Model Overview

\n\n

Description:

One Transformer to Rule all Segmentation Tasks or OneFormer is a universal segmentation architecture capable of addressing panoptic, instance, or semantic image segmentation tasks. The task token is essential in dynamically guiding the model to output task-specific predictions by conditioning the architecture on the desired segmentation type (e.g., \"semantic,\" \"instance,\" or \"panoptic\") during a single, unified training and inference process.

This model is ready for non-commercial use.

License/Terms of Use:

GOVERNING TERMS: Use of this model is governed by NVIDIA License. ADDITIONAL INFORMATION: MIT License.

Deployment Geography:

Global

Use Case:

Intended Users: This model is intended for use by computer vision engineers, robotics engineers, and researchers who require a comprehensive and detailed pixel-level understanding of an image.
\nIntended Use Cases: The model's ability to perform semantic, instance, and panoptic segmentation simultaneously makes it ideal for:

Autonomous Systems: Providing full scene understanding for self-driving cars or drones by identifying both \"stuff\" (road, sky) and \"things\" (car 1, car 2, pedestrian 1).
Robotic Perception: Enabling robots to identify, locate, and separate individual objects for \"pick-and-place\" tasks in cluttered environments.
Medical Image Analysis: Segmenting and counting individual cells or tumors (instances) while also classifying surrounding anatomical regions or tissues (semantic).
Geospatial Analysis: Analyzing satellite imagery to map land use (e.g., \"forest,\" \"water\") while also detecting and counting individual objects (e.g., \"buildings,\" \"vehicles\").
Computational Photography: Powering features like AR effects or portrait mode by creating precise masks that separate subjects from their background.

Release Date:

NGC 11/25/2025 via [URL]

References(s):

J. Jain, J. Li, M. Chiu, A. Hassani, N. Orlov, H. Shi: OneFormer: One Transformer to Rule Universal Image Segmentation

Model Architecture:

Architecture Type: The model is a unified segmentor that takes color (RGB) images as inputs and generates segmentation masks and associated labels as outputs.

Network Architecture:

The backbone feature extractor of this model is the Swin-L model pretrained on the ImageNet dataset.
The multi-scale features from the backbone are then fed into a Pixel Decoder (similar to an FPN) to generate high-resolution, multi-scale feature maps.
The core of the architecture is a transformer decoder that takes two sets of inputs: the multi-scale feature maps and a set of learnable queries.
A key innovation of OneFormer is the use of a task token. This single, learnable token is added to the queries to \"prompt\" the model, conditioning it to perform a specific task (semantic, instance, or panoptic segmentation) using the exact same weights.
Finally, the refined query embeddings from the decoder are passed to two parallel prediction heads:
A classification head (a linear layer or small MLP) to predict the class label for each query.
A mask head (also an MLP) to dynamically generate the final mask for each query by combining the decoder outputs with the pixel decoder's feature maps.

Number of Model Parameters: 223M*10^7

Input(s):

Input Type(s): Image, Text
\nInput Format(s):

Image: Red, Green, Blue (RGB)
Text: String

Input Parameters:

Image: Two-Dimensional (2D)
Text: One-Dimensional (1D)

Other Properties Related to Input: The image size should be divisible by 32, and the text should state, \"This task is semantic/instance/panoptic\".

Output(s)

Output Type(s): Label, Mask and Score for each detected object in the input image.

Output Format(s):

Label: Integer
Mask: Red, Green, Blue (RGB)
Score: Float

Output Parameters:

Label: One-Dimensional (1D)
Mask: Two-Dimensional (2D)
Score: One-Dimensional (1D)

Other Properties Related to Output:

pred_classes: Batch size x Number of queries
pred_masks: Batch size x Number of queries x Height x Width
pred_scores: Batch size x Number of queries

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

Software Integration:

Runtime Engine(s):

TAO v6.25.11

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Blackwell
NVIDIA Jetson
NVIDIA Hopper
NVIDIA Lovelace
NVIDIA Pascal
NVIDIA Turing
NVIDIA Volta

Preferred/Supported Operating System(s):

Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s):

v1.0

Training, Testing, and Evaluation Datasets:

Dataset Overview

Total Number of Datasets: 02 Datasets (COCO 2017 and ADE20K)
Data Modality: Image

Images are scaled, typically by resizing the shorter edge to a fixed size (e.g., 800 pixels) while maintaining the aspect ratio. From these scaled images, large random crops (e.g., 1024x1024) are taken to create training patches. Standard augmentations, including random horizontal flipping (with 50% probability) and photometric jitter (adjusting brightness, contrast, and saturation), are applied to improve model robustness.

COCO

Link: https://cocodataset.org/#home
\nData Collection Method by dataset:\nAutomated
\nLabeling Method by dataset:\nHuman
\nProperties:\nThe COCO dataset (specifically the 2017 version) is the gold-standard benchmark for panoptic segmentation. It is designed to test a model's ability to unify the tasks of instance segmentation (for \"things\") and semantic segmentation (for \"stuff\").

