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Model Card - Fast-FoundationStereo

Model Overview

Description:

Fast-FoundationStereo estimates the disparity of each pixel in a rectified binocular stereo pair of images. It is a transformer-based foundational model that shows strong zero-shot generalization while running in real time. Fast-FoundationStereo was developed by NVIDIA. This model is ready for commercial or non-commercial use.

License/Terms of Use:

This model is released under the NVIDIA Open Model Agreement: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/

Deployment Geography:

Global

Use Case:

Researchers and developers in the field of computer vision, specifically those interested in depth estimation, are expected to use this method for tasks such as three-dimensional reconstruction, object detection, object pose estimation, and scene understanding.

Release Date:

Github 02/01/2026 via https://github.com/NVlabs/Fast-FoundationStereo

References(s):

Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matching

Model Architecture:

Architecture Type: Transformer and Convolutional Neural Network (CNN)

Network Architecture: The network contains three parts: 1) an EdgeNeXt student module that distills the original FoundationStereo feature extractor; 2) a set of CNN and transformer blocks that perform matching with long-range dependencies; 3) a reduced set of convGRU blocks.

This model was developed based on FoundationStereo.

Number of model parameters: 14.6M (1.46*10^7).

Computational Load (Internal Only: For NVIDIA Models Only)

Cumulative Compute: Trained on 32 × NVIDIA A100-SXM4-80GB GPUs (4 nodes × 8 GPUs) for 206,389 s of wall-clock time (~57.3 h, ~1.83 × 10^3 GPU-hours) using FP16 mixed-precision. At the A100 dense FP16 Tensor Core peak of 312 TFLOPS, this corresponds to an upper-bound cumulative compute of ~2.06 × 10^21 FLOPs.
\nEstimated Energy and Emissions for Model Training: Using the A100-SXM4-80GB TDP of 400 W, GPU energy ≈ 734 kWh. Applying a 1.5× node-level overhead (CPU, DRAM, NIC, storage; Patterson et al. 2021) and a 1.2 data-center PUE yields ≈ 1.32 × 10^3 kWh of total system energy. Assuming a grid carbon intensity of 0.37 kg CO₂eq/kWh (U.S. average), estimated emissions ≈ 4.9 × 10^2 kg CO₂eq.

Input(s):

Input Type(s): Image (a pair of two rectified binocular stereo images)

Input Format(s):

Image: Red, Green, Blue (RGB)

Input Parameters:

Image: Two-Dimensional (2D)

Other Properties Related to Input: Rectified stereo image pair from a camera such as Zed. The baseline is needed to convert disparity to depth. No alpha channel and no pre-processing needed. Bit depth: 24-bit.

Output(s)

Output Type(s): Image (disparity map)

Output Format(s):

Image: 16-bit unsigned integer

Output Parameters:

Image: Two-Dimensional (2D)

Other Properties Related to Output: Final 2D disparity map. No alpha channel and no post-processing needed. Bit depth: 16-bit.

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):

Not Applicable (N/A)

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere

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: Initial model version with full capabilities, unpruned and trained.

Training, Testing, and Evaluation Datasets:

Training Dataset:

Data Modality:

Image

Training Data Size:

** Image Training Data Size

1 Million to 1 Billion Images

** Data Collection Method by dataset

Hybrid: Synthetic, Automatic/Sensors

** Labeling Method by dataset

Hybrid: Synthetic, Automatic/Sensors

Properties: The training dataset includes: 1) a large-scale internal proprietary synthetic dataset featuring 1.4 million stereo pairs with a large diversity of objects and scenes and high photorealism; 2) the external commercial SDG.

Link: Internal, proprietary dataset, and external commercial SDG.

Testing Dataset:

Link: Middlebury dataset.

Data Collection Method by dataset:

Automatic/Sensors

Labeling Method by dataset:

Automatic/Sensors

Properties: The dataset encompasses a wide range of scenarios, includes diverse three-dimensional assets, captures stereo images under diversely randomized camera parameters, and achieves high fidelity in both rendering and spatial layouts.

