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Field	Response
Participation considerations from adversely impacted groups (protected classes) in model design and testing:	None of the above
Measures taken to mitigate against unwanted bias:	None of the above.

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TrafficCamNet Transformer - Lite

Model Overview

Description

TrafficCamNet Transformer - Lite is an object-detection model that detects one or more objects from the following four categories in an image and returns a bounding box and category label for each detected object.

car
road sign
person
bicycle

This model is ready for commercial use.

GOVERNING TERMS:

Use of this model is governed by the NVIDIA Open Model License Agreement.

Deployment Geography:

Global

Use Case

This model can be used in computer-vision use cases to detect cars, road signs, people, and two-wheelers in video.

Release Date:

NGC [11/14/2025] via URL

Reference

Y. Zhao, W. Lv, S. Xu, J. Wei, G. Wang, Q. Dang, Y. Liu, J. Chen: DETRs Beat YOLOs on Real-time Object Detection

Model Architecture

Architecture Type: Convolution Neural Network + Transformer Encoder Decoder.\nNetwork Architecture:

This model was developed based on the RT-DETR object detection model with a CNN backbone.\nThe CNN in the backbone is a ResNet50 model, but the detection head is a Transformer encoder decoder.

** Number of model parameters: 4.5 * 10^7

Computational Load (Internal Only: For NVIDIA Models Only)

Cumulative Compute: 6.15 * 10^13
\nEstimated Energy and Emissions for Model Training: 2.5 * 10^3 KWH

Input

Input Type: Image
\nInput Formats: Red, Green, Blue (RGB)
\nInput Parameters: Two-Dimensional (2D)
\nOther Properties Related to Input: Minimum 32 x 32 Resolution required; no alpha channel or bits

\n
Note: All model variants were fine-tuned with 3x544x960 (CxHxW) image input.
\n

Output

Output Type(s): Bounding boxes and Class labels
\nOutput Parameters: One Dimensional (1D), Two Dimensional (2D) vectors
\nOther Properties Related to Output:

pred_logits: B x 300 (Batch Size x Number of Queries)
pred_boxes: B x 300 x 4 (Batch Size x Number of Queries x Coordinates in cxcywh format)

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems.\nBy leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries),\nthe model achieves faster training and inference times compared to CPU-only solutions.

How to Use This Model

This model requires NVIDIA hardware and software. Hardware requirements: any NVIDIA GPU with at least 12 GB of memory. Training and fine-tuning are supported with the TAO Toolkit.

The primary use case for this models is object detection.

The model is intended to be fine-tuned using the Train, Adapt, Optimize (TAO) Toolkit or used directly with deployment SDKs such as DeepStream, Triton, or TensorRT for object detection in traffic systems. High-fidelity models can be trained to detect classes not originally included in the model. A Jupyter notebook is available as part of the TAO container and can be used for retraining.

Instructions to Use Pretrained Models with TAO

To use these models as pretrained weights for transfer learning, use the following snippet as a template for\nthe model, dataset and train components of the experiment spec file to train a RT-DETR model.\nFor more information on the experiment spec file, see the TAO Toolkit User Guide.

train:\n  pretrained_model_path: /path/to/trafficcamnet/resnet50_its.pth\n  precision: 'bf16'\n  checkpoint_interval: 10\n  validation_interval: 10\n  num_epochs: 100\nmodel:\n  backbone: resnet_50\n  train_backbone: true\ndataset:\n  train_data_sources:\n  - image_dir: /path/to/train/images\n    json_file: /path/to/coco/format/train/annotations.json\n  val_data_sources:\n    image_dir: /path/to/validation/images\n    json_file: /path/to/coco/format/val/annotations.json\n  test_data_sources:\n    image_dir: /path/to/test/images\n    json_file: /path/to/coco/format/test/annotations.json\n  infer_data_sources:\n    image_dir:\n    - /media/scratch.metropolis3/vpraveen/datasets/its_datasets/legacy_datasets_val/images\n    classmap: /path/to/labels.txt\n  batch_size: 4\n  workers: 8\n  remap_mscoco_category: false\n  pin_memory: true\n  dataset_type: serialized\n  num_classes: 5\n  eval_class_ids: null\n  augmentation:\n    multi_scales:\n    - - 480\n      - 832\n    - - 512\n      - 896\n    - - 544\n      - 960\n    - - 544\n      - 960\n    - - 544\n      - 960\n    - - 576\n      - 992\n    - - 608\n      - 1056\n    - - 672\n      - 1184\n    - - 704\n      - 1216\n    - - 736\n      - 1280\n    - - 768\n      - 1344\n    - - 800\n      - 1408\n    train_spatial_size:\n    - 544\n    - 960\n    eval_spatial_size:\n    - 544\n    - 960\n

