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FaceDetect-IR Model Card

Model Overview

The model described in this card detects one or more faces in the given image / video. Compared to the PeopleNet model, this model gives better results detecting large faces, such as faces in webcam images.

Model Architecture

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.

Training

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

FaceDetectIR v1.0 model was trained on a proprietary dataset with more than 846K faces. The training dataset consists of images taken from cameras mounted at varied heights and angles, cameras of varied field-of view (FOV) and occlusions. While most training images are IR (gray) images, some color (RGB) images are added as seeded content to improve accuracy of the models.

Training Data Ground-truth Labeling Guidelines

The training dataset is created by labeling ground-truth bounding-boxes and categories by human labellers. Following guidelines were used while labelling the training data for NVIDIA FaceDetectIR model.

FaceDetectIR project labelling guidelines

Face bounding boxes should be as tight as possible.
Label each face bounding box with an occlusion level ranging from 0 to 9. 0 means the face is fully visible and 9 means the face is 90% or more occluded. For training, only faces with occlusion level 0-5 are considered.
The datasets consist of webcam images so truncation is rarely seen. If faces are at the edge of the frame with visibility less than 60% due to truncation, this image is dropped from the dataset.

Performance

Evaluation Data

The inference performance of FaceIR v1.0 model was measured against 16559 proprietary images across a variety of environments, occlusion conditions, camera heights and camera angles.

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 key performance indicators (KPI) are calculated for IR images only. There are no color images in our evaluation set.

\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		FaceDetectIR
Content	Precision (in %)	Recall (in %)	Accuracy (in %)
Evaluation set	99.8	96.40	96.21

Real-time Inference Performance

The inference is run on the provided pruned model at INT8 precision. The inference performance is run using trtexec on Jetson Nano, AGX Xavier, Xavier NX and NVIDIA T4 GPU. On the Jetson Nano FP16 inference is run. 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

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

The primary use case for this model is to detect faces from an IR (infrared) camera. The model can be used to detect faces 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 faces in RGB (color) images. However, this is not the main intended use for this model.

There are two flavors of the model:

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 384x240x3 dimension input tensors and outputs 24x15x4 bbox coordinate tensor and 24x15x1 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 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.

Input

Gray Image whose values in RGB channels are the same. 384 X 240 X 3 (W x H x C)\nChannel Ordering of the Input: NCHW, where N = Batch Size, C = number of channels (3), H = Height of images (240), W = Width of the images (384)\nInput scale: 1/255.0\nMean subtraction: None

Output

Category labels (faces) and bounding-box coordinates for each detected face in the input image.

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 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\nmkdir -p $HOME/facedetectir && \\\nwget https://api.ngc.nvidia.com/v2/models/nvidia/tao/facedetectir/versions/pruned_v1.0/files/resnet18_facedetectir_pruned.etlt \\\n-O $HOME/facedetectir/resnet18_facedetectir_pruned.etlt && \\\nwget https://api.ngc.nvidia.com/v2/models/nvidia/tao/facedetectir/versions/pruned_v1.0/files/facedetectir_int8.txt \\\n-O $HOME/facedetectir/facedetectir_int8.txt\n\n## Run Application\n\nxhost +\nsudo docker run --gpus all -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$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 \\\ndeepstream-app -c deepstream_app_source1_labels_faceirnet.txt\n

