CWIP Overview

Description:

CWIP (Contrastive World-Image Pre-training) projects a camera frame and a world-model frame into a joint embedding space to score camera-to-world consistency and emit per-patch object-presence and object-type classifications. It was developed by NVIDIA as part of Cosmos Evaluator for automated quality scoring of generative world foundation models.

This model is ready for commercial or non-commercial use.

License/Terms of Use:

OpenMDW-1.1

Deployment Geography:

Global

Use Case:

Developers and researchers building, evaluating, or auditing generative world foundation models for autonomous driving, including users of NVIDIA Cosmos and the open-source Cosmos Evaluator suite. Specific applications include per-frame embeddings, alignment scoring, per-patch defect detection, object-type classification, and automated quality scoring of driving-video generations.

Release Date:

GitHub: 07/07/2026 via https://github.com/nvidia-cosmos/cosmos-evaluator
\nNGC: 07/07/2026 via https://catalog.ngc.nvidia.com/orgs/nvidia/cosmos/models/cwip
\nHuggingFace: 07/07/2026 via https://huggingface.co/nvidia/CWIP-1.0

Reference(s):

Perception Encoder: The best visual embeddings are not at the output of the network
\nLearning Transferable Visual Models From Natural Language Supervision
\nDeformable DETR: Deformable Transformers for End-to-End Object Detection
\nSigmoid Loss for Language Image Pre-Training

Model Architecture:

Architecture Type: Transformer
\nNetwork Architecture:\nA custom dual-encoder architecture that encodes a pair of world-model control frame and camera frame, and outputs global embeddings for each frame and two segmentation masks for consistency and object-type prediction respectively. PE-Spatial-B16-512 is used as the frozen image encoder in v1.0; only the alignment layers and patch classifier are trained, on 1280x720 frame pairs.

Model is trained and tested with BF16 precision.

This model was developed based on PE-Spatial-B16-512 (used frozen).
\nNumber of model parameters: 0.3B (0.3*10^9)

Input:

Input Type(s): Image
\nInput Format(s): RGB (Red, Green, Blue)
\nInput Parameters: Three-Dimensional (3D)
\nOther Properties Related to Input: The model accepts a paired camera frame and world-model frame as input, where world-model frames are visual representations of autonomous-driving-relevant scene elements. The pair is encoded into per-frame embeddings used for camera-to-world consistency scoring, per-patch defect detection, and object-type classification. Camera and world frames must share spatial dimensions (the camera frame is resized to match the world frame if they differ). Trainable layers were trained and evaluated at 1280x720 frame pairs; other resolutions are supported via RoPE2D and bilinear interpolation of the encoder's absolute position embedding.

Output:

Output Type(s): Embeddings, Segmentation Masks
\nOutput Format: Tensor, Tensor
\nOutput Parameters: One-Dimensional (1D), Three-Dimensional (3D)
\nOther Properties Related to Output: The model outputs per-frame L2-normalized 1024-dimensional global embedding vectors for world-model frame and camera frame, per-patch defect classifications (MATCH, EXTRA, MISSING, WRONG, DONT_CARE) and object-type classifications over an 18-class taxonomy. These outputs are used for automated quality scoring of driving-video generations. Defect classification masks (MATCH+EXTRA+WRONG) may be used to filter object-type classification to improve precision.

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):

PyTorch
Transformers (optional)

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Hopper
NVIDIA Lovelace

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):

CWIP v1.0

Model weights are stored in HuggingFace safetensors format. PyTorch is required for inference. See Cosmos Evaluator repo for reference usage.

Training and Evaluation Datasets:

Training Dataset:

Data Modality: Video

Video Training Data Size: Less than 10,000 Hours
\nData Collection Method by dataset: Automated
\nLabeling Method by dataset: Automated
\nProperties (Quantity, Dataset Descriptions, Sensor(s)): Approximately 136,000 camera and world-model video pairs of driving scenes. Each video is approximately 20 seconds long.

Evaluation Dataset:

Data Collection Method by dataset: Hybrid: Automated/Synthetic
\nLabeling Method by dataset: Hybrid: Automated/Manually-Labelled/Synthetic
\nProperties (Quantity, Dataset Descriptions, Sensor(s)): Approximately 7K camera and world-model video pairs of real driving scenes. Each video is ~20 seconds long. Positive and negative frame pairs are sampled from these videos for evaluation. Evaluates Precision, Recall, F1, and IoU, and visual qualitative check.

Approximately 1.2K camera and world-model video pairs of synthetic driving scenes with human labels. Each video is ~10 seconds long. Evaluates missing objects and hallucinations, and visual qualitative check.

Inference:

Acceleration Engine: PyTorch
\nTest Hardware: NVIDIA Hopper (H100)

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. 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.

For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.

