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Field	Response
Participation considerations from adversely impacted groups (protected classes) in model design and testing:	None of the Above
Measures taken to mitigate against unwanted bias:	None.

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NvDepthAnythingV2 Overview

Description:

DepthAnythingV2 is a state-of-the-art monocular depth estimation model that generalizes well on zero-shot images. TAO NvDepthAnythingV2 relative depth model is a pretrained commercial model that can produce a relative depth map given a single image. In addition, NvDepthAnythingV2 can be used as initialization when fine-tuning the MetricDepthAnything model for better accuracy.

This model is ready for commercial use.

License/Terms of Use

License to use these models is covered by the NVIDIA Open Model License. By downloading the model, you accept the terms and conditions of these licenses.

Deployment Geography:

Global

Use Case:

This model is intended for developers working on industrial, robotics, and smart space applications to estimate the depth from monocular image input.

Release Date:

NGC [07/25/2025] link

References(s):

DepthAnythingV2 paper

Model Architecture:

Architecture Type: Transformer-based Network Architecture

Network Architecture: Vit-Large
\nNumber of model parameters: 3.6*10^8\n**This model was developed based on DINOV2-ViT-Large

Computational Load

Cumulative Compute: 2.0952*10^16
\nEstimated Energy and Emissions for Model Training:

Energy Consumption in kWh: 1075.2 kWh
Emissions: 0.3472896 tCO2e

Input:

Input Type(s): RGB image
\nInput Format: Red, Green, Blue (RGB)
\nInput Parameters: Two-Dimensional (2D)
\nOther Properties Related to Input: B X 3 X H X W (Batch Size x Channel x Height x Width)

Output:

Output Type(s): Image
\nOutput Format: Depth Map Image
\nOutput Parameters: Two-Dimensional (2D)
\nOther Properties Related to Output: B X 3 X H X W (Batch Size x Channel x Height x Width)

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 Engines

TAO 6.2.0

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
Linux 4 Tegra
QNX
Windows

Model Version(s):

deployable_relative_depthanythingv2_large_v1.0: decrypted ONNX files. Inference supported in TAO Toolkit.

Training, Testing, and Evaluation Datasets:

** The total size (in number of data points): 6.02M images
\n** Total number of datasets: 4 datasets

** Dataset partition: Training 5.99M, validation 29K

Training Dataset:

Data Collection Method by dataset:

Hybrid: Automatic/Sensors, Synthetic

Labeling Method by dataset:

Hybrid: Automatic/Sensors, Synthetic

Properties:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Dataset	No. of Images	Sensor
NV Internal Real Image Data	3.6M	Automated
NV Internal Real Image Data	94K	RGB Camera
NV Internal Synthetic Data	2M	Synthetic
Crestereo Synthetic Data	196K	Synthetic

\n
\n

Evaluation Dataset:

Link:

NYUDV2 Dataset Page\n
\n

**Benchmark Score
\nAccuracy was determined using the following metrics:

AbsRel : (Scale-Shift Invariant) Absolute Relative Error
D-1 : (Scale-Shift Invariant) This measures the percentage of predicted pixels that differ from the true pixels by no more than 25%.

The results is zero-shot evaluation on the NYUDV2 dataset and relative depth (inverse depth) with scale-shift invariant accuracy is used to calculate the metric.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Method	AbsRel	D-1
NvDepthAnythingV2	0.047	0.979

Data Collection Method by dataset:

Undisclosed

Labeling Method by dataset:

Automatic/Sensors

Properties: 654 images and ground truth depth were used for evaluation.

Inference

Acceleration Engine: Onnx

Test Hardware [Name the specific test hardware model]

Jetson AGX Orin
Jetson AGX Thor
L4
L40S
A100
RTX PRO 6000 Blackwell
H200
H100
B200
GB200

The inference performance of the provided NvRelativeDepthAnything model is evaluated at FP16 precisions. The model's input resolution is 3x518x914 pixels. The performance assessment was conducted using trtexec on a range of devices.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Platform	BS	FPS
Jetson AGX Orin	32	3.705279734
Jetson AGX Thor	32	3.338531822
L4	32	17.89128806
L40S	32	62.5196595
A100	32	73.9937429
RTX PRO 6000 Blackwell	32	112.9716124
H200	32	166.6069658
H100	32	172.7824453
B200	32	320.4046711
GB200	32	350.4049915