\n
Install deepstream on your local host and run the deepstream-app.
\n
To deploy this model with DeepStream 5.1, please follow the instructions below:
\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_faceirnet.txt - Main config file for DeepStream app\nconfig_infer_primary_faceirnet.txt - File to configure inference settings\nlabels_faceirnet.txt - Label file with 1 class\n
```
\n
Key Parameters in config_infer_primary_faceirnet.txt
\n
```
tlt-model-key\ntlt-encoded-model\nlabelfile-path\nint8-calib-file\ninput-dims\nnum-detected-classes\n
```
\n
Run deepstream-app:
\n
```
deepstream-app -c deepstream_app_source1_faceirnet.txt\n
```
\n
Documentation to deploy with DeepStream is provided in \"Deploying to DeepStream\" chapter of TAO User Guide.
\n

Limitations

Small faces

NVIDIA FaceIR model does not give good results on detecting small faces (generally, if the face occupies less than 10% of the image area, the face is small)

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.

References

Citations

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

Technical blogs

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

License

License to use these models is covered by the Model EULA. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of these licenses.

Ethical Considerations

NVIDIA FaceIR model detects faces. However, no additional information such as race, gender, and skin type about the faces is inferred.

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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Compared to the PeopleNet model, this model gives better results detecting large faces, such as faces in webcam images.\n\n## Model Architecture \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.\n\nThe 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## Training \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 \n\nFaceDetectIR v1.0 model was trained on a proprietary dataset with more than 846K faces. The training dataset consists of images taken from cameras mounted at varied heights and angles, cameras of varied field-of view (FOV) and occlusions. While most training images are IR (gray) images, some color (RGB) images are added as seeded content to improve accuracy of the models.\n\n### Training Data Ground-truth Labeling Guidelines \n\nThe training dataset is created by labeling ground-truth bounding-boxes and categories by human labellers. Following guidelines were used while labelling the training data for NVIDIA FaceDetectIR model.\n\nFaceDetectIR project labelling guidelines\n\n- Face bounding boxes should be as tight as possible.\n- Label each face bounding box with an occlusion level ranging from 0 to 9. 0 means the face is fully visible and 9 means the face is 90% or more occluded. For training, only faces with occlusion level 0-5 are considered.\n- The datasets consist of webcam images so truncation is rarely seen. If faces are at the edge of the frame with visibility less than 60% due to truncation, this image is dropped from the dataset.\n\n## Performance\n\n### Evaluation Data \n\nThe inference performance of FaceIR v1.0 model was measured against 16559 proprietary images across a variety of environments, occlusion conditions, camera heights and camera angles.\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 key performance indicators (KPI) are calculated for IR images only. There are no color images in our evaluation set.\n\n| Model | | FaceDetectIR | |\n|---|---|---|---|\n| Content | Precision (in %) | Recall (in %) | Accuracy (in %) |\n|Evaluation set| 99.8 | 96.40 | 96.21 |\n\n### Real-time Inference Performance \n\nThe inference is run on the provided pruned model at INT8 precision. The inference performance is run using [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/master/samples/opensource/trtexec) on Jetson Nano, AGX Xavier, Xavier NX and NVIDIA T4 GPU. On the Jetson Nano FP16 inference is run. 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\nThis model needs to be used with NVIDIA Hardware and Software. For Hardware, the model can run on any NVIDIA GPU including NVIDIA Jetson devices. This model can only be used with [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 for this model is to detect faces from an IR (infrared) camera. The model can be used to detect faces 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 faces in RGB (color) images. However, this is not the main intended use for this model.\n\nThere are two flavors of the model:\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 `384x240x3` dimension input tensors and outputs `24x15x4` bbox coordinate tensor and `24x15x1` 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` 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### Input \n\nGray Image whose values in RGB channels are the same. 384 X 240 X 3 (W x H x C)\nChannel Ordering of the Input: NCHW, where N = Batch Size, C = number of channels (3), H = Height of images (240), W = Width of the images (384)\nInput scale: 1/255.0\nMean subtraction: None\n\n### Output \n\nCategory labels (faces) and bounding-box coordinates for each detected face in the input image.\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 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/facedetectir && \\\n wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/facedetectir/versions/pruned_v1.0/files/resnet18_facedetectir_pruned.etlt \\\n -O $HOME/facedetectir/resnet18_facedetectir_pruned.etlt && \\\n wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/facedetectir/versions/pruned_v1.0/files/facedetectir_int8.txt \\\n -O $HOME/facedetectir/facedetectir_int8.txt\n\n ## Run Application\n\n xhost +\n sudo docker run --gpus all -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$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_labels_faceirnet.txt\n ```\n\n2. Install deepstream on your local host and run the deepstream-app.