TAO Commercial Pretrained FAN Classification Model

What is Train Adapt Optimize (TAO) Toolkit?

Train Adapt Optimize (TAO) Toolkit is a Python-based AI toolkit for customizing purpose-built pre-trained AI models with your own data. TAO Toolkit adapts popular network architectures and backbones to your data, allowing you to train, fine tune, prune, and export highly optimized and accurate AI models for edge deployment.

The pre-trained models accelerate the AI training process and reduce costs associated with large scale data collection, labeling, and training models from scratch. Transfer learning with pre-trained models can be used for AI applications in smart cities, retail, healthcare, industrial inspection and more.

Build end-to-end services and solutions for transforming pixels and sensor data to actionable insights using the TAO DeepStream SDK and TensorRT. These models are suitable for object detection, classification, and segmentation.

Model Overview

Image Classification is a popular computer vision technique in which an image is classified into one of the designated classes based on the image features. This model card contains pretrained weights of most of the popular classification models. These weights that may be used as a starting point with the classification app in Train Adapt Optimize (TAO) Toolkit to facilitate transfer learning.

Model Architecture

FAN (Fully Attentional Network) is a transformer-based family of backbone from NVIDIA research that achieves SOTA in robustness against various corruptions. This family of backbone can easily generalize to new domains, be more robust to noise, blur etc. Key design behind FAN block is the attentional channel processing module that leads to robust representation learning. FAN can be used for image classification tasks as well as downstream tasks such as object detection and segmentation. Use FAN when your testing dataset has a domain gap from the training dataset.

Training

This model was trained using the classification_pyt entrypoint in TAO. The training algorithm optimizes the network to minimize the cross-entropy loss.

Training Data

The FAN models were trained on the NVImageNet dataset.

Performance

Evaluation Data

We have tested the FAN models on the ImageNet1K validation dataset.

Methodology and KPI

The key performance indicator is the accuracy, following the standard evaluation protocol for image classification. The KPI for the evaluation data are reported below.

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

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

model	top-1 accuracy
fan_large_hybrid_nvimagenet	69.1
fan_base_hybrid_nvimagenet	68.3
fan_small_hybrid_nvimagenet	68.38

Real-time Inference Performance

The inference is run on the provided unpruned model at FP16 precision. The inference performance is run using trtexec on Jetson AGX Xavier, Xavier NX, Orin, Orin NX and NVIDIA T4, and Ampere GPUs. 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.

FAN-B-H-384 (384 resolution)

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\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 Nano	4	16
Orin NX 16GB	4	23.4
AGX Orin 64GB	8	61.2
A2	8	55.5
T4	8	91
A30	16	260
L4	4	207
L40	4	558
A100	64	577
H100	64	985

FAN-L-H-384

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\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 Nano	-	-
Orin NX 16GB	-	-
AGX Orin 64GB	-	-
A2	8	38
T4	4	62
A30	8	179
L4	4	145
L40	4	366
A100	64	402
H100	64	681

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 for these models is classifying objects in a color (RGB) image. The model can be used to classify objects from photos and videos by using appropriate video or image decoding and pre-processing.

These models are intended for training and fine-tune using TAO Toolkit and user datasets for image classification. High-fidelity models can be trained to the new use cases. A Jupyter notebook is available as a part of the TAO container and can be used to re-train.

The models are also intended for easy edge deployment using DeepStream SDK or TensorRT. DeepStream provides the facilities to create efficient video analytic pipelines to capture, decode, and pre-process the data before running inference.

Input

B X 3 X 224 X 224 (B C H W)
B X 3 X 384 X 384 (B C H W)

Output

Category labels (1000 classes) for the input image.

