PeopleSemSegFormer Model Card

Model Overview

The model described in this card segments one or more “person” object within an image and returns a semantic segmentation mask for all people within an image.

Model Architecture

Segformer is a real-time state of the art transformer based semantic segmentation model. SegFormer is a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders. It then predicts a class label for every pixel in the input image. This model segments the person and the background.

Training Algorithm

The training algorithm optimizes the network to minimize the cross-entropy loss for every pixel of the mask.

Training Data

PeopleSemSegFormer v1.0 model was trained on a proprietary dataset with more than 7.6 million images and more than 71 million objects for person class. The training dataset consists of a mix of camera heights, crowd-density, and field-of view (FOV). Approximately half of the training data consisted of images captured in an indoor office environment. For this case, the camera is typically set up at approximately 10 feet height, 45-degree angle and has close field-of-view. This content was chosen to improve accuracy of the models for convenience-store retail analytics use-case. We have also added approximately 500 thousand images with low-density scenes with people extending their hands and feet to improve the performance for use-cases where person object detection is followed by pose-estimation. This dataset included about 200k of \"Low Contrast\" images, where the people and their clothing blend into the background.

\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

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	Training	Dataset	Object	Distribution
Category	Number of Images	Number of Persons	Number of Bags	Number of Faces
Natural	4804552	23085430	8061920	10786381
Rotated	5746323	19930535	7234679	10094039
Broadcast	566408	566408	0	358518
Broadcast Rotated	369895	369895	0	59104
Blended	24841	26041	11225	0
Blended Rotated	24335	24523	9707	0
Simulation	27417	368914	0	92916
Total	7656570	41334979	12280764	18354191

Training Data Ground-truth Labeling Guidelines

All objects were auto-labelled using TAO-MAL AI-Assisted Annotation model from NVIDIA.

Performance

Evaluation Data

The inference performance of PeopleSemSegFormer model was measured against 300 proprietary images that were hand labelled across a variety of environments. The frames are high resolution images 1920x1080 pixels resized to 1820x1024 pixels before passing to the PeopleSemSegFormer model.

Methodology and KPI

The KPI for the evaluation data are reported in the table below. Model is evaluated based on Mean Intersection-Over-Union. Mean Intersection-Over-Union (MIOU) is a common evaluation metric for semantic image segmentation, which first computes the IOU for each semantic class and then computes the average over classes.

\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

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Model	FAN-Base-Hybrid-Segformer
Content	MIOU
5ft	91.86
10ft	91.4
20ft	89.7
Office use-case	97.01

Real-time Inference Performance

The inference is run on the provided unpruned models at INT8 precision. On the Jetson Nano FP16 precision is used. The inference performance is run using trtexec on Jetson Nano, AGX Xavier, Xavier NX and NVIDIA T4 GPU. The Jetson devices are running at Max-N configuration for maximum GPU frequency. The performance shown here is the inference only performance. The end-to-end performance with streaming video data might slightly vary depending on other bottlenecks in the hardware and software.

BS - Batch Size

\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

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Platform	BS	FPS
Jetson Orin Nano	1	6.6
Orin NX 16GB	1	9.7
AGX Orin 64GB	1	24.2
A2	1	23.3
T4	4	39.6
A30	8	116.8
L4	1	83.4
L40	2	210
A100	32	254
H100	32	454

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 model is intended for training using TAO Toolkit with the user's own dataset or using it as it is. 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.

Primary use case intended for the model is segmenting people in a color (RGB) image. The model can be used to segment people from photos and videos by using appropriate video or image decoding and pre-processing. Note this model performs semantic segmentation and not instance based segmentation.

Input

Color Images of resolution 512x512x3

Output

Category label (person or background) for every pixel in the input image. Outputs a semantic of people for the input image.

Output image

Instructions to deploy these models with DeepStream

To create the entire end-to-end video analytics application, deploy these models with DeepStream SDK. DeepStream SDK is a streaming analytics toolkit to accelerate building AI-based video analytics applications. DeepStream supports direct integration of these models into the deepstream sample app.

To deploy these models with DeepStream 6.1, please follow the instructions below:

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:

/opt/nvidia/deepstream is the default DeepStream installation directory. This path will be different if you are installing in a different directory.

You will need 1 config files and 1 label file. These files are provided in [NVIDIA-AI-IOT](@todo : update).

nvinfer_config.txt - File to configure inference settings for PeopleSemSegFormer\nlabels.txt - Label file with 2 classes\n

Key Parameters in nvinfer_config.txt

# You can either provide the onnx model and key or trt engine obtained by using tao-converter\n# model-engine-file=../../path/to/trt_engine\nonnx-file=/path/to/onnx-file # Provide path to onnx model\nnet-scale-factor=0.007843\noffsets=127.5;127.5;127.5\n# Since the model input channel is 3, using RGB color format.\nmodel-color-format=0\nlabelfile-path=./labels.txt\ninfer-dims=3;512;512 # Replace this with the input dimensions of your image \nbatch-size=1\n## 0=FP32, 1=INT8, 2=FP16 mode\nnetwork-mode=2\ninterval=0\ngie-unique-id=1\ncluster-mode=2\n## 0=Detector, 1=Classifier, 2=Semantic Segmentation, 3=Instance Segmentation, 100=Other\nnetwork-type=100\noutput-tensor-meta=1\nnum-detected-classes=20\nsegmentation-output-order=1\nroi-top-offset=0\nroi-bottom-offset=0\ndetected-min-w=0\ndetected-min-h=0\ndetected-max-w=0\ndetected-max-h=0\n

