GestureNet Model Card

Model Overview

The model described in this card is a classification network, which aims to classify hand crop images into 5 gesture types:

thumbs up
fist
stop
ok
two
random

GestureNet is cascaded with hand detect or a bodypose network. For example, BodyPoseNet detects human body joints which are used to create hand crops and GestureNet acts as a classifier determining the gesture of the hand.

Model Architecture

This is a classification model with a Resnet-18 backbone.

Training Algorithm

This model was trained using the GestureNet entrypoint in TAO. The training algorithm optimizes the network to minimize the categorical cross entropy loss for the classes.

Training Data

GestureNet was trained on a proprietary dataset with more than 150K images. The training dataset contains hand crops from user facing camera at various heights with variations in user, backgrounds and illumination.

Performance

Evaluation Dataset

The inference performance of the GestureNet model was measured against 10000 proprietary images across a variety of environments, backgrounds and illumination.

Methodology and KPI

The KPI for the evaluation data are reported in the table below. Model is evaluated based on precision, recall and f1 score.

\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		GestureNet
Content	Precision	Recall	F1-Score
Evaluation set	85%	85%	85%

Real-time Inference Performance

The inference uses FP16 precision. The inference performance runs with trtexec on Jetson Nano, AGX Xavier, Xavier NX and NVIDIA T4 GPU. The Jetson devices run at Max-N configuration for maximum system performance. The end-to-end performance with streaming video data might slightly vary depending on use cases of applications.

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

Device	Precision	Batch_size	FPS
Nano	FP16	1	79
NX	FP16	1	111
Xavier	FP16	1	481
T4	FP16	1	1227

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 6.0 or TensorRT.

There are two flavors of the model:

trainable
deployable

The trainable 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.\nThe deployable model is intended for efficient deployment on the edge using DeepStream or TensorRT.\nThe trainable and deployable models are encrypted and will only operate with the following key:

Model load key: nvidia_tlt

Please make sure to use this as the key for all TAO commands that require a model load key.

Input

RGB Images of 160 X 160 X 3

Output

Gesture category labels.

Instructions to deploy this model with DeepStream

To create the entire end-to-end video analytic application, deploy this model with DeepStream.

Limitations

Non-frontal view

The GestureNet model is designed to classify hand gestures from a camera facing the subject.

Complex background

The GestureNet model is designed to classify hand gestures of subjects inside rooms with mosty monochromatic background behind the hand.

Dark-lighting, Monochrome or Infrared Camera Images

The GestureNet model was trained on RGB images in good lighting conditions. Therefore, images captured in dark lighting conditions or a monochrome image or IR camera image may not provide good detection results.

Model versions:

trainable_v1.0 - Pre-trained model that is intended for training.
deployabale_v1.0 - Deployment models that is intended to run on the inference pipeline.
deployabale_v2.0 - Deployment models that is intended to run on the inference pipeline with int8 calibration. The calibration file is generated for TensorRT 7.
deployabale_v2.0.1 - Deployment models that is intended to run on the inference pipeline with int8 calibration. The calibration file is generated for TensorRT 8.

References

Citations

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)

Using TAO Pre-trained Models

Get TAO Container
Get other Purpose-built models from NGC model registry:\n
- FaceNet
- FPENet
- GazeNet
- EmotionNet
- GestureNet
- HeartRateNet
- PeopleNet
- TrafficCamNet
- DashCamNet
- VehicleMakeNet
- VehicleTypeNet
\n

Technical blogs

Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - Part 1 | Part 2
Read the technical tutorial on how PeopleNet model can be trained with custom data using Transfer Learning Toolkit

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 Consideration

NVIDIA GestureNet model classifies the type of the gesture from a given crop. Training and evaluation dataset mostly consists of users from South Asia. An ideal training and evaluation dataset would additionally include users from other ethnicities.