Instructions to Use Pretrained Models with TAO

To use these models as pretrained weights for transfer learning, please use the snippet below as template for the model and train component of the experiment spec file to train a FAN Classification model. For more information on the experiment spec file, please refer to the TAO Toolkit User Guide.

model:\n  init_cfg:\n    checkpoint: /path/to/the/fan_hybrid_base.pth\n    prefix: backbone\n  backbone:\n    type: fan_base_16_p4_hybrid\n    custom_args:\n      use_rel_pos_bias: True\n  head:\n    type: TAOLinearClsHead\n

Instructions to Deploy These Models with DeepStream

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

Limitations

FAN was trained on the NVImageNet dataset with 1000 object categories. Hence the model may not perform well on different data distributions, so we recommend conducting further finetuning on the target domain to get higher accuracy.

Model Versions

fan_small_hybrid_nvimagenet - NVImageNet pre-trained FAN-Hybrid-Small model for finetune. (224 resolution)
fan_base_hybrid_nvimagenet - NVImageNet pre-trained FAN-Hybrid-Base model for finetune. (224 resolution)
fan_large_hybrid_nvimagenet - NVImageNet pre-trained FAN-Hybrid-Large model for finetune. (224 resolution)

Reference

Citations

Zhou, Daquan, et al. \"Understanding the robustness in vision transformers.\" International Conference on Machine Learning. PMLR, 2022.

Using TAO Pre-trained Models

Get TAO Container
Get other purpose-built models from the NGC model registry:\n
- TrafficCamNet
- PeopleNet
- PeopleNet
- PeopleNet-Transformer
- DashCamNet
- FaceDetectIR
- VehicleMakeNet
- VehicleTypeNet
- PeopleSegNet
- PeopleSemSegNet
- License Plate Detection
- License Plate Recognition
- Gaze Estimation
- Facial Landmark
- Heart Rate Estimation
- Gesture Recognition
- Emotion Recognition
- FaceDetect
- 2D Body Pose Estimation
- ActionRecognitionNet
- ActionRecognitionNet
- PoseClassificationNet
- People ReIdentification
- PointPillarNet
- CitySegFormer
- Retail Object Detection
- Retail Object Embedding
- Optical Inspection
- Optical Character Detection
- Optical Character Recognition
- PCB Classification
- PeopleSemSegFormer
- LPDNet
- License Plate Recognition
- Gaze Estimation
- Facial Landmark
- Heart Rate Estimation
- Gesture Recognition
- Emotion Recognition
- FaceDetect
- 2D Body Pose Estimation
- ActionRecognitionNet
- ActionRecognitionNet
- PoseClassificationNet
- People ReIdentification
- PointPillarNet
- CitySegFormer
- Retail Object Detection
- Retail Object Embedding
- Optical Inspection
- Optical Character Detection
- Optical Character Recognition
- PCB Classification
- PeopleSemSegFormer
\n

License

License to use these models is covered by the NVIDIA Open Model License. By downloading the model, you accept the terms and conditions of these licenses.