Software Integration

Runtime Engine:

TAO 6.0.0

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Blackwell
NVIDIA Hopper
NVIDIA Lovelace
NVIDIA Pascal
NVIDIA Turing

[Preferred/Supported] Operating System(s):

Linux

Model Versions

trainable_resnet50_v2.0 - Pre-trained Resnet50 variant of the model to facilitate transfer learning via TAO Toolkit.
deployable_resnet50_v2.0 - Pre-trained Resnet50 variant of the model that's optimized for deployment via TensorRT.
trainable_resnet18_v2.0 - Pre-trained Resnet18 variant of the model to facilitate transfer learning via TAO Toolkit.
deployable_resnet18_v2.0 - Pre-trained ResNet18 variant of the model that's optimized for deployment via TensorRT.

Training, Testing, and Evaluation Datasets:

Training Datasets

Link:

ITS Dataset (NVIDIA Internal)

Data Modality

Image

Image Training Data Size

Less than a Million Images

Data Collection Method by dataset:

Hybrid: Automated, Human (custom collected and curated)

Labeling Method by dataset:

Hybrid: Automated, Human (custom collected and curated)

Properties:

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

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

Dataset	No. of Images
NV Internal Data	228K

Testing Datasets

Link:

ITS Dataset (NVIDIA Internal)

Data Modality

Image\nData Collection Method by dataset:
Hybrid: Automated, Human (custom collected and curated)

Labeling Method by dataset:

Hybrid: Automated, Human

Properties:

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

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

Dataset	No. of Images
NV Internal Data	10K

Evaluation Datasets

Link:

ITS Dataset (NVIDIA Internal)

Data Collection Method by dataset:

Hybrid: Automated, Human (custom collected and curated)

Labeling Method by dataset:

Hybrid: Automated, Human

Properties:

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

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

Dataset	No. of Images
NV Internal Data	5K

Performance

Evaluation Data

We evaluated the TrafficCamNet Transformer - Lite models on a curated dataset of 18,500 proprietary images across a variety of environments and traffic intersections, with corresponding bounding-box annotations for cars, road signs, people, and bicycles. These frames are high-resolution 1920x1080 images, resized to 960x544 before running inference with the model.

Methodology and KPI

We compute mean Average Precision at IoU = 0.50 (mAP50). The table below reports per-class mAP50 across multiple datasets/conditions. Values are in [0, 1].

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

Class	Model	SUTD_HL	SUTD_rainy	SUTD_sunny	SUTD_day	SUTD_foggy	SUTD_cloudy	SUTD_snowy	SUTD_night	DAWN	DAWN_snow	DAWN_fog	DAWN_rain	DAWN_sand	ACDC	ACDC_rain	ACDC_snow	ACDC_fog	ACDC_night	GGBridge	CowHollow	MarketFerry
bicycle	TrafficCamNet Transformer - Lite	0.61	0.6	0.61	0.62	0.59	0.62	0.44	0.51	0.8	1.0	0.8	0.95	0.8	0.37	0.44	0.44	0.57	0.25	0.59	0.92	0.54
car	TrafficCamNet Transformer - Lite	0.81	0.81	0.8	0.82	0.8	0.83	0.84	0.74	0.77	0.72	0.79	0.84	0.75	0.81	0.83	0.85	0.85	0.68	0.96	0.87	0.9
person	TrafficCamNet Transformer - Lite	0.63	0.62	0.63	0.64	0.7	0.63	0.64	0.54	0.75	0.81	0.76	0.65	0.73	0.58	0.45	0.71	0.7	0.51	0.77	0.6	0.88
road_sign	TrafficCamNet Transformer - Lite	0.41	0.37	0.4	0.41	0.46	0.46	0.37	0.38	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.71	0.61	0.25

Inference

Acceleration Engine: Tensor(RT), DeepStream

The TrafficCamNet Transformer - Lite model inference can be run via DeepStream using the same tao_detection apps.