Evaluation Dataset:

Benchmark Score: Evaluated on the public leaderboards well known to the stereo community.

Middlebury: https://vision.middlebury.edu/stereo/ — the classic leaderboard for dense stereo matching, developed at Middlebury College.
ETH3D: https://www.eth3d.net/ — a popular leaderboard for stereo, developed at ETH.
KITTI: https://www.cvlibs.net/datasets/kitti/eval_stereo.php — a cornerstone benchmark for autonomous driving research.

Data Collection Method by dataset:

Automatic/Sensors

Labeling Method by dataset:

Automatic/Sensors

Properties:

The Middlebury Stereo dataset consists of high-resolution stereo sequences with complex geometry and pixel-accurate ground-truth disparity data. The ground-truth disparities were acquired using a novel technique that employs structured lighting and infrared paint.
ETH3D is a multi-view stereo / 3D reconstruction benchmark that covers a variety of indoor and outdoor scenes. Ground-truth geometry was obtained using a high-precision laser scanner. A DSLR camera as well as a synchronized multi-camera rig with varying field-of-view was used to capture images.
KITTI stereo dataset is a cornerstone of autonomous driving research, developed by the Karlsruhe Institute of Technology (KIT) and the Toyota Technological Institute at Chicago (TTIC). It provides real-world, high-resolution stereo imagery paired with precise ground-truth depth data collected from a moving vehicle in diverse urban environments.

Inference:

Acceleration Engine: TensorRT
\nTest Hardware:

Zed Stereo Camera
NVIDIA GeForce RTX 3090

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 make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.

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

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Model Card - Fast-FoundationStereo