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.\nFor more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

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\n\nThis model is ready for commercial use.
\n\n\n### License/Terms of Use\n\nLicense to use these models is covered by the NVIDIA Open Model License. By downloading the model, you accept the terms and conditions of these [licenses](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\n### Deployment Geography:\nGlobal
\n\n### Use Case:
\nThis model is intended for developers working on industrial, robotics, and smart space applications to estimate the depth from monocular image input.
\n\n### Release Date:
\nNGC [07/25/2025] [link](https://registry.ngc.nvidia.com/orgs/nvstaging/teams/tao/models/nvdepthanythingv2)
\n\n## References(s):\n[DepthAnythingV2 paper](https://arxiv.org/pdf/2406.09414)
\n\n## Model Architecture:\n**Architecture Type:** Transformer-based Network Architecture
\n\n**Network Architecture:** Vit-Large
\n**Number of model parameters:** 3.6*10^8\n**This model was developed based on [DINOV2-ViT-Large](https://github.com/facebookresearch/dinov2/tree/main)
\n\n## Computational Load\n**Cumulative Compute:** 2.0952*10^16
\n**Estimated Energy and Emissions for Model Training:**\n* Energy Consumption in kWh: 1075.2 kWh \n* Emissions: 0.3472896 tCO2e\n\n## Input:
\n**Input Type(s):** RGB image
\n**Input Format:** Red, Green, Blue (RGB)
\n**Input Parameters:** Two-Dimensional (2D)
\n**Other Properties Related to Input:** B X 3 X H X W (Batch Size x Channel x Height x Width)
\n\n## Output:
\n**Output Type(s):** Image
\n**Output Format:** Depth Map Image
\n**Output Parameters:** Two-Dimensional (2D)
\n**Other Properties Related to Output:** B X 3 X H X W (Batch Size x Channel x Height x Width)
\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 Engines**\n* TAO 6.2.0
\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* Linux 4 Tegra
\n* QNX
\n* Windows
\n\n\n## Model Version(s):\n- **deployable_relative_depthanythingv2_large_v1.0**: decrypted [ONNX](https://onnx.ai/) files. Inference supported in [TAO Toolkit](https://github.com/NVIDIA/tao_pytorch_backend).
\n\n\n## Training, Testing, and Evaluation Datasets:\n\n** The total size (in number of data points): 6.02M images
\n** Total number of datasets: 4 datasets
\n\n** Dataset partition: Training 5.99M, validation 29K
\n\n\n### Training Dataset:\n\nData Collection Method by dataset:
\n* Hybrid: Automatic/Sensors, Synthetic
\n\nLabeling Method by dataset:
\n* Hybrid: Automatic/Sensors, Synthetic
\n\n**Properties:** \n| Dataset | No. of Images |Sensor|\n|--|--|--|\n|NV Internal Real Image Data| 3.6M | Automated |\n|NV Internal Real Image Data| 94K | RGB Camera|\n|NV Internal Synthetic Data | 2M | Synthetic |\n|Crestereo Synthetic Data | 196K | Synthetic |\n
\n\n### Evaluation Dataset:\n\n**Link:** \n- NYUDV2 [Dataset Page](https://cs.nyu.edu/~fergus/datasets/nyu_depth_v2.html)\n
\n\n**Benchmark Score
\nAccuracy was determined using the following metrics:\n- AbsRel : (Scale-Shift Invariant) Absolute Relative Error\n- D-1 : (Scale-Shift Invariant) This measures the percentage of predicted pixels that differ from the true pixels by no more than 25%.\n\nThe results is zero-shot evaluation on the NYUDV2 dataset and relative depth (inverse depth) with scale-shift invariant accuracy is used to calculate the metric.\n\n| Method | AbsRel | D-1 | \n| ------- | ------- | -------|\n| NvDepthAnythingV2 | 0.047 | 0.979 |\n\n\nData Collection Method by dataset:
\n* Undisclosed
\n\nLabeling Method by dataset:
\n* Automatic/Sensors
\n\n**Properties:** 654 images and ground truth depth were used for evaluation.
\n\n# Inference\n**Acceleration Engine:** Onnx
\n\n**Test Hardware [Name the specific test hardware model]**\n- Jetson AGX Orin\n- Jetson AGX Thor\n- L4\n- L40S\n- A100\n- RTX PRO 6000 Blackwell\n- H200\n- H100\n- B200\n- GB200\n\nThe inference performance of the provided NvRelativeDepthAnything model is evaluated at FP16 precisions. The model's input resolution is 3x518x914 pixels. The performance assessment was conducted using trtexec on a range of devices.
