TAO Commercial Pretrained ConvNextv2

Model Overview

Description

ConvNextv2 is a model that can be used as a backbone for most of the popular computer vision tasks such as classification, segmentation and detection.

ConvNextv2 is a modern convolutional network architecture, codesigned with a fully convolutional masked autoencoder framework. It has shown improved performance over the pure\nConvNets on various recognition benchmarks, including classification, detection, and segmentation.

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

The primary use case for these models is feature extraction for downstream tasks like classification, object detection and segmentation.

Release Date

NGC [06/13/2025]

Reference

S. Woo, S. Debnath, R. Hu, X. Chen, Z. Liu, I.S Kweon, S Xie: ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Model Architecture

Architecture Type: Convolution Neural Network (CNN)\nNetwork Architecture: ConvNextv2-nano, ConvNext-tiny, ConvNextv2-large.

Input

Input Type: Image
Input Formats: Red, Green, Blue (RGB)
Input Parameters: Two-Dimensional (2D)
Other Properties Related to Input: Image of dimensions: 224 X 224 X 3 (H x W x C); no alpha channel or bits

Output

Output Type(s): Embedding - Float tensor\nOutput Format: 2D Vector\nOutput Parameters: Two-Dimensional (2D)

Other Properties Related to Output: Batch size X 1000

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.

How to Use This Model

This model needs to be used with NVIDIA Hardware and Software. For Hardware, the model can run on any NVIDIA GPU with sufficient memory (>12G). This model can only be used with TAO Toolkit.

The primary use case for these models is feature extraction.

It is intended for training and fine-tune using Train Adapt Optimize (TAO) Toolkit. High fidelity models can be trained to new use cases. A Jupyter notebook is available as a part of TAO container and can be used to re-train.

Instructions to Use Pretrained Models with TAO

To use these models as pretrained weights for transfer learning, use the following snippet as a template for the model and train components of the experiment spec file to train a ConvNextv2 model. For more information on the experiment spec file, see the TAO Toolkit User Guide.

train:\n  stage: \"finetune\"\n  batch_size: 64\n  pretrained_model_path: /path/to/convnextv2_checkpoint.pth\n  precision: 'bf16-mixed'\n  num_gpus: 8\n  checkpoint_interval: 10\n  validation_interval: 10\n  num_epochs: 100\n  smoothing: 0.1\nmodel:\n  arch: convnextv2_large\n  num_classes: 1000\n  drop_path_rate: 0.1\n

Software Integration

Runtime Engine:

TAO 5.5.0

Supported Hardware Microarchitecture Compatibility:

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

[Preferred/Supported] Operating System(s):

Linux

Model Versions

convnextv2_nano_trainable_v1.0 - Pre-trained ConvNextv2-nano model for finetuning.
convnextv2_tiny_trainable_v1.0 - Pre-trained ConvNextv2-tiny model for finetuning.
convnextv2_large_trainable_v1.0 - Pre-trained ConvNextv2-large model for finetuning.

Training, Testing and Evaluation Datasets

Training Datasets

Data Collection Method by dataset:

Automated

Labeling Method by dataset:

Automated

Properties:

\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
NV Internal Data	5M

Testing Datasets

Data Collection Method by dataset:

Automated

Labeling Method by dataset:

Automated

Properties:

\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
NV Internal Data	50,000

Evaluation Datasets

Link: https://www.image-net.org/

Data Collection Method by dataset:

Hybrid: Automated, Human

Labeling Method by dataset:

Hybrid: Automated, Human

Properties:
\n50,000 validation images from ImageNet dataset

Performance

Evaluation Data

We tested the ConvNextv2 models on the ImageNet 1k validation dataset.

Methodology and KPI

The KPI for the evaluation data are reported below.

\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\">

model	Precision	Zero-shot KNN
ConvNextv2-nano	FP32	0.69
ConvNextv2-tiny	FP32	0.70
ConvNextv2-large	FP32	0.70

Inference

Engine: Tensor(RT)
\nTest Hardware:

AGX Orin 64GB
Orin Nano 8GB
Orin NX 16GB

The inference is run on the provided unpruned model at FP16 precision. The inference performance is run using trtexec on Jetson AGX Xavier, Xavier NX, Orin, Orin NX and NVIDIA T4, and Ampere GPUs. The Jetson devices are running at Max-N configuration for maximum GPU frequency. The performance shown here is the inference only performance. The end-to-end performance with streaming video data might vary depending on other bottlenecks in the hardware and software.