\n\n# Model Overview\n\n### Description:\nFast-FoundationStereo estimates the disparity of each pixel in a rectified binocular stereo pair of images. It is a transformer-based foundational model that shows strong zero-shot generalization while running in real time. Fast-FoundationStereo was developed by NVIDIA. This model is ready for commercial or non-commercial use.
\n\n### License/Terms of Use:\nThis model is released under the NVIDIA Open Model Agreement: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/
\n\n### Deployment Geography:\nGlobal
\n\n### Use Case:
\nResearchers and developers in the field of computer vision, specifically those interested in depth estimation, are expected to use this method for tasks such as three-dimensional reconstruction, object detection, object pose estimation, and scene understanding.
\n\n### Release Date:
\nGithub 02/01/2026 via https://github.com/NVlabs/Fast-FoundationStereo
\n\n## References(s):\n[Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matching](https://arxiv.org/abs/2512.11130)
\n\n## Model Architecture:\n**Architecture Type:** Transformer and Convolutional Neural Network (CNN)
\n\n**Network Architecture:** The network contains three parts: 1) an EdgeNeXt student module that distills the original FoundationStereo feature extractor; 2) a set of CNN and transformer blocks that perform matching with long-range dependencies; 3) a reduced set of convGRU blocks.
\n\n**This model was developed based on FoundationStereo.**
\n\n**Number of model parameters:** 14.6M (1.46*10^7).
\n\n## Computational Load (Internal Only: For NVIDIA Models Only)\n**Cumulative Compute:** Trained on 32 × NVIDIA A100-SXM4-80GB GPUs (4 nodes × 8 GPUs) for 206,389 s of wall-clock time (~57.3 h, ~1.83 × 10^3 GPU-hours) using FP16 mixed-precision. At the A100 dense FP16 Tensor Core peak of 312 TFLOPS, this corresponds to an upper-bound cumulative compute of ~2.06 × 10^21 FLOPs.
\n**Estimated Energy and Emissions for Model Training:** Using the A100-SXM4-80GB TDP of 400 W, GPU energy ≈ 734 kWh. Applying a 1.5× node-level overhead (CPU, DRAM, NIC, storage; Patterson et al. 2021) and a 1.2 data-center PUE yields ≈ 1.32 × 10^3 kWh of total system energy. Assuming a grid carbon intensity of 0.37 kg CO₂eq/kWh (U.S. average), estimated emissions ≈ 4.9 × 10^2 kg CO₂eq.
\n\n## Input(s):
\n**Input Type(s):** Image (a pair of two rectified binocular stereo images)
\n\n**Input Format(s):**\n- Image: Red, Green, Blue (RGB)
\n\n**Input Parameters:**\n- Image: Two-Dimensional (2D)
\n\n**Other Properties Related to Input:** Rectified stereo image pair from a camera such as Zed. The baseline is needed to convert disparity to depth. No alpha channel and no pre-processing needed. Bit depth: 24-bit.
\n\n## Output(s)\n\n**Output Type(s):** Image (disparity map)
\n\n**Output Format(s):**\n- Image: 16-bit unsigned integer
\n\n**Output Parameters:**\n- Image: Two-Dimensional (2D)
\n\n**Other Properties Related to Output:** Final 2D disparity map. No alpha channel and no post-processing needed. Bit depth: 16-bit.
\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\n## Software Integration:\n**Runtime Engine(s):**\n* Not Applicable (N/A)
\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Ampere
\n\n**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\n## Model Version(s):\nv1.0: Initial model version with full capabilities, unpruned and trained.
\n\n## Training, Testing, and Evaluation Datasets:\n\n## Training Dataset:\n\n### Data Modality:\n* Image
\n\n### Training Data Size:\n\n** Image Training Data Size
\n* 1 Million to 1 Billion Images
\n\n** Data Collection Method by dataset
\n* Hybrid: Synthetic, Automatic/Sensors
\n\n** Labeling Method by dataset
\n* Hybrid: Synthetic, Automatic/Sensors
\n\n**Properties:** The training dataset includes: 1) a large-scale internal proprietary synthetic dataset featuring 1.4 million stereo pairs with a large diversity of objects and scenes and high photorealism; 2) the external commercial SDG.\n\n**Link:** Internal, proprietary dataset, and external commercial SDG.
\n\n### Testing Dataset:\n\n**Link:** Middlebury dataset.
\n\nData Collection Method by dataset:
\n* Automatic/Sensors
\n\nLabeling Method by dataset:
\n* Automatic/Sensors
\n\n**Properties:** The dataset encompasses a wide range of scenarios, includes diverse three-dimensional assets, captures stereo images under diversely randomized camera parameters, and achieves high fidelity in both rendering and spatial layouts.
\n\n### Evaluation Dataset:\n\n**Benchmark Score:** Evaluated on the public leaderboards well known to the stereo community.
\n\n* Middlebury: https://vision.middlebury.edu/stereo/ — the classic leaderboard for dense stereo matching, developed at Middlebury College.
\n* ETH3D: https://www.eth3d.net/ — a popular leaderboard for stereo, developed at ETH.
\n* KITTI: https://www.cvlibs.net/datasets/kitti/eval_stereo.php — a cornerstone benchmark for autonomous driving research.