Technical blogs

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

Ethical Considerations

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 developers to ensure that it meets the requirements for the relevant industry and use case, that the necessary instructions 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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TAO Toolkit adapts popular network architectures and backbones to your data, allowing you to train, fine tune, prune, and export highly optimized and accurate AI models for edge deployment.\n\nThe pre-trained models accelerate the AI training process and reduce costs associated with large scale data collection, labeling, and training models from scratch. Transfer learning with pre-trained models can be used for AI applications in smart cities, retail, healthcare, industrial inspection and more.\n\nBuild end-to-end services and solutions for transforming pixels and sensor data to actionable insights using the TAO [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) and [TensorRT](https://developer.nvidia.com/tensorrt). These models are suitable for object detection, classification, and segmentation.\n\n## Model Overview \n\nImage Classification is a popular computer vision technique in which an image is classified into one of the designated classes based on the image features. This model card contains pretrained weights of most of the popular classification models. These weights that may be used as a starting point with the classification app in Train Adapt Optimize (TAO) Toolkit to facilitate transfer learning.\n\n## Model Architecture \n\nFAN (Fully Attentional Network) is a transformer-based family of backbone from NVIDIA research that achieves SOTA in robustness against various corruptions. This family of backbone can easily generalize to new domains, be more robust to noise, blur etc. Key design behind FAN block is the attentional channel processing module that leads to robust representation learning. FAN can be used for image classification tasks as well as downstream tasks such as object detection and segmentation. Use FAN when your testing dataset has a domain gap from the training dataset.\n\n## Training \n\nThis model was trained using the `classification_pyt` entrypoint in TAO. The training algorithm optimizes the network to minimize the cross-entropy loss.\n\n### Training Data \n\nThe FAN models were trained on the NVImageNet dataset.\n\n## Performance\n\n### Evaluation Data \n\nWe have tested the FAN models on the ImageNet1K validation dataset.\n\n### Methodology and KPI\n\nThe key performance indicator is the accuracy, following the standard evaluation protocol for image classification. The KPI for the evaluation data are reported below. \n\n| model | top-1 accuracy |\n|-------------------------------|----------------|\n| fan_large_hybrid_nvimagenet | 69.1 |\n| fan_base_hybrid_nvimagenet | 68.3 |\n| fan_small_hybrid_nvimagenet | 68.38 |\n\n### Real-time Inference Performance \n\nThe inference is run on the provided unpruned model at FP16 precision. The inference performance is run using [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/master/samples/trtexec) on Jetson AGX Xavier, Xavier NX, Orin, Orin NX and NVIDIA T4, and Ampere GPUs. 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\nFAN-B-H-384 (384 resolution)\n\n| Platform | BS | FPS |\n|------------------|----|------|\n| Jetson Orin Nano | 4 | 16 |\n| Orin NX 16GB | 4 | 23.4 |\n| AGX Orin 64GB | 8 | 61.2 |\n| A2 | 8 | 55.5 |\n| T4 | 8 | 91 |\n| A30 | 16 | 260 |\n| L4 | 4 | 207 |\n| L40 | 4 | 558 |\n| A100 | 64 | 577 |\n| H100 | 64 | 985 |\n\nFAN-L-H-384\n\n| Platform | BS | FPS |\n|------------------|----|-----|\n| Jetson Orin Nano | - | - |\n| Orin NX 16GB | - | - |\n| AGX Orin 64GB | - | - |\n| A2 | 8 | 38 |\n| T4 | 4 | 62 |\n| A30 | 8 | 179 |\n| L4 | 4 | 145 |\n| L40 | 4 | 366 |\n| A100 | 64 | 402 |\n| H100 | 64 | 681 |\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 for these models is classifying objects in a color (RGB) image. The model can be used to classify objects from photos and videos by using appropriate video or image decoding and pre-processing.\n\nThese models are intended for training and fine-tune using TAO Toolkit and user datasets for image classification. High-fidelity models can be trained to the new use cases. A Jupyter notebook is available as a part of the [TAO container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit-pytorch) and can be used to re-train.\n\nThe models are also intended for easy edge deployment using DeepStream SDK or TensorRT. DeepStream provides the facilities to create efficient video analytic pipelines to capture, decode, and pre-process the data before running inference.\n\n### Input\n\n* B X 3 X 224 X 224 (B C H W)\n* B X 3 X 384 X 384 (B C H W)\n\n### Output\n\nCategory labels (1000 classes) for the input image.\n\n### Instructions to Use Pretrained Models with TAO\n\nTo use these models as pretrained weights for transfer learning, please use the snippet below as template for the `model` and `train` component of the experiment spec file to train a FAN Classification model. For more information on the experiment spec file, please refer to the [TAO Toolkit User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\n```yaml\nmodel:\n init_cfg:\n checkpoint: /path/to/the/fan_hybrid_base.pth\n prefix: backbone\n backbone:\n type: fan_base_16_p4_hybrid\n custom_args:\n use_rel_pos_bias: True\n head:\n type: TAOLinearClsHead\n```\n\n### Instructions to Deploy These Models with DeepStream\n\nDocumentation to deploy with DeepStream is provided in \"Deploying to DeepStream\" chapter of [TAO User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\n## Limitations\n\nFAN was trained on the NVImageNet dataset with 1000 object categories. Hence the model may not perform well on different data distributions, so we recommend conducting further finetuning on the target domain to get higher accuracy.