\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\n\n\n\n\n

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

Model	Platform	BS	FPS
ConvNextv2-nano	AGX Orin 64GB	16	834
ConvNextv2-nano	Jetson Orin 16GB	16	317
ConvNextv2-nano	Jetson Nano 8GB	8	212
ConvNextv2-tiny	AGX Orin 64GB	16	533
ConvNextv2-tiny	Jetson Orin 16GB	16	197
ConvNextv2-tiny	Jetson Nano 8GB	8	135
ConvNextv2-large	AGX Orin 64GB	16	139
ConvNextv2-large	Jetson Orin 16GB	16	43
ConvNextv2-large	Jetson Nano 8GB	16	35

Using TAO Pre-trained Models

Get TAO Container
Get other purpose-built models from the NGC model registry:\n
- TrafficCamNet
- PeopleNet
- PeopleNet
- PeopleNet-Transformer
- DashCamNet
- FaceDetectIR
- VehicleMakeNet
- VehicleTypeNet
- PeopleSegNet
- PeopleSemSegNet
- License Plate Detection
- License Plate Recognition
- Gaze Estimation
- Facial Landmark
- Heart Rate Estimation
- Gesture Recognition
- Emotion Recognition
- FaceDetect
- 2D Body Pose Estimation
- ActionRecognitionNet
- ActionRecognitionNet
- PoseClassificationNet
- People ReIdentification
- PointPillarNet
- CitySegFormer
- Retail Object Detection
- Retail Object Embedding
- Optical Inspection
- Optical Character Detection
- Optical Character Recognition
- PCB Classification
- PeopleSemSegFormer
- LPDNet
- License Plate Recognition
- Gaze Estimation
- Facial Landmark
- Heart Rate Estimation
- Gesture Recognition
- Emotion Recognition
- FaceDetect
- 2D Body Pose Estimation
- ActionRecognitionNet
- ActionRecognitionNet
- PoseClassificationNet
- People ReIdentification
- PointPillarNet
- CitySegFormer
- Retail Object Detection
- Retail Object Embedding
- Optical Inspection
- Optical Character Detection
- Optical Character Recognition
- PCB Classification
- PeopleSemSegFormer
\n

Technical Blogs

Train like a ‘pro’ without being an AI expert using TAO AutoML
Create Custom AI models using NVIDIA TAO Toolkit with Azure Machine Learning
Developing and Deploying AI-powered Robots with NVIDIA Isaac Sim and NVIDIA TAO
Learn endless ways to adapt and supercharge your AI workflows with TAO - Whitepaper
Customize Action Recognition with TAO and deploy with DeepStream
Read the two-part blog on training and optimizing 2D body pose estimation model with TAO - Part 1 | Part 2
Learn how to train a real-time License plate detection and recognition app with TAO and DeepStream.
Model accuracy is extremely important; learn how you can achieve state of the art accuracy for classification and object detection models using TAO.

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 supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Promise and the Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns here.