Run ds-tao-segmentation:

PeopleSemSegFormer

ds-tao-segmentation -c configs/segformer_tao/nvinfer_config.txt -i file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_720p.mp4\n

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

Limitations

Under-represented classes

NVIDIA PeopleSemSegFormer model was trained to detect classes that are predominantly found in road transport setting. It relatively performs poorly on under-represented classes in our internal Intelligent Transport System dataset. Some of these classes include: rider, truck, train, motorcycle.

Model versions:

PeopleSemSegFormer:

trainable_PeopleSemSegFormer_v1.0 - PeopleSemSegFormer model deployable to deepstream.
deployable_PeopleSemSegFormer_v1.0 - PeopleSemSegFormer model deployable to deepstream.

References

Citations

Xie, Enze, et al. \"SegFormer: Simple and efficient design for semantic segmentation with transformers.\" Advances in Neural Information Processing Systems 34 (2021): 12077-12090.
https://github.com/NVIDIA/semantic-segmentation

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

Technical blogs

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
Learn how to train Instance segmentation model using MaskRCNN with TAO
Learn how to improve INT8 accuracy using Quantization aware training(QAT) with TAO
Read the technical tutorial on how PeopleNet model can be trained with custom data using Transfer Learning Toolkit
Learn how to train and deploy real-time intelligent video analytics apps and services using DeepStream SDK

License

License to use this model 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

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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SegFormer is a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders. It then predicts a class label for every pixel in the input image. This model segments the person and the background.\n\n\n## Training Algorithm \n\nThe training algorithm optimizes the network to minimize the cross-entropy loss for every pixel of the mask.\n\n## Training Data \n\nPeopleSemSegFormer v1.0 model was trained on a proprietary dataset with more than 7.6 million images and more than 71 million objects for person class. The training dataset consists of a mix of camera heights, crowd-density, and field-of view (FOV). Approximately half of the training data consisted of images captured in an indoor office environment. For this case, the camera is typically set up at approximately 10 feet height, 45-degree angle and has close field-of-view. This content was chosen to improve accuracy of the models for convenience-store retail analytics use-case. We have also added approximately 500 thousand images with low-density scenes with people extending their hands and feet to improve the performance for use-cases where person object detection is followed by pose-estimation. This dataset included about 200k of \"Low Contrast\" images, where the people and their clothing blend into the background.\n\n| | Training | Dataset | Object | Distribution |\n| -- | -- | -- | -- | -- |\n| Category | Number of Images | Number of Persons | Number of Bags | Number of Faces |\n| Natural | 4804552 | 23085430 | 8061920 | 10786381 |\n| Rotated | 5746323 | 19930535 | 7234679 | 10094039 |\n| Broadcast | 566408 | 566408 | 0 | 358518 |\n| Broadcast Rotated | 369895 | 369895 | 0 | 59104 |\n| Blended | 24841 | 26041 | 11225 | 0 |\n| Blended Rotated | 24335 | 24523 | 9707 | 0 |\n| Simulation | 27417 | 368914 | 0 |92916 |\n| Total | 7656570 | 41334979 | 12280764 | 18354191 |\n\n### Training Data Ground-truth Labeling Guidelines\n\n- All objects were auto-labelled using TAO-MAL AI-Assisted Annotation model from NVIDIA.\n\n## Performance\n\n### Evaluation Data \n\nThe inference performance of PeopleSemSegFormer model was measured against 300 proprietary images that were hand labelled across a variety of environments. The frames are high resolution images 1920x1080 pixels resized to 1820x1024 pixels before passing to the PeopleSemSegFormer model.\n\n### Methodology and KPI\n\nThe KPI for the evaluation data are reported in the table below. Model is evaluated based on Mean Intersection-Over-Union. Mean Intersection-Over-Union (MIOU) is a common evaluation metric for semantic image segmentation, which first computes the IOU for each semantic class and then computes the average over classes.\n\n| Model | FAN-Base-Hybrid-Segformer |\n| ---- | ----- |\n| Content | MIOU |\n| 5ft | 91.86 |\n| 10ft | 91.4 |\n| 20ft | 89.7 |\n| Office use-case | 97.01 |\n\n\n### Real-time Inference Performance \n\nThe inference is run on the provided unpruned models at INT8 precision. On the Jetson Nano FP16 precision is used. The inference performance is run using [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/master/samples/trtexec) on Jetson Nano, AGX Xavier, Xavier NX and NVIDIA T4 GPU. The Jetson devices are running at Max-N configuration for maximum GPU frequency. The performance shown here is the inference only performance. The end-to-end performance with streaming video data might slightly vary depending on other bottlenecks in the hardware and software.\n\n**BS** - Batch Size\n\n| Platform | BS | FPS |\n|------------------|----|------|\n| Jetson Orin Nano | 1 | 6.6 |\n| Orin NX 16GB | 1 | 9.7 |\n| AGX Orin 64GB | 1 | 24.2 |\n| A2 | 1 | 23.3 |\n| T4 | 4 | 39.6 |\n| A30 | 8 | 116.8|\n| L4 | 1 | 83.4 |\n| L40 | 2 | 210 |\n| A100 | 32 | 254 |\n| H100 | 32 | 454 |\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, DeepStream SDK or TensorRT.\n\nThe model is intended for training using TAO Toolkit with the user's own dataset or using it as it is. 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.\n\nPrimary use case intended for the model is segmenting people in a color (RGB) image. The model can be used to segment people from photos and videos by using appropriate video or image decoding and pre-processing. Note this model performs semantic segmentation and not instance based segmentation.\n\n\n### Input\n\nColor Images of resolution 512x512x3\n\n### Output\n\nCategory label (person or background) for every pixel in the input image. Outputs a semantic of people for the input image.\n\n### Output image\n\n