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

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It was developed by NVIDIA as part of Cosmos Evaluator for automated quality scoring of generative world foundation models. \n\nThis model is ready for commercial or non-commercial use. \n\n\n### License/Terms of Use:\n[OpenMDW-1.1](https://openmdw.ai/license/1-1/)\n### Deployment Geography: \nGlobal\n\n### Use Case: \nDevelopers and researchers building, evaluating, or auditing generative world foundation models for autonomous driving, including users of NVIDIA Cosmos and the open-source Cosmos Evaluator suite. Specific applications include per-frame embeddings, alignment scoring, per-patch defect detection, object-type classification, and automated quality scoring of driving-video generations. \n \n\n### Release Date:\n**GitHub:** 07/07/2026 via https://github.com/nvidia-cosmos/cosmos-evaluator \n**NGC:** 07/07/2026 via https://catalog.ngc.nvidia.com/orgs/nvidia/cosmos/models/cwip \n**HuggingFace:** 07/07/2026 via https://huggingface.co/nvidia/CWIP-1.0 \n \n\n## Reference(s):\n[Perception Encoder: The best visual embeddings are not at the output of the network](https://arxiv.org/abs/2504.13181) \n[Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) \n[Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) \n[Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) \n\n## Model Architecture: \n**Architecture Type:** Transformer \n**Network Architecture:**\nA custom dual-encoder architecture that encodes a pair of world-model control frame and camera frame, and outputs global embeddings for each frame and two segmentation masks for consistency and object-type prediction respectively. [PE-Spatial-B16-512](https://huggingface.co/facebook/PE-Spatial-B16-512) is used as the frozen image encoder in v1.0; only the alignment layers and patch classifier are trained, on 1280x720 frame pairs.\n\nModel is trained and tested with BF16 precision.\n\n**This model was developed based on PE-Spatial-B16-512 (used frozen).** \n**Number of model parameters:** 0.3B (0.3*10^9) \n \n \n## Input: \n**Input Type(s):** Image \n**Input Format(s):** RGB (Red, Green, Blue) \n**Input Parameters:** Three-Dimensional (3D) \n**Other Properties Related to Input:** The model accepts a paired camera frame and world-model frame as input, where world-model frames are visual representations of autonomous-driving-relevant scene elements. The pair is encoded into per-frame embeddings used for camera-to-world consistency scoring, per-patch defect detection, and object-type classification. Camera and world frames must share spatial dimensions (the camera frame is resized to match the world frame if they differ). Trainable layers were trained and evaluated at 1280x720 frame pairs; other resolutions are supported via RoPE2D and bilinear interpolation of the encoder's absolute position embedding. \n \n\n## Output: \n**Output Type(s):** Embeddings, Segmentation Masks \n**Output Format:** Tensor, Tensor \n**Output Parameters:** One-Dimensional (1D), Three-Dimensional (3D) \n**Other Properties Related to Output:** The model outputs per-frame L2-normalized 1024-dimensional global embedding vectors for world-model frame and camera frame, per-patch defect classifications (MATCH, EXTRA, MISSING, WRONG, DONT_CARE) and object-type classifications over an 18-class taxonomy. These outputs are used for automated quality scoring of driving-video generations. Defect classification masks (MATCH+EXTRA+WRONG) may be used to filter object-type classification to improve precision. \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* PyTorch\n* Transformers (optional)\n\n**Supported Hardware Microarchitecture Compatibility:**\n* NVIDIA Ampere\n* NVIDIA Hopper\n* NVIDIA Lovelace\n\n**Supported Operating System(s):** 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\n## Model Version(s): \nCWIP v1.0 \n\nModel weights are stored in HuggingFace safetensors format. PyTorch is required for inference. See [Cosmos Evaluator repo](https://github.com/NVIDIA/cosmos-evaluator) for reference usage. \n \n## Training and Evaluation Datasets: \n\n\n## Training Dataset:\n\n**Data Modality:** Video \n\n**Video Training Data Size:** Less than 10,000 Hours \n**Data Collection Method by dataset:** Automated \n**Labeling Method by dataset:** Automated \n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** Approximately 136,000 camera and world-model video pairs of driving scenes. Each video is approximately 20 seconds long.\n\n### Evaluation Dataset:\n\n**Data Collection Method by dataset:** Hybrid: Automated/Synthetic \n**Labeling Method by dataset:** Hybrid: Automated/Manually-Labelled/Synthetic \n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** Approximately 7K camera and world-model video pairs of real driving scenes. Each video is ~20 seconds long. Positive and negative frame pairs are sampled from these videos for evaluation. Evaluates Precision, Recall, F1, and IoU, and visual qualitative check.\n\nApproximately 1.2K camera and world-model video pairs of synthetic driving scenes with human labels. Each video is ~10 seconds long. Evaluates missing objects and hallucinations, and visual qualitative check.\n\n\n## Inference:\n**Acceleration Engine:** PyTorch \n**Test Hardware:** NVIDIA Hopper (H100)\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. 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\nFor more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. \n \nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://app.intigriti.com/programs/nvidia/nvidiavdp/detail).","displayName","CWIP (Contrastive World-Image Pre-training)",["D",1783460269382],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/cosmos/models/cwip","rawImageUrl","","canGuestDownload","latestVersionSizeInBytes","licenseTerms",{"_262":263,"_264":265,"_266":267,"_268":34,"_269":270},"isMultinodeEnabled","isMultiArchitecture","bias","Field | Response\n:---------------------------------------------------------------------------------------------------|:---------------\nParticipation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None\nMeasures taken to mitigate against unwanted bias: | None","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Task/Domain: | Synthetic Data Quality Evaluation\nModel Type: | Transformer\nIntended Users: | Developers and researchers building, evaluating, or auditing generative world foundation models for autonomous driving, including users of NVIDIA Cosmos and the open-source Cosmos Evaluator suite. Specific applications include per-frame embeddings, alignment scoring, per-patch defect detection, object-type classification, and automated quality scoring of driving-video generations.\nOutput: | L2-normalized 1024-dimensional global embedding vectors for world-model frame and camera frame, and segmentation masks for per-patch defect classifications (MATCH, EXTRA, MISSING, WRONG, DONT_CARE) and object-type classifications over an 18-class taxonomy respectively.\nDescribe how the model works: | CWIP projects a camera frame and a world-model frame into a joint embedding space to score camera-to-world consistency and emit per-patch object-presence and object-type classifications. It uses a transformer-based architecture to process visual inputs and generate embeddings and segmentation masks for quality scoring of driving-video generations.\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable\nTechnical Limitations and Mitigation: | This model may not detect all inconsistencies between world-model control frame and camera frame. This model is trained on pairs of world-model control frame and camera frame. For other control signals, adaptation to the world-model control format or re-training with those specific control signals may be needed.\nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | Precision, Recall, F1, and IoU\nPotential Known Risks: | This model may output inaccurate semantic segmentation or suboptimal global embeddings that lead to incorrect synthetic image/video defect detection.\nTerms of Use/Licensing: | [OpenMDW-1.1](https://openmdw.ai/license/1-1/)","privacy","| Privacy Information |\n| --- |\n| The CWIP model was trained on data that may contain images, audio-video, and text relating to people. NVIDIA collected and used this data in compliance with applicable data protection and privacy laws. This model was not designed to specifically derive insights or otherwise learn from any personal data contained in the datasets. |\n| NVIDIA uses a combination of filters, data minimization techniques, and other guardrails to help prevent personal data from being recited by our models. |\n| Please review NVIDIA's Applicable [Privacy Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) for more information. |","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nModel Application Field(s): | Synthetic Data Quality Evaluation\nDescribe the life critical impact (if present). | Not Applicable\nUse Case Restrictions: | Abide by [OpenMDW-1.1](https://openmdw.ai/license/1-1/)\nModel and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. 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GOVERNING DOWNLOAD TERMS: Use of the model is governed by the OpenMDW License Agreement, version 1.1.