\n\n To deploy this model with [DeepStream 5.1](https://developer.nvidia.com/deepstream-sdk), please follow the instructions below:\n\n [Download](https://developer.nvidia.com/deepstream-sdk) and install DeepStream SDK. The installation instructions for DeepStream are provided in [DeepStream development guide](https://docs.nvidia.com/metropolis/deepstream/dev-guide/index.html). The config files for the purpose-built models are located in:\n\n ```sh\n /opt/nvidia/deepstream/deepstream-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_faceirnet.txt - Main config file for DeepStream app\n config_infer_primary_faceirnet.txt - File to configure inference settings\n labels_faceirnet.txt - Label file with 1 class\n ```\n\n Key Parameters in `config_infer_primary_faceirnet.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_faceirnet.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## Limitations \n\n### Small faces\n\nNVIDIA FaceIR model does not give good results on detecting small faces (generally, if the face occupies less than 10% of the image area, the face is small)\n\n## Model versions\n\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## References\n\n### Citations \n\nRedmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: CVPR. (2016)\nErhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks, In: CVPR. (2014)\nHe, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: CVPR (2015)\n\n### Using TAO Pre-trained Models \n\n- Get [TAO Container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit)\n- Get other Purpose-built models from NGC model registry:\n - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)\n - [TrafficCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:trafficcamnet)\n - [DashCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:dashcamnet)\n - [VehicleMakeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehiclemakenet)\n - [VehicleTypeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehicletypenet)\n\n### Technical blogs \n\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_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## License \n\nLicense to use these models is covered by the Model EULA. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of these [licenses](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/).\n\n## Ethical Considerations \n\nNVIDIA FaceIR model detects faces. However, no additional information such as race, gender, and skin type about the faces is inferred.\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","FaceDetectIR",["D",1731434401235],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/facedetectir","rawImageUrl","https://raw.githubusercontent.com/kbojo/images/master/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",9532530,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[302],"displayLabels",[243,244,245,246,247,248,249,250,251,252,253,254,255,256],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_210":300,"_258":300,"_259":301,"_261":262},{"_210":297,"_258":298,"_259":299,"_261":262},{"_210":295,"_258":295,"_259":296,"_261":262},{"_210":292,"_258":293,"_259":294,"_261":262},{"_210":290,"_258":290,"_259":291,"_261":262},{"_210":286,"_258":287,"_259":288,"_261":262},{"_210":284,"_258":284,"_259":285,"_261":262},{"_210":280,"_258":281,"_259":282,"_261":262},{"_210":276,"_258":277,"_259":278,"_261":262},{"_210":274,"_258":274,"_259":275,"_261":262},{"_210":271,"_258":272,"_259":273,"_261":262},{"_210":267,"_258":268,"_259":269,"_261":262},{"_210":265,"_258":265,"_259":266,"_261":262},{"_210":257,"_258":257,"_259":260,"_261":262},"TAO Toolkit","value","filter",{"_263":264,"_258":257},"category","label","field","framework","Smart Infrastructure",{"_263":262,"_258":265},"Smart Cities / Spaces","indus_smart_cities_spaces",{"_263":270,"_258":268},"industry","Retail","indus_retail",{"_263":270,"_258":272},"Public Safety",{"_263":262,"_258":274},"Object Detection","uscs_object_detection",{"_263":279,"_258":277},"application","NVIDIA AI","soln_nvidia_ai",{"_263":283,"_258":281},"technology","NSPECT-1DBQ-GZ4A",{"_263":262,"_258":284},"Metropolis","pltfm_metropolis",{"_263":289,"_258":287},"platform","IR",{"_263":262,"_258":290},"Healthcare","indus_healthcare",{"_263":270,"_258":293},"DetectNet_v2",{"_263":262,"_258":295},"DeepStream","pltfm_deepstream",{"_263":289,"_258":298},"CV",{"_263":262,"_258":300},{"_210":303,"_258":303,"_259":304,"_261":305},"NVIDIA AI Enterprise Supported",{"_263":262,"_258":303},"feature",{"_106":1480,"_108":1481},"ngcApps",[1433],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1414":1415,"_1416":1417,"_1418":1419,"_1420":1421,"_1422":317,"_137":341,"_1423":1424,"_1425":55,"_1426":85,"_1427":85,"_1428":1429,"_1430":85,"_1431":1432},"amplitude",{"_1411":1412,"_1014":-7,"_1413":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1405":1406,"_1407":1408,"_1409":1410},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1289":165,"_1290":1291,"_1292":1293,"_1294":1295},"announcements",[],"consentState",{"_379":1279,"_1280":85,"_1281":30,"_1282":30,"_1283":1284},"consentConfig",{"_361":362,"_363":364,"_365":366,"_367":368,"_369":370,"_371":372,"_373":374,"_375":376,"_377":378,"_379":380},"showContactAdmin",1783956625044,[],[],[355,356,357,358,359,360],{"_44":58,"_46":69,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":67,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":65,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":59,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":101,"_50":-5,"_52":-5,"_54":55},"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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This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. It is either correlated to the 6suuid or a unique value, and can be changed during the lifetime of this cookie.This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","s_ips","This cookie is associated with Adobe Analytics. The cookie is used for measuring a visitor’s scroll activity to see how much of a page they view before moving on to another page.","_gd_session","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","dmn_chk_-xxxxxxxxxxxx","com","Posthog Analytics","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_cs_id","blogs.nvidia.com","_session_id","resources.nvidia.com","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. 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