\n\n## Model Versions\n\n- **fan_small_hybrid_nvimagenet** - NVImageNet pre-trained FAN-Hybrid-Small model for finetune. (224 resolution)\n- **fan_base_hybrid_nvimagenet** - NVImageNet pre-trained FAN-Hybrid-Base model for finetune. (224 resolution)\n- **fan_large_hybrid_nvimagenet** - NVImageNet pre-trained FAN-Hybrid-Large model for finetune. (224 resolution)\n\n\n## Reference\n\n### Citations \n\n- Zhou, Daquan, et al. \"Understanding the robustness in vision transformers.\" International Conference on Machine Learning. PMLR, 2022.\n\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 the NGC model registry:\n - [TrafficCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:trafficcamnet)\n - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)\n - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)\n - [PeopleNet-Transformer](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet_transformer)\n - [DashCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:dashcamnet)\n - [FaceDetectIR](https://ngc.nvidia.com/catalog/models/nvidia:tao:facedetectir)\n - [VehicleMakeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehiclemakenet)\n - [VehicleTypeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehicletypenet)\n - [PeopleSegNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplesegnet)\n - [PeopleSemSegNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplesemsegnet)\n - [License Plate Detection](https://ngc.nvidia.com/catalog/models/nvidia:tao:lpdnet)\n - [License Plate Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:lprnet)\n - [Gaze Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:gazenet)\n - [Facial Landmark](https://ngc.nvidia.com/catalog/models/nvidia:tao:fpenet)\n - [Heart Rate Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:heartratenet)\n - [Gesture Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:gesturenet)\n - [Emotion Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:emotionnet)\n - [FaceDetect](https://ngc.nvidia.com/catalog/models/nvidia:tao:facenet)\n - [2D Body Pose Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:bodyposenet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [PoseClassificationNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:poseclassificationnet)\n - [People ReIdentification](https://ngc.nvidia.com/catalog/models/nvidia:tao:reidentificationnet)\n - [PointPillarNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pointpillarnet)\n - [CitySegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/citysemsegformer)\n - [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection)\n - [Retail Object Embedding](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_recognition)\n - [Optical Inspection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/optical_inspection)\n - [Optical Character Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocdnet)\n - [Optical Character Recognition](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocrnet)\n - [PCB Classification](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pcb_classification)\n - [PeopleSemSegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplesemsegformer)\n - [LPDNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:lpdnet)\n - [License Plate Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:lprnet)\n - [Gaze Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:gazenet)\n - [Facial Landmark](https://ngc.nvidia.com/catalog/models/nvidia:tao:fpenet)\n - [Heart Rate Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:heartratenet)\n - [Gesture Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:gesturenet)\n - [Emotion Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:emotionnet)\n - [FaceDetect](https://ngc.nvidia.com/catalog/models/nvidia:tao:facenet)\n - [2D Body Pose Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:bodyposenet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [PoseClassificationNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:poseclassificationnet)\n - [People ReIdentification](https://ngc.nvidia.com/catalog/models/nvidia:tao:reidentificationnet)\n - [PointPillarNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pointpillarnet)\n - [CitySegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/citysemsegformer)\n - [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection)\n - [Retail Object Embedding](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_recognition)\n - [Optical Inspection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/optical_inspection)\n - [Optical Character Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocdnet)\n - [Optical Character Recognition](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocrnet)\n - [PCB Classification](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pcb_classification)\n - [PeopleSemSegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplesemsegformer)\n\n## License \n\nLicense to use these models is covered by the NVIDIA Open Model License. By downloading the model, you accept the terms and conditions of these [licenses](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\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- Refer to the [TAO Toolkit documentation](https://docs.nvidia.com/tao/tao-toolkit/index.html)\n- Read the [TAO Toolkit Quick Start Guide](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html) 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 the [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 applications using the [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk)\n- Deploy your models in [Riva](https://developer.nvidia.com/riva) for ConvAI use cases.\n\n## Ethical Considerations \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. 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