned.etlt \\\n -O $HOME/dashcamnet/resnet18_dashcamnet_pruned.etlt && \\\n wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/dashcamnet/versions/pruned_v1.0/files/dashcamnet_int8.txt \\\n -O $HOME/dashcamnet/dashcamnet_int8.txt\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 mkdir -p $HOME/vehicletypenet && \\\n wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/vehicletypenet/versions/pruned_v1.0/files/resnet18_vehicletypenet_pruned.etlt \\\n -O $HOME/vehicletypenet/resnet18_vehicletypenet_pruned.etlt && \\\n wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/vehicletypenet/versions/pruned_v1.0/files/vehicletypenet_int8.txt \\\n -O $HOME/vehicletypenet/vehicletypenet_int8.txt\n\n ## Run Application\n\n xhost +\n sudo docker run --gpus all -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$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 [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-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\n You will need 2 config files and 1 label file. These files are provided in the `tlt_pretrained_models` directory.\n\n ```sh\n deepstream_app_source1_dashcamnet_vehiclemakenet_vehicletypenet.txt - Main config file for DeepStream app\n config_infer_primary_dashcamnet.txt - File to configure inference settings\n labels_dashcamnet.txt - Label file with 3 classes\n ```\n\n ***Note:*** The `deepstream_app_source1_dashcamnet_vehiclemakenet_vehicletypenet.txt` configures 3 models: DashCamNet as primary detector, and [VehicleMakeNet](https://ngc.nvidia.com/catalog/models/nvidia:vehiclemakenet) and [VehicleTypeNet](https://ngc.nvidia.com/catalog/models/nvidia:vehicletypenet) as secondary classifiers. The classification models are typically used after initial object detection. To disable the secondary classifiers, set the `enable` flag under `secondar_gie*` to 0\n\n ```sh\n [secondary-gie0]\n enable=0\n ...\n\n [secondary-gie1]\n enable=0\n ```\n\n Key Parameters in `config_infer_primary_dashcamnet.txt`\n\n ```sh\n tlt-model-key\n tlt-encoded-model\n labelfile-path\n int8-calib-file\n input-dims\n num-detected-classes\n ```\n\n Run `deepstream-app`:\n\n ```sh\n deepstream-app -c deepstream_app_source1_dashcamnet_vehiclemakenet_vehicletypenet.txt\n ```\n\n Documentation 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\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\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","DashCamNet",["D",1742230640472],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/dashcamnet","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",5493121,"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 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 vehicles, persons, road signs and two-wheelers. |\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 cars, persons, road signs, and bicycles within an image and returns bounding box coordinates, labels, and confidence scores for each category. |\n| Technical Limitations: | Model accuracy may be reduced in low-light, low contrast lighting conditions, with warped images, objects positioning relatively close to or far away from the camera frame (smaller than 20x20 pixels), and with objects occluded (less than 40% of the object is visible) or truncated. The accuracy may also be lower if run against images from other countries, especially if the country has a very different traffic pattern than the US. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Accuracy, Precision, Recall |\n| Potential Known Risks: | This model may misclassify cars, persons, road signs, and bicycles. |\n| Licensing: | [https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/) |","privacy","| Field | Response |\n| -- | -- |\n| Generatable or reverse engineerable personally-identifiable information (PII)? | None |\n| Protected classes used to create this model? | None |\n| Was consent obtained for any PII used? | Yes |\n| How often is dataset reviewed? | \tBefore Release |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | Yes |\n| If PII collected for the development of the model, was it collected directly by NVIDIA? | Yes, PII was collected by NVIDIA. |\n| If PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects?\t| Yes |\n| If PII collected for the development of this AI model, was it minimized to only what was required? | Yes |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | Yes |\n| Applicable NVIDIA Privacy Policy\t| [https://www.nvidia.com/en-us/about-nvidia/privacy-policy/](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) |","safetyAndSecurity","| Field | Response |\n| -- | -- |\n| Model Application(s): | Object Detection |\n| Describe the life-critical application (if present). | None |\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",[236],"displayLabels",[219,220,221],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_181":233,"_223":234,"_225":235,"_227":228},{"_181":231,"_223":231,"_225":232,"_227":228},{"_181":222,"_223":224,"_225":226,"_227":228},"NVIDIA AI","value","soln_nvidia_ai","filter",{"_229":230,"_223":224},"category","label","field","technology","NSPECT-HZG0-FMSH",{"_229":228,"_223":231},"AI","soln_ai",{"_229":230,"_223":234},{"_181":237,"_223":237,"_225":238,"_227":239},"NVIDIA AI Enterprise Supported",{"_229":228,"_223":237},"feature","appConfig",{"_1377":1378,"_274":1379},"ngcApps",[1330],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1311":1312,"_1313":1314,"_1315":1316,"_1317":1318,"_1319":252,"_79":281,"_1320":1321,"_1322":44,"_1323":110,"_1324":110,"_1325":1326,"_1327":110,"_1328":1329},"amplitude",{"_1308":1309,"_917":-7,"_1310":20},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0yJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1302":1303,"_1304":1305,"_1306":1307},"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",1792830149503,"1.28.1","defaultSearchSuggestions",{"_1187":113,"_1188":1189,"_1190":1191,"_1192":1193},"announcements",[],"consentState",{"_319":1177,"_1178":20,"_1179":20,"_1180":110,"_1181":1182},"consentConfig",{"_301":302,"_303":304,"_305":306,"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318,"_319":320},"showContactAdmin",1792830149504,[],[],[295,296,297,298,299,300],{"_33":47,"_35":56,"_37":64,"_39":-5,"_41":-5,"_43":44},{"_33":47,"_35":54,"_37":64,"_39":-5,"_41":-5,"_43":44},{"_33":47,"_35":52,"_37":64,"_39":-5,"_41":-5,"_43":44},{"_33":47,"_35":48,"_37":64,"_39":-5,"_41":-5,"_43":44},{"_33":45,"_35":46,"_37":64,"_39":-5,"_41":-5,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":123,"_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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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503],[343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360],{"_361":461,"_181":461,"_112":113,"_333":462},{"_361":457,"_181":457,"_112":113,"_333":458},{"_361":452,"_181":452,"_112":113,"_333":453},{"_361":448,"_181":448,"_112":113,"_333":449},{"_361":443,"_181":443,"_112":113,"_333":444},{"_361":438,"_181":438,"_112":113,"_333":439},{"_361":431,"_181":431,"_112":113,"_333":432},{"_361":423,"_181":423,"_112":113,"_333":424},{"_361":418,"_181":418,"_112":113,"_333":419},{"_361":413,"_181":413,"_112":113,"_333":414},{"_361":405,"_181":405,"_112":113,"_333":406},{"_361":400,"_181":400,"_112":113,"_333":401},{"_361":395,"_181":395,"_112":113,"_333":396},{"_361":390,"_181":390,"_112":113,"_333":391},{"_361":383,"_181":383,"_112":113,"_333":384},{"_361":378,"_181":378,"_112":113,"_333":379},{"_361":371,"_181":371,"_112":113,"_333":372},{"_361":362,"_181":362,"_112":113,"_333":363},"hostName","amazon-adsystem.com",[364,365],{"_152":370,"_367":362,"_368":20,"_112":369},{"_152":366,"_367":362,"_368":20,"_112":369},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[373,374],{"_152":377,"_367":371,"_368":20,"_112":376},{"_152":375,"_367":371,"_368":20,"_112":376},"IDE","This domain is owned by Doubleclick (Google). 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