Training Set: 118,000 images (train2017)
Validation Set: 5,000 images (val2017)
Total Classes: 133 total categories\n
- 80 \"Thing\" Categories (for instance segmentation, e.g., 'person', 'dog', 'stop sign')
- 53 \"Stuff\" Categories (for semantic segmentation, e.g., 'sky', 'grass', 'road')
\n
Total Annotations: The dataset is incredibly dense, containing over 1.5 million annotated object instances across its training and validation splits.

ADE20K

Link: https://ade20k.csail.mit.edu/
\nData Collection Method by dataset:\nAutomated
\nLabeling Method by dataset:\nHuman
\nProperties: The ADE20K (MIT SceneParsing Benchmark) dataset is a densely annotated benchmark designed for scene parsing. This task requires the model to label every single pixel in an image with a semantic category, including a vast array of objects, parts of objects, and background \"stuff.\"

Training Set: 20,210 images
Validation Set: 2,000 images
Semantic Classes: The standard benchmark (used by OneFormer) consists of 150 semantic categories (e.g., 'person', 'car', 'building', 'road', 'tree').
Total Annotations: The full dataset contains over 700,000 unique object instances.

Inference:

Acceleration Engine: Tensor(RT)
\nTest Hardware:

1x NVIDIA A100 80GB
1x NVIDIA H100 80GB

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

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\n\n### Deployment Geography:\nGlobal\n\n### Use Case:
\n**Intended Users:** This model is intended for use by computer vision engineers, robotics engineers, and researchers who require a comprehensive and detailed pixel-level understanding of an image.
\n**Intended Use Cases:** The model's ability to perform semantic, instance, and panoptic segmentation simultaneously makes it ideal for:\n - Autonomous Systems: Providing full scene understanding for self-driving cars or drones by identifying both \"stuff\" (road, sky) and \"things\" (car 1, car 2, pedestrian 1).\n - Robotic Perception: Enabling robots to identify, locate, and separate individual objects for \"pick-and-place\" tasks in cluttered environments.\n - Medical Image Analysis: Segmenting and counting individual cells or tumors (instances) while also classifying surrounding anatomical regions or tissues (semantic).\n - Geospatial Analysis: Analyzing satellite imagery to map land use (e.g., \"forest,\" \"water\") while also detecting and counting individual objects (e.g., \"buildings,\" \"vehicles\").\n - Computational Photography: Powering features like AR effects or portrait mode by creating precise masks that separate subjects from their background.
\n\n### Release Date:
\nNGC 11/25/2025 via [[URL](https://registry.ngc.nvidia.com/orgs/nvstaging/teams/tao/models/oneformer/version)]
\n\n## References(s):\nJ. Jain, J. Li, M. Chiu, A. Hassani, N. Orlov, H. Shi: OneFormer: One Transformer to Rule Universal Image Segmentation\n\n## Model Architecture:\n**Architecture Type:** The model is a unified segmentor that takes color (RGB) images as inputs and generates segmentation masks and associated labels as outputs. \n\n**Network Architecture:** \n- The backbone feature extractor of this model is the Swin-L model pretrained on the ImageNet dataset.\n- The multi-scale features from the backbone are then fed into a Pixel Decoder (similar to an FPN) to generate high-resolution, multi-scale feature maps.\n- The core of the architecture is a transformer decoder that takes two sets of inputs: the multi-scale feature maps and a set of learnable queries.\n- A key innovation of OneFormer is the use of a task token. This single, learnable token is added to the queries to \"prompt\" the model, conditioning it to perform a specific task (semantic, instance, or panoptic segmentation) using the exact same weights.\n- Finally, the refined query embeddings from the decoder are passed to two parallel prediction heads:\n- A classification head (a linear layer or small MLP) to predict the class label for each query.\n- A mask head (also an MLP) to dynamically generate the final mask for each query by combining the decoder outputs with the pixel decoder's feature maps.\n\n**Number of Model Parameters:** 223M*10^7\n\n## Input(s):
\n**Input Type(s):** Image, Text
\n**Input Format(s):** \n- Image: Red, Green, Blue (RGB)
\n- Text: String
\n\n**Input Parameters:** \n- Image: Two-Dimensional (2D)
\n- Text: One-Dimensional (1D)
\n\n**Other Properties Related to Input:** The image size should be divisible by 32, and the text should state, \"This task is semantic/instance/panoptic\". \n\n## Output(s)\n\n**Output Type(s):** Label, Mask and Score for each detected object in the input image.
\n\n**Output Format(s):**
\n- Label: Integer
\n- Mask: Red, Green, Blue (RGB)
\n- Score: Float
\n\n**Output Parameters:**\n- Label: One-Dimensional (1D)
\n- Mask: Two-Dimensional (2D)
\n- Score: One-Dimensional (1D)
\n\n**Other Properties Related to Output:** \n - `pred_classes`: Batch size x Number of queries\n - `pred_masks`: Batch size x Number of queries x Height x Width\n - `pred_scores`: Batch size x Number of queries \n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n## Software Integration:\n**Runtime Engine(s):** \n* TAO v6.25.11