\n| Platform | BS | FPS |\n|------------------|----|------|\n| Jetson AGX Orin\t | 32 |\t3.705279734 |\n| Jetson AGX Thor |\t32 |\t3.338531822 |\n| L4\t | 32 |\t17.89128806 |\n| L40S\t | 32 |\t62.5196595 |\n| A100\t | 32 |\t73.9937429 |\n| RTX PRO 6000 Blackwell |\t32 |\t112.9716124 |\n| H200\t | 32 |\t166.6069658 |\n| H100\t | 32 |\t172.7824453 |\n| B200\t | 32 |\t320.4046711 |\n| GB200\t | 32 |\t350.4049915 |\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.\nFor more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
","displayName","NvDepthAnythingV2",["D",1761345741112],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/nvdepthanythingv2","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","| Field | Response |\n| -- | -- |\n|Participation considerations from adversely impacted groups [(protected classes)](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None of the Above |\n| Measures taken to mitigate against unwanted bias: | None.|","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Task/Domain: | Monocular Depth Estimation\nModel Type: | Transformer\nIntended Users: | This model is intended for developers working in industrial, robotics, and smart space applications\nOutput: | Depth Map\nDescribe how the model works: | Image input is encoded into features, then depth map is estimated using Dense Prediction Transformers(DPT) Head\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable\nTechnical Limitations & Mitigation: | The model may fail to estimate very sharp edges (for example, where pixel values change quickly) in complex scenes. \nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | Accuracy - delta-1 (D1) \nPotential Known Risks: | The model may fail to estimate fine grain details in complex scenes (for example, scenes with complex textures).\n| Licensing: | [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) Additional Information: [Apache 2.0](https://github.com/facebookresearch/dinov2?tab=Apache-2.0-1-ov-file#readme)","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personal data? | No\nPersonal data used to create this model? | No\nHow often is dataset reviewed? | Before Release\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes\nApplicable Privacy Policy | [NVIDIA Privacy Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/)","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nModel Application Field(s): | Industrial/Machinery and Robotics
\nDescribe the life critical impact (if present). | Not Applicable
\nUse Case Restrictions: | Abide by [NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). Additional Information: [Apache 2.0](https://github.com/facebookresearch/dinov2?tab=Apache-2.0-1-ov-file#readme)\nModel and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.","featureLabels",[],"displayLabels",[198,199],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_161":207,"_201":207,"_202":208,"_204":205},{"_161":200,"_201":200,"_202":203,"_204":205},"NSPECT-FY5H-V6CO","value","filter",{"_206":205,"_201":200},"category","label","field","NSPECT-CQZ4-A7Z4",{"_206":205,"_201":207},"appConfig",{"_1346":1347,"_243":1348},"ngcApps",[1299],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1280":1281,"_1282":1283,"_1284":1285,"_1286":1287,"_1288":221,"_77":250,"_1289":1290,"_1291":44,"_1292":112,"_1293":112,"_1294":1295,"_1296":112,"_1297":1298},"amplitude",{"_1277":1278,"_885":-7,"_1279":20},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0yJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1271":1272,"_1273":1274,"_1275":1276},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","https://api.ngc.nvidia.com","ngcHelmOrigin","https://helm.ngc.nvidia.com","origin","https://catalog.ngc.nvidia.com","user","usesMocks","theme","year",2026,"now",1794308778900,"1.30.0","defaultSearchSuggestions",{"_1155":115,"_1156":1157,"_1158":1159,"_1160":1161},"announcements",[],"consentState",{"_287":1145,"_1146":20,"_1147":20,"_1148":112,"_1149":1150},"consentConfig",{"_269":270,"_271":272,"_273":274,"_275":276,"_277":278,"_279":280,"_281":282,"_283":284,"_285":286,"_287":288},"showContactAdmin",1794308778901,[],[],[264,265,266,267,268],{"_33":48,"_35":56,"_37":54,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":55,"_37":54,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":49,"_37":54,"_39":-5,"_41":-5,"_43":44},{"_33":45,"_35":46,"_37":38,"_39":40,"_41":47,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_43":44},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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