Test Hardware:

A2
A30
DGX H100
DGX A100
L4
L40
Orin
Orin Nano 8GB
Orin NX
Orin NX16GB

Inference is performed on the provided unpruned model at FP16 precision. Inference performance is measured using trtexec on Jetson AGX Xavier, Xavier NX, Orin, Orin NX, NVIDIA T4, and Ampere GPUs.\nJetson devices run in the Max-N configuration to achieve maximum GPU frequency. The performance shown here reflects inference-only results. End-to-end performance with streaming video may vary depending on hardware and software bottlenecks.

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

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

Platform	BS	FPS
Jetson Orin	8	86.107
RTX 4060Ti	8	278.151
RTX 4090	8	540.533
A100	32	808.631

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.\nFor more detailed information on ethical considerations for this model, please see the Model Card++ Bias, Explainability, Safety & Security, and Privacy Subcards.\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

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\n**Estimated Energy and Emissions for Model Training:** 2.5 * 10^3 KWH\n\n### Input\n\n**Input Type:** Image
\n**Input Formats:** Red, Green, Blue (RGB)
\n**Input Parameters**: Two-Dimensional (2D)
\n**Other Properties Related to Input:** Minimum 32 x 32 Resolution required; no alpha channel or bits\n\n> Note: All model variants were fine-tuned with 3x544x960 (CxHxW) image input.\n\n### Output\n\n**Output Type(s):** Bounding boxes and Class labels
\n**Output Parameters:** One Dimensional (1D), Two Dimensional (2D) vectors
\n**Other Properties Related to Output:**\n - `pred_logits`: B x 300 (Batch Size x Number of Queries)\n - `pred_boxes`: B x 300 x 4 (Batch Size x Number of Queries x Coordinates in `cxcywh` format)\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems.\nBy leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries),\nthe model achieves faster training and inference times compared to CPU-only solutions.\n\n## How to Use This Model\n\nThis model requires NVIDIA hardware and software. Hardware requirements: any NVIDIA GPU with at least 12 GB of memory. Training and fine-tuning are supported with the [TAO Toolkit](https://developer.nvidia.com/tao-toolkit).\n\nThe primary use case for this models is object detection.\n\nThe model is intended to be fine-tuned using the Train, Adapt, Optimize (TAO) Toolkit or used directly with deployment SDKs such as DeepStream, Triton, or TensorRT for object detection in traffic systems. High-fidelity models can be trained to detect classes not originally included in the model. A Jupyter notebook is available as part of the [TAO container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit-pytorch) and can be used for retraining.\n\n### Instructions to Use Pretrained Models with TAO\n\nTo use these models as pretrained weights for transfer learning, use the following snippet as a template for\nthe `model`, `dataset` and `train` components of the experiment spec file to train a RT-DETR model.\nFor more information on the experiment spec file, see the [TAO Toolkit User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\n```yaml\ntrain:\n pretrained_model_path: /path/to/trafficcamnet/resnet50_its.pth\n precision: 'bf16'\n checkpoint_interval: 10\n validation_interval: 10\n num_epochs: 100\nmodel:\n backbone: resnet_50\n train_backbone: true\ndataset:\n train_data_sources:\n - image_dir: /path/to/train/images\n json_file: /path/to/coco/format/train/annotations.json\n val_data_sources:\n image_dir: /path/to/validation/images\n json_file: /path/to/coco/format/val/annotations.json\n test_data_sources:\n image_dir: /path/to/test/images\n json_file: /path/to/coco/format/test/annotations.json\n infer_data_sources:\n image_dir:\n - /media/scratch.metropolis3/vpraveen/datasets/its_datasets/legacy_datasets_val/images\n classmap: /path/to/labels.txt\n batch_size: 4\n workers: 8\n remap_mscoco_category: false\n pin_memory: true\n dataset_type: serialized\n num_classes: 5\n eval_class_ids: null\n augmentation:\n multi_scales:\n - - 480\n - 832\n - - 512\n - 896\n - - 544\n - 960\n - - 544\n - 960\n - - 544\n - 960\n - - 576\n - 992\n - - 608\n - 1056\n - - 672\n - 1184\n - - 704\n - 1216\n - - 736\n - 1280\n - - 768\n - 1344\n - - 800\n - 1408\n train_spatial_size:\n - 544\n - 960\n eval_spatial_size:\n - 544\n - 960\n```\n### Software Integration\n\n**Runtime Engine:**\n * TAO 6.0.0\n\n**Supported Hardware Microarchitecture Compatibility:**\n * NVIDIA Ampere\n * NVIDIA Blackwell\n * NVIDIA Hopper\n * NVIDIA Lovelace\n * NVIDIA Pascal\n * NVIDIA Turing\n\n**[Preferred/Supported] Operating System(s):**\n\n * Linux\n\n### Model Versions\n- **trainable_resnet50_v2.0** - Pre-trained Resnet50 variant of the model to facilitate transfer learning via TAO Toolkit.