\n\nData Collection Method by dataset:
\n* Automatic/Sensors
\n\nLabeling Method by dataset:
\n* Automatic/Sensors
\n\n**Properties:**\n* The Middlebury Stereo dataset consists of high-resolution stereo sequences with complex geometry and pixel-accurate ground-truth disparity data. The ground-truth disparities were acquired using a novel technique that employs structured lighting and infrared paint.
\n* ETH3D is a multi-view stereo / 3D reconstruction benchmark that covers a variety of indoor and outdoor scenes. Ground-truth geometry was obtained using a high-precision laser scanner. A DSLR camera as well as a synchronized multi-camera rig with varying field-of-view was used to capture images.
\n* KITTI stereo dataset is a cornerstone of autonomous driving research, developed by the Karlsruhe Institute of Technology (KIT) and the Toyota Technological Institute at Chicago (TTIC). It provides real-world, high-resolution stereo imagery paired with precise ground-truth depth data collected from a moving vehicle in diverse urban environments.
\n\n## Inference:\n**Acceleration Engine:** TensorRT
\n**Test Hardware:**
\n* Zed Stereo Camera
\n* NVIDIA GeForce RTX 3090
\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\nPlease make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.
\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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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467],[306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322],{"_323":429,"_156":429,"_114":167,"_296":430},{"_323":424,"_156":424,"_114":167,"_296":425},{"_323":420,"_156":420,"_114":167,"_296":421},{"_323":415,"_156":415,"_114":167,"_296":416},{"_323":410,"_156":410,"_114":167,"_296":411},{"_323":398,"_156":398,"_114":167,"_296":399},{"_323":386,"_156":386,"_114":167,"_296":387},{"_323":381,"_156":381,"_114":167,"_296":382},{"_323":370,"_156":370,"_114":167,"_296":371},{"_323":365,"_156":365,"_114":167,"_296":366},{"_323":360,"_156":360,"_114":167,"_296":361},{"_323":355,"_156":355,"_114":167,"_296":356},{"_323":350,"_156":350,"_114":167,"_296":351},{"_323":345,"_156":345,"_114":167,"_296":346},{"_323":340,"_156":340,"_114":167,"_296":341},{"_323":333,"_156":333,"_114":167,"_296":334},{"_323":324,"_156":324,"_114":167,"_296":325},"hostName","amazon-adsystem.com",[326,327],{"_143":332,"_329":324,"_330":18,"_114":331},{"_143":328,"_329":324,"_330":18,"_114":331},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[335,336],{"_143":339,"_329":333,"_330":18,"_114":338},{"_143":337,"_329":333,"_330":18,"_114":338},"IDE","This domain is owned by Doubleclick (Google). 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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",[362],{"_143":363,"_329":360,"_330":18,"_114":364},"sp","Testing","google.com",[367],{"_143":368,"_329":365,"_330":18,"_114":369},"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",[372,373,374,375],{"_143":380,"_329":370,"_330":18,"_114":377},{"_143":379,"_329":370,"_330":18,"_114":377},{"_143":378,"_329":370,"_330":18,"_114":377},{"_143":376,"_329":370,"_330":18,"_114":377},"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",[383],{"_143":384,"_329":381,"_330":18,"_114":385},"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",[388,389,390,391],{"_143":397,"_329":386,"_330":18,"_114":393},{"_143":395,"_329":386,"_330":18,"_114":396},{"_143":394,"_329":386,"_330":18,"_114":393},{"_143":392,"_329":386,"_330":18,"_114":393},"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",[400,401,402,403],{"_143":409,"_329":398,"_330":18,"_114":407},{"_143":408,"_329":398,"_330":18,"_114":407},{"_143":406,"_329":398,"_330":18,"_114":407},{"_143":404,"_329":398,"_330":18,"_114":405},"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",[412],{"_143":413,"_329":410,"_330":18,"_114":414},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[417],{"_143":418,"_329":415,"_330":18,"_114":419},"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",[422],{"_143":423,"_329":420,"_330":18,"_114":364},"R","6sc.co",[426],{"_143":427,"_329":424,"_330":18,"_114":428},"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.",[555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575],[520,521,522,523,524],{"_323":415,"_156":415,"_114":167,"_296":546},{"_323":381,"_156":381,"_114":167,"_296":541},{"_323":534,"_156":534,"_114":167,"_296":535},{"_323":530,"_156":530,"_114":167,"_296":531},{"_323":525,"_156":525,"_114":167,"_296":526},"nvcrm.my.site.com",[527],{"_143":528,"_329":525,"_330":18,"_114":529},"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",[532],{"_143":533,"_329":530,"_330":18,"_114":364},"edge","github.com",[536,537],{"_143":540,"_329":534,"_330":18,"_114":539},{"_143":538,"_329":534,"_330":18,"_114":539},"logged_in","This domain is owned by Github. 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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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