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GOVERNING DOWNLOAD TERMS: Use of the model is governed by the OpenMDW License Agreement, version 1.1.

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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",[438],{"_78":439,"_405":436,"_406":34,"_182":440},"sp","Testing","google.com",[443],{"_78":444,"_405":441,"_406":34,"_182":445},"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",[448,449,450,451],{"_78":456,"_405":446,"_406":34,"_182":453},{"_78":455,"_405":446,"_406":34,"_182":453},{"_78":454,"_405":446,"_406":34,"_182":453},{"_78":452,"_405":446,"_406":34,"_182":453},"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",[459],{"_78":460,"_405":457,"_406":34,"_182":461},"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",[464,465,466,467],{"_78":473,"_405":462,"_406":34,"_182":469},{"_78":471,"_405":462,"_406":34,"_182":472},{"_78":470,"_405":462,"_406":34,"_182":469},{"_78":468,"_405":462,"_406":34,"_182":469},"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",[476,477,478,479],{"_78":485,"_405":474,"_406":34,"_182":483},{"_78":484,"_405":474,"_406":34,"_182":483},{"_78":482,"_405":474,"_406":34,"_182":483},{"_78":480,"_405":474,"_406":34,"_182":481},"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",[488],{"_78":489,"_405":486,"_406":34,"_182":490},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[493],{"_78":494,"_405":491,"_406":34,"_182":495},"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",[498],{"_78":499,"_405":496,"_406":34,"_182":440},"R","6sc.co",[502],{"_78":503,"_405":500,"_406":34,"_182":504},"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.",4,[632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652],[597,598,599,600,601],{"_399":491,"_210":491,"_182":220,"_373":623},{"_399":457,"_210":457,"_182":220,"_373":618},{"_399":611,"_210":611,"_182":220,"_373":612},{"_399":607,"_210":607,"_182":220,"_373":608},{"_399":602,"_210":602,"_182":220,"_373":603},"nvcrm.my.site.com",[604],{"_78":605,"_405":602,"_406":34,"_182":606},"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",[609],{"_78":610,"_405":607,"_406":34,"_182":440},"edge","github.com",[613,614],{"_78":617,"_405":611,"_406":34,"_182":616},{"_78":615,"_405":611,"_406":34,"_182":616},"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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