\n- **deployable_resnet50_v2.0** - Pre-trained Resnet50 variant of the model that's optimized for deployment via TensorRT.\n- **trainable_resnet18_v2.0** - Pre-trained Resnet18 variant of the model to facilitate transfer learning via TAO Toolkit.\n- **deployable_resnet18_v2.0** - Pre-trained ResNet18 variant of the model that's optimized for deployment via TensorRT.\n\n## Training, Testing, and Evaluation Datasets:\n\n### Training Datasets\n\n **Link:** \n * ITS Dataset (NVIDIA Internal)\n\n **Data Modality**\n * Image\n\n **Image Training Data Size**\n * Less than a Million Images\n\n **Data Collection Method by dataset:**
\n * Hybrid: Automated, Human (custom collected and curated)
\n\n **Labeling Method by dataset:**
\n * Hybrid: Automated, Human (custom collected and curated)
\n\n **Properties:**
\n\n | Dataset | No. of Images |\n |--|--|\n | NV Internal Data | 228K |\n\n### Testing Datasets\n\n **Link:** \n * ITS Dataset (NVIDIA Internal)\n\n **Data Modality**\n * Image\n **Data Collection Method by dataset:**
\n * Hybrid: Automated, Human (custom collected and curated)
\n\n **Labeling Method by dataset:**
\n * Hybrid: Automated, Human
\n\n **Properties:**
\n | Dataset | No. of Images |\n |--|--|\n |NV Internal Data| 10K | \n\n### Evaluation Datasets\n\n **Link:** \n * ITS Dataset (NVIDIA Internal)\n\n **Data Collection Method by dataset:**
\n * Hybrid: Automated, Human (custom collected and curated)
\n\n **Labeling Method by dataset:**
\n * Hybrid: Automated, Human
\n\n **Properties:**
\n\n | Dataset | No. of Images |\n |--|--|\n |NV Internal Data| 5K | \n\n## Performance\n\n### Evaluation Data \n\nWe evaluated the TrafficCamNet Transformer - Lite models on a curated dataset of 18,500 proprietary images across a variety of environments and traffic intersections, with corresponding bounding-box annotations for cars, road signs, people, and bicycles. These frames are high-resolution 1920x1080 images, resized to 960x544 before running inference with the model.\n\n### Methodology and KPI\n\nWe compute mean Average Precision at IoU = 0.50 (mAP50). The table below reports per-class mAP50 across multiple datasets/conditions. Values are in [0, 1].\n\n| Class | Model | SUTD_HL | SUTD_rainy | SUTD_sunny | SUTD_day | SUTD_foggy | SUTD_cloudy | SUTD_snowy | SUTD_night | DAWN | DAWN_snow | DAWN_fog | DAWN_rain | DAWN_sand | ACDC | ACDC_rain | ACDC_snow | ACDC_fog | ACDC_night | GGBridge | CowHollow | MarketFerry |\n|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|--|\n| bicycle | TrafficCamNet Transformer - Lite | 0.61 | 0.6 | 0.61 | 0.62 | 0.59 | 0.62 | 0.44 | 0.51 | 0.8 | 1.0 | 0.8 | 0.95 | 0.8 | 0.37 | 0.44 | 0.44 | 0.57 | 0.25 | 0.59 | 0.92 | 0.54 |\n| car | TrafficCamNet Transformer - Lite | 0.81 | 0.81 | 0.8 | 0.82 | 0.8 | 0.83 | 0.84 | 0.74 | 0.77 | 0.72 | 0.79 | 0.84 | 0.75 | 0.81 | 0.83 | 0.85 | 0.85 | 0.68 | 0.96 | 0.87 | 0.9 |\n| person | TrafficCamNet Transformer - Lite | 0.63 | 0.62 | 0.63 | 0.64 | 0.7 | 0.63 | 0.64 | 0.54 | 0.75 | 0.81 | 0.76 | 0.65 | 0.73 | 0.58 | 0.45 | 0.71 | 0.7 | 0.51 | 0.77 | 0.6 | 0.88 |\n| road_sign | TrafficCamNet Transformer - Lite | 0.41 | 0.37 | 0.4 | 0.41 | 0.46 | 0.46 | 0.37 | 0.38 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.71 | 0.61 | 0.25 |\n\n## Inference \n\n**Acceleration Engine:** Tensor(RT), DeepStream
\n\nThe TrafficCamNet Transformer - Lite model inference can be run via DeepStream using the same [tao_detection](https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps/tree/master/apps/tao_detection) apps.\n\n**Test Hardware:**