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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For example, BodyPoseNet detects human body joints which are used to create hand crops and GestureNet acts as a classifier determining the gesture of the hand.\n\n### Model Architecture \n\nThis is a classification model with a Resnet-18 backbone.\n\n### Training Algorithm \n\nThis model was trained using the GestureNet entrypoint in TAO. The training algorithm optimizes the network to minimize the categorical cross entropy loss for the classes.\n\n#### Training Data \n\nGestureNet was trained on a proprietary dataset with more than 150K images. The training dataset contains hand crops from user facing camera at various heights with variations in user, backgrounds and illumination.\n\n### Performance\n\n#### Evaluation Dataset \n\nThe inference performance of the GestureNet model was measured against 10000 proprietary images across a variety of environments, backgrounds and illumination.\n\n#### Methodology and KPI\n\nThe KPI for the evaluation data are reported in the table below. Model is evaluated based on precision, recall and f1 score.\n\n| Model | | GestureNet | |\n|---|---|---|---|\n| Content | Precision | Recall | F1-Score |\n|Evaluation set| 85% | 85% | 85% |\n\n#### Real-time Inference Performance \n\nThe inference uses FP16 precision. The inference performance runs with [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/master/samples/opensource/trtexec) on Jetson Nano, AGX Xavier, Xavier NX and NVIDIA T4 GPU. The Jetson devices run at Max-N configuration for maximum system performance. The end-to-end performance with streaming video data might slightly vary depending on use cases of applications.\n\n| Device | Precision | Batch_size | FPS |\n| ---- | ---- | ---- | --- |\n| Nano | FP16 |1| 79 |\n| NX | FP16 | 1 | 111 |\n| Xavier | FP16 | 1 | 481 |\n| T4 | FP16 | 1 | 1227 |\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 6.0](https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps) or [TensorRT](https://developer.nvidia.com/tensorrt).\n\nThere are two flavors of the model:\n\n- trainable\n- deployable\n\nThe `trainable` 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.\nThe `deployable` model is intended for efficient deployment on the edge using DeepStream or TensorRT.\nThe `trainable` and `deployable` models are encrypted and will only operate with the following key:\n\n- Model load key: `nvidia_tlt`\n\nPlease make sure to use this as the key for all TAO commands that require a model load key.\n\n#### Input\n\nRGB Images of 160 X 160 X 3\n\n#### Output\n\nGesture category labels.\n\n#### Instructions to deploy this model with DeepStream \n\nTo create the entire end-to-end video analytic application, deploy this model with [DeepStream](https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps).\n\n### Limitations \n\n#### Non-frontal view\n\nThe GestureNet model is designed to classify hand gestures from a camera facing the subject.\n\n#### Complex background\n\nThe GestureNet model is designed to classify hand gestures of subjects inside rooms with mosty monochromatic background behind the hand.\n\n#### Dark-lighting, Monochrome or Infrared Camera Images\n\nThe GestureNet model was trained on RGB images in good lighting conditions. Therefore, images captured in dark lighting conditions or a monochrome image or IR camera image may not provide good detection results.\n\n### Model versions:\n\n- **trainable_v1.0** - Pre-trained model that is intended for training.\n- **deployabale_v1.0** - Deployment models that is intended to run on the inference pipeline.\n- **deployabale_v2.0** - Deployment models that is intended to run on the inference pipeline with int8 calibration. The calibration file is generated for TensorRT 7.\n- **deployabale_v2.0.1** - Deployment models that is intended to run on the inference pipeline with int8 calibration. The calibration file is generated for TensorRT 8.\n\n### References \n\n#### Citations \n\n- He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)\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 - [FaceNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:facenet)\n - [FPENet](https://ngc.nvidia.com/catalog/models/nvidia:tao:fpenet)\n - [GazeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:gazenet)\n - [EmotionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:emotionnet)\n - [GestureNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:gesturenet)\n - [HeartRateNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:heartratenet)\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- 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- 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\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) 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://developer.nvidia.com/deep-learning-models-license-agreement).\n\n### Ethical Consideration \n\nNVIDIA GestureNet model classifies the type of the gesture from a given crop. Training and evaluation dataset mostly consists of users from South Asia. An ideal training and evaluation dataset would additionally include users from other ethnicities.\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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It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[461],{"_78":462,"_428":459,"_429":32,"_173":463},"sp","Testing","google.com",[466],{"_78":467,"_428":464,"_429":32,"_173":468},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","tags.srv.stackadapt.com",[471,472,473,474],{"_78":479,"_428":469,"_429":32,"_173":476},{"_78":478,"_428":469,"_429":32,"_173":476},{"_78":477,"_428":469,"_429":32,"_173":476},{"_78":475,"_428":469,"_429":32,"_173":476},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[482],{"_78":483,"_428":480,"_429":32,"_173":484},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. For this reason it is classified as a primarily tracking/targeting domain.","twitter.com",[487,488,489,490],{"_78":496,"_428":485,"_429":32,"_173":492},{"_78":494,"_428":485,"_429":32,"_173":495},{"_78":493,"_428":485,"_429":32,"_173":492},{"_78":491,"_428":485,"_429":32,"_173":492},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[499,500,501,502],{"_78":508,"_428":497,"_429":32,"_173":506},{"_78":507,"_428":497,"_429":32,"_173":506},{"_78":505,"_428":497,"_429":32,"_173":506},{"_78":503,"_428":497,"_429":32,"_173":504},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","YouTube is a Google owned platform for hosting and sharing videos. 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675],[620,621,622,623,624],{"_422":514,"_225":514,"_173":174,"_396":646},{"_422":480,"_225":480,"_173":174,"_396":641},{"_422":634,"_225":634,"_173":174,"_396":635},{"_422":630,"_225":630,"_173":174,"_396":631},{"_422":625,"_225":625,"_173":174,"_396":626},"nvcrm.my.site.com",[627],{"_78":628,"_428":625,"_429":32,"_173":629},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[632],{"_78":633,"_428":630,"_429":32,"_173":463},"edge","github.com",[636,637],{"_78":640,"_428":634,"_429":32,"_173":639},{"_78":638,"_428":634,"_429":32,"_173":639},"logged_in","This domain is owned by Github. 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We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv.modal.cuda","This cookie name suggests it is related to a modal popup functionality on NVIDIA's website, possibly to remember user preferences or interactions with the modal.","readme_library","docs.api.nvidia.com","_im_ses.1006927","geo_locator","nv.modal.deepstream","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","_im_id.1006927","_hjUserAttributesHash","docs.nvidia.com","This cookie name is associated with Hotjar which is used to know when any user attribute has changed and needs to be updated.","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","nv.modal.cudnn","This cookie name suggests it is related to modal popup handling or user interface preferences on NVIDIA's website, specifically for the cuDNN library section.","readme_language","nv.modal.hpc","This cookie name suggests it is related to a modal pop-up on a website, possibly to control the display of a high-priority cookie notice or other important information to the user.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","_biz_uid","This cookie 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.\n","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","_biz_nA","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website 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Name	Size	Updated	Actions
int8_calibration.txt	1.61 KB	March 1, 2025 UTC	curl wget Download
model.etlt	43.99 MB	March 1, 2025 UTC	curl wget Download