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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Governing Terms: Your use of the model is governed by the NVIDIA Open Model Agreement.

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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.","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.","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. 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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 performance.",[711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754],[683,684,685,686,687],{"_380":706,"_210":706,"_169":221,"_352":707},{"_380":701,"_210":701,"_169":221,"_352":702},{"_380":450,"_210":450,"_169":221,"_352":698},{"_380":691,"_210":691,"_169":221,"_352":692},{"_380":596,"_210":596,"_169":221,"_352":688},[689],{"_76":690,"_386":596,"_387":89,"_169":600},"_gh_sess","statcounter.com",[693,694],{"_76":697,"_386":691,"_387":31,"_169":696},{"_76":695,"_386":691,"_387":31,"_169":696},"is_unique","This domain is owned by StatCounter.\nThe main business activity is: Real time visitor analytics","is_visitor_unique",[699],{"_76":700,"_386":450,"_387":89,"_169":456},"YSC","bizible.com",[703],{"_76":704,"_386":701,"_387":31,"_169":705},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[708],{"_76":709,"_386":706,"_387":31,"_169":710},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_76":816,"_386":524,"_387":31,"_169":817},{"_76":814,"_386":524,"_387":89,"_169":815},{"_76":811,"_386":812,"_387":31,"_169":813},{"_76":789,"_386":802,"_387":89,"_169":790},{"_76":809,"_386":524,"_387":31,"_169":810},{"_76":763,"_386":527,"_387":31,"_169":659},{"_76":763,"_386":560,"_387":31,"_169":659},{"_76":807,"_386":524,"_387":31,"_169":808},{"_76":791,"_386":646,"_387":89,"_169":792},{"_76":789,"_386":530,"_387":89,"_169":790},{"_76":805,"_386":524,"_387":31,"_169":806},{"_76":803,"_386":524,"_387":31,"_169":804},{"_76":797,"_386":524,"_387":31,"_169":221},{"_76":788,"_386":802,"_387":31,"_169":431},{"_76":789,"_386":527,"_387":89,"_169":790},{"_76":800,"_386":560,"_387":31,"_169":801},{"_76":798,"_386":651,"_387":31,"_169":799},{"_76":789,"_386":675,"_387":89,"_169":790},{"_76":797,"_386":646,"_387":31,"_169":221},{"_76":761,"_386":527,"_387":31,"_169":762},{"_76":788,"_386":527,"_387":31,"_169":431},{"_76":795,"_386":524,"_387":89,"_169":796},{"_76":793,"_386":524,"_387":31,"_169":794},{"_76":791,"_386":524,"_387":89,"_169":792},{"_76":789,"_386":646,"_387":89,"_169":790},{"_76":788,"_386":646,"_387":31,"_169":431},{"_76":786,"_386":524,"_387":31,"_169":787},{"_76":784,"_386":524,"_387":31,"_169":785},{"_76":782,"_386":524,"_387":89,"_169":783},{"_76":779,"_386":780,"_387":31,"_169":781},{"_76":761,"_386":646,"_387":31,"_169":762},{"_76":777,"_386":524,"_387":89,"_169":778},{"_76":775,"_386":524,"_387":31,"_169":776},{"_76":774,"_386":646,"_387":89,"_169":769},{"_76":772,"_386":524,"_387":89,"_169":773},{"_76":770,"_386":539,"_387":31,"_169":771},{"_76":768,"_386":646,"_387":89,"_169":769},{"_76":766,"_386":524,"_387":31,"_169":767},{"_76":764,"_386":524,"_387":31,"_169":765},{"_76":763,"_386":524,"_387":31,"_169":659},{"_76":761,"_386":539,"_387":31,"_169":762},{"_76":759,"_386":524,"_387":31,"_169":760},{"_76":757,"_386":524,"_387":89,"_169":758},{"_76":755,"_386":524,"_387":31,"_169":756},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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The cookie is used for measuring a visitor’s scroll activity to see how much of a page they view before moving on to another page.","_an_uid","This cookie is likely used for analytics purposes to track user behavior and site usage, which may include unique user identification for tracking.","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","s_ips","_hly_vid","This cookie _hly_vid 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.","s_plt","This cookie is used to measure the time taken for pages to load, allowing website owners to identify slow loading pages and improve user experience.","_session_id","resources.nvidia.com","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. 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It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","omniverse.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","This cookie is associated with Akamai. 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It is a temporary cookie used to determine if the browser accepts cookies.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","bm_ss","This cookie is used to help protect websites from automated bot traffic.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. 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Name	Size	Updated	Actions
cfg.yaml	182 B	June 1, 2026 UTC	curl wget Download
model_best_bp2_serialize.pth	67.81 MB	June 1, 2026 UTC	curl wget Download