\n- A2\n- A30\n- DGX H100\n- DGX A100\n- L4\n- L40\n- Orin\n- Orin Nano 8GB\n- Orin NX\n- Orin NX16GB\n\nInference is performed on the provided unpruned model at FP16 precision. Inference performance is measured using [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/main/samples/trtexec) on Jetson AGX Xavier, Xavier NX, Orin, Orin NX, NVIDIA T4, and Ampere GPUs.\nJetson devices run in the Max-N configuration to achieve maximum GPU frequency. The performance shown here reflects inference-only results. End-to-end performance with streaming video may vary depending on hardware and software bottlenecks.\n\n| Platform | BS | FPS |\n|------------------|----|--------|\n| Jetson Orin\t | 8 | 86.107 |\n| RTX 4060Ti\t | 8 | 278.151|\n| RTX 4090\t | 8 | 540.533|\n| A100 | 32 | 808.631|\n\n## Ethical Considerations \n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\nFor more detailed information on ethical considerations for this model, please see the Model Card++ Bias, Explainability, Safety & Security, and Privacy Subcards.\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","TrafficCamNet Transformer Lite",["D",1765812338408],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/trafficcamnet_transformer_lite","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",511959861,"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: | None of the above.|","explainability","| Field | Response |\n| -- | -- |\n| Intended Application(s) & Domain(s): | Detecting Objects |\n| Model Type: | Convolutional Neural Network + Transformer Encoder Decoder |\n| Intended Users: | The model is intended for developers that build computer vision applications to detect cars, road signs, persons or two-wheelers in a video. |\n| Output: | Bounding Box, Class Labels |\n| Describe how the model works: | Extracts features from provided images for the car, persons, two-wheelers and road-signs. |\n| Technical Limitations: | The model may not perform well in low-light and low contrast video sequences and not detect cars that are more than 80% occluded. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Mean Average Precision (mAP) |\n| Licensing: | [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/)|\n| Potential Known Risks: | The model may not not detect objects that are more than 80% occluded. |","privacy","| Field | Response |\n|-------|----------|\n| Generatable or reverse engineerable personal data? | No |\n| Personal data used to create this model? | Yes|\n| Was consent obtained for any personal data used? | Yes |\n| How often is dataset reviewed? | During dataset creation, model training, evaluation and before release |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | Yes |\n| If personal data was collected for the development of the model, was it collected directly by NVIDIA? | Yes |\n| If personal data was collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Yes |\n| If personal data was 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? | No, not possible with externally-sourced data. |\n| Applicable Privacy Policy | [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). | Not recommended for safety critical use cases |\n| Use Case Restrictions: | Abide by [NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/) |\n| Describe access restrictions (if any): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |","featureLabels",[],"displayLabels",[210,211],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_172":219,"_213":219,"_214":220,"_216":217},{"_172":212,"_213":212,"_214":215,"_216":217},"NSPECT-YF0Y-6AVF","value","filter",{"_218":217,"_213":212},"category","label","field","NSPECT-FY5H-V6CO",{"_218":217,"_213":219},"appConfig",{"_1359":1360,"_255":1361},"ngcApps",[1312],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1293":1294,"_1295":1296,"_1297":1298,"_1299":1300,"_1301":233,"_78":262,"_1302":1303,"_1304":44,"_1305":111,"_1306":111,"_1307":1308,"_1309":111,"_1310":1311},"amplitude",{"_1290":1291,"_899":-7,"_1292":20},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0yJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1284":1285,"_1286":1287,"_1288":1289},"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",1794316030803,"1.30.0","defaultSearchSuggestions",{"_1169":114,"_1170":1171,"_1172":1173,"_1174":1175},"announcements",[],"consentState",{"_301":1159,"_1160":20,"_1161":20,"_1162":111,"_1163":1164},"consentConfig",{"_283":284,"_285":286,"_287":288,"_289":290,"_291":292,"_293":294,"_295":296,"_297":298,"_299":300,"_301":302},"showContactAdmin",1794316030804,[],[],[276,277,278,279,280,281],{"_33":48,"_35":56,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":55,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":51,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":49,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":45,"_35":46,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":282,"_43":44},4,"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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