\n\n### Instructions to deploy these models with DeepStream\n\nTo create the entire end-to-end video analytics application, deploy these models with [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk). DeepStream SDK is a streaming analytics toolkit to accelerate building AI-based video analytics applications. DeepStream supports direct integration of these models into the deepstream sample app.\n\nTo deploy these models with [DeepStream 6.1](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`/opt/nvidia/deepstream` is the default DeepStream installation directory. This path will be different if you are installing in a different directory.\n\nYou will need 1 config files and 1 label file. These files are provided in [NVIDIA-AI-IOT](@todo : update).\n\n```sh\nnvinfer_config.txt - File to configure inference settings for PeopleSemSegFormer\nlabels.txt - Label file with 2 classes\n```\n\nKey Parameters in `nvinfer_config.txt`\n\n```sh\n# You can either provide the onnx model and key or trt engine obtained by using tao-converter\n# model-engine-file=../../path/to/trt_engine\nonnx-file=/path/to/onnx-file # Provide path to onnx model\nnet-scale-factor=0.007843\noffsets=127.5;127.5;127.5\n# Since the model input channel is 3, using RGB color format.\nmodel-color-format=0\nlabelfile-path=./labels.txt\ninfer-dims=3;512;512 # Replace this with the input dimensions of your image \nbatch-size=1\n## 0=FP32, 1=INT8, 2=FP16 mode\nnetwork-mode=2\ninterval=0\ngie-unique-id=1\ncluster-mode=2\n## 0=Detector, 1=Classifier, 2=Semantic Segmentation, 3=Instance Segmentation, 100=Other\nnetwork-type=100\noutput-tensor-meta=1\nnum-detected-classes=20\nsegmentation-output-order=1\nroi-top-offset=0\nroi-bottom-offset=0\ndetected-min-w=0\ndetected-min-h=0\ndetected-max-w=0\ndetected-max-h=0\n```\n\nRun `ds-tao-segmentation`:\n\n#### PeopleSemSegFormer\n```sh\nds-tao-segmentation -c configs/segformer_tao/nvinfer_config.txt -i file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_720p.mp4\n```\n\nDocumentation to deploy with DeepStream is provided in \"Deploying to DeepStream\" chapter of [TAO User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\n## Limitations \n\n### Under-represented classes\n\nNVIDIA PeopleSemSegFormer model was trained to detect classes that are predominantly found in road transport setting. It relatively performs poorly on under-represented classes in our internal Intelligent Transport System dataset. Some of these classes include: rider, truck, train, motorcycle. \n\n\n### Model versions:\n\n#### PeopleSemSegFormer:\n\n- **trainable_PeopleSemSegFormer_v1.0** - PeopleSemSegFormer model deployable to deepstream.\n- **deployable_PeopleSemSegFormer_v1.0** - PeopleSemSegFormer model deployable to deepstream.\n\n## References\n\n## Citations \n\n- Xie, Enze, et al. \"SegFormer: Simple and efficient design for semantic segmentation with transformers.\" Advances in Neural Information Processing Systems 34 (2021): 12077-12090.\n- https://github.com/NVIDIA/semantic-segmentation\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### Technical blogs \n\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- Learn how to train [Instance segmentation model using MaskRCNN with TAO](https://developer.nvidia.com/blog/training-instance-segmentation-models-using-maskrcnn-on-tao-toolkit//)\n- Learn how to improve INT8 accuracy using [Quantization aware training(QAT) with TAO](https://developer.nvidia.com/blog/improving-int8-accuracy-using-quantization-aware-training-and-tao-toolkit//)\n- Read the technical tutorial on how [PeopleNet model can be trained with custom data using Transfer Learning Toolkit](https://devblogs.nvidia.com/training-custom-pretrained-models-using-tlt/)\n- Learn how to [train and deploy real-time intelligent video analytics apps and services using DeepStream SDK](https://devblogs.nvidia.com/building-iva-apps-using-deepstream-5.0/)\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- 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 model on the edge using DeepStream. Learn more about [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk)\n\n## License \n\nLicense to use this model 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\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. 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