\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Ampere\n* NVIDIA Blackwell\n* NVIDIA Jetson\n* NVIDIA Hopper\n* NVIDIA Lovelace\n* NVIDIA Pascal\n* NVIDIA Turing\n* NVIDIA Volta\n\n**Preferred/Supported Operating System(s):**\n* Linux\n\nThe integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.\n## Model Version(s):\nv1.0\n\n## Training, Testing, and Evaluation Datasets:\n\n### Dataset Overview \n- Total Number of Datasets: 02 Datasets (COCO 2017 and ADE20K)\n- Data Modality: Image
\n\nImages are scaled, typically by resizing the shorter edge to a fixed size (e.g., 800 pixels) while maintaining the aspect ratio. From these scaled images, large random crops (e.g., 1024x1024) are taken to create training patches. Standard augmentations, including random horizontal flipping (with 50% probability) and photometric jitter (adjusting brightness, contrast, and saturation), are applied to improve model robustness.\n### COCO\n**Link:** https://cocodataset.org/#home
\n**Data Collection Method by dataset:**\nAutomated
\n**Labeling Method by dataset:**\nHuman
\n**Properties:**\nThe COCO dataset (specifically the 2017 version) is the gold-standard benchmark for panoptic segmentation. It is designed to test a model's ability to unify the tasks of instance segmentation (for \"things\") and semantic segmentation (for \"stuff\").
\n - Training Set: 118,000 images (train2017)\n - Validation Set: 5,000 images (val2017)\n - Total Classes: 133 total categories\n - 80 \"Thing\" Categories (for instance segmentation, e.g., 'person', 'dog', 'stop sign')\n - 53 \"Stuff\" Categories (for semantic segmentation, e.g., 'sky', 'grass', 'road')\n - Total Annotations: The dataset is incredibly dense, containing over 1.5 million annotated object instances across its training and validation splits.
\n \n\n### ADE20K\n**Link:** https://ade20k.csail.mit.edu/
\n**Data Collection Method by dataset:**\nAutomated
\n**Labeling Method by dataset:**\nHuman
\n**Properties:** The ADE20K (MIT SceneParsing Benchmark) dataset is a densely annotated benchmark designed for scene parsing. This task requires the model to label every single pixel in an image with a semantic category, including a vast array of objects, parts of objects, and background \"stuff.\"
\n - Training Set: 20,210 images\n - Validation Set: 2,000 images\n - Semantic Classes: The standard benchmark (used by OneFormer) consists of 150 semantic categories (e.g., 'person', 'car', 'building', 'road', 'tree').\n - Total Annotations: The full dataset contains over 700,000 unique object instances.
\n\n\n# Inference:\n**Acceleration Engine:** Tensor(RT)
\n**Test Hardware:**
\n* 1x NVIDIA A100 80GB
\n* 1x NVIDIA H100 80GB
\n\n## Ethical Considerations:\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
\n\n\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
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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",[443],{"_77":444,"_410":441,"_411":31,"_167":445},"sp","Testing","google.com",[448],{"_77":449,"_410":446,"_411":31,"_167":450},"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",[453,454,455,456],{"_77":461,"_410":451,"_411":31,"_167":458},{"_77":460,"_410":451,"_411":31,"_167":458},{"_77":459,"_410":451,"_411":31,"_167":458},{"_77":457,"_410":451,"_411":31,"_167":458},"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",[464],{"_77":465,"_410":462,"_411":31,"_167":466},"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",[469,470,471,472],{"_77":478,"_410":467,"_411":31,"_167":474},{"_77":476,"_410":467,"_411":31,"_167":477},{"_77":475,"_410":467,"_411":31,"_167":474},{"_77":473,"_410":467,"_411":31,"_167":474},"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",[481,482,483,484],{"_77":490,"_410":479,"_411":31,"_167":488},{"_77":489,"_410":479,"_411":31,"_167":488},{"_77":487,"_410":479,"_411":31,"_167":488},{"_77":485,"_410":479,"_411":31,"_167":486},"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. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[493],{"_77":494,"_410":491,"_411":31,"_167":495},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[498],{"_77":499,"_410":496,"_411":31,"_167":500},"receive-cookie-deprecation","This domain is owned by im-apps.net which provides information and resources about various instant messaging (IM) applications to help users make informed decisions based on their communication needs.","influ2.com",[503],{"_77":504,"_410":501,"_411":31,"_167":445},"R","6sc.co",[507],{"_77":508,"_410":505,"_411":31,"_167":509},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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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.",[636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656],[601,602,603,604,605],{"_404":496,"_215":496,"_167":168,"_378":627},{"_404":462,"_215":462,"_167":168,"_378":622},{"_404":615,"_215":615,"_167":168,"_378":616},{"_404":611,"_215":611,"_167":168,"_378":612},{"_404":606,"_215":606,"_167":168,"_378":607},"nvcrm.my.site.com",[608],{"_77":609,"_410":606,"_411":31,"_167":610},"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",[613],{"_77":614,"_410":611,"_411":31,"_167":445},"edge","github.com",[617,618],{"_77":621,"_410":615,"_411":31,"_167":620},{"_77":619,"_410":615,"_411":31,"_167":620},"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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