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It has shown improved performance over the pure\nConvNets on various recognition benchmarks, including classification, detection, and segmentation.\n\nThis model is ready for commercial use. \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\n\nGlobal\n\n### Use Case\n\nThe primary use case for these models is feature extraction for downstream tasks like classification, object detection and segmentation.\n\n### Release Date\n\nNGC [06/13/2025]\n\n## Reference\n\n- S. Woo, S. Debnath, R. Hu, X. Chen, Z. Liu, I.S Kweon, S Xie: ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders\n\n### Model Architecture \n**Architecture Type:** Convolution Neural Network (CNN)\n**Network Architecture:** ConvNextv2-nano, ConvNext-tiny, ConvNextv2-large.\n\n### Input\n- **Input Type:** Image\n- **Input Formats:** Red, Green, Blue (RGB)\n- **Input Parameters:** Two-Dimensional (2D)\n- **Other Properties Related to Input:** Image of dimensions: 224 X 224 X 3 (H x W x C); no alpha channel or bits\n\n### Output\n**Output Type(s):** Embedding - Float tensor\n**Output Format:** 2D Vector\n**Output Parameters:** Two-Dimensional (2D)\n- **Other Properties Related to Output:** Batch size X 1000\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## How to Use This Model\nThis model needs to be used with NVIDIA Hardware and Software. For Hardware, the model can run on any NVIDIA GPU with sufficient memory (>12G). This model can only be used with [TAO Toolkit](https://developer.nvidia.com/tao-toolkit).\n\nThe primary use case for these models is feature extraction.\n\nIt is intended for training and fine-tune using Train Adapt Optimize (TAO) Toolkit. High fidelity models can be trained to new use cases. A Jupyter notebook is available as a part of [TAO container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit-pytorch) and can be used to re-train.\n\n### Instructions to Use Pretrained Models with TAO\nTo use these models as pretrained weights for transfer learning, use the following snippet as a template for the `model` and `train` components of the experiment spec file to train a ConvNextv2 model. For more information on the experiment spec file, see the [TAO Toolkit User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\n```yaml\ntrain:\n stage: \"finetune\"\n batch_size: 64\n pretrained_model_path: /path/to/convnextv2_checkpoint.pth\n precision: 'bf16-mixed'\n num_gpus: 8\n checkpoint_interval: 10\n validation_interval: 10\n num_epochs: 100\n smoothing: 0.1\nmodel:\n arch: convnextv2_large\n num_classes: 1000\n drop_path_rate: 0.1\n```\n### Software Integration\n**Runtime Engine:**\n * TAO 5.5.0\n\n**Supported Hardware Microarchitecture Compatibility:**\n * NVIDIA Ampere\n * NVIDIA Blackwell\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\n### Model Versions\n- **convnextv2_nano_trainable_v1.0** - Pre-trained ConvNextv2-nano model for finetuning.\n- **convnextv2_tiny_trainable_v1.0** - Pre-trained ConvNextv2-tiny model for finetuning.\n- **convnextv2_large_trainable_v1.0** - Pre-trained ConvNextv2-large model for finetuning.\n\n## Training, Testing and Evaluation Datasets\n\n### Training Datasets\n\n **Data Collection Method by dataset:**
\n * Automated
\n\n **Labeling Method by dataset:**
\n * Automated
\n\n **Properties:**
\n\n | Dataset | No. of Images |\n |--|--|\n |NV Internal Data| 5M | \n\n### Testing Datasets\n\n **Data Collection Method by dataset:**
\n * Automated
\n\n **Labeling Method by dataset:**
\n * Automated
\n\n **Properties:**
\n\n | Dataset | No. of Images |\n |--|--|\n |NV Internal Data| 50,000 |\n\n### Evaluation Datasets\n\n **Link:** [https://www.image-net.org/](https://www.image-net.org/)\n\n **Data Collection Method by dataset:**
\n * Hybrid: Automated, Human
\n\n **Labeling Method by dataset:**
\n * Hybrid: Automated, Human
\n\n **Properties:**
\n 50,000 validation images from [ImageNet dataset](https://www.image-net.org/download.php) \n\n## Performance\n\n### Evaluation Data \n\nWe tested the ConvNextv2 models on the ImageNet 1k validation dataset.\n\n### Methodology and KPI\nThe KPI for the evaluation data are reported below. \n\n|model|Precision|Zero-shot KNN|\n|---|---|---|\n|ConvNextv2-nano|FP32|0.69|\n|ConvNextv2-tiny|FP32|0.70|\n|ConvNextv2-large|FP32|0.70|\n\n## Inference \n\n**Engine:** Tensor(RT)
\n**Test Hardware:**
\n- AGX Orin 64GB\n- Orin Nano 8GB\n- Orin NX 16GB\n\nThe inference is run on the provided unpruned model at FP16 precision. The inference performance is run using [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/main/samples/trtexec) on Jetson AGX Xavier, Xavier NX, Orin, Orin NX and NVIDIA T4, and Ampere GPUs. The Jetson devices are running at Max-N configuration for maximum GPU frequency. The performance shown here is the inference only performance. The end-to-end performance with streaming video data might vary depending on other bottlenecks in the hardware and software.\n\n| Model | Platform | BS | FPS |\n|------------------|------------------|----|-------|\n| ConvNextv2-nano | AGX Orin 64GB | 16 | 834 |\n| ConvNextv2-nano | Jetson Orin 16GB | 16 | 317 |\n| ConvNextv2-nano | Jetson Nano 8GB | 8 | 212 |\n| ConvNextv2-tiny | AGX Orin 64GB | 16 | 533 |\n| ConvNextv2-tiny | Jetson Orin 16GB | 16 | 197 |\n| ConvNextv2-tiny | Jetson Nano 8GB | 8 | 135 |\n| ConvNextv2-large | AGX Orin 64GB | 16 | 139 |\n| ConvNextv2-large | Jetson Orin 16GB | 16 | 43 |\n| ConvNextv2-large | Jetson Nano 8GB | 16 | 35 |\n\n\n## Using TAO Pre-trained Models \n\n- Get [TAO Container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit)\n- Get other purpose-built models from the NGC model registry:\n - [TrafficCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:trafficcamnet)\n - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)\n - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)\n - [PeopleNet-Transformer](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet_transformer)\n - [DashCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:dashcamnet)\n - [FaceDetectIR](https://ngc.nvidia.com/catalog/models/nvidia:tao:facedetectir)\n - [VehicleMakeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehiclemakenet)\n - [VehicleTypeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehicletypenet)\n - [PeopleSegNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplesegnet)\n - [PeopleSemSegNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplesemsegnet)\n - [License Plate Detection](https://ngc.nvidia.com/catalog/models/nvidia:tao:lpdnet)\n - [License Plate Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:lprnet)\n - [Gaze Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:gazenet)\n - [Facial Landmark](https://ngc.nvidia.com/catalog/models/nvidia:tao:fpenet)\n - [Heart Rate Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:heartratenet)\n - [Gesture Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:gesturenet)\n - [Emotion Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:emotionnet)\n - [FaceDetect](https://ngc.nvidia.com/catalog/models/nvidia:tao:facenet)\n - [2D Body Pose Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:bodyposenet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [PoseClassificationNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:poseclassificationnet)\n - [People ReIdentification](https://ngc.nvidia.com/catalog/models/nvidia:tao:reidentificationnet)\n - [PointPillarNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pointpillarnet)\n - [CitySegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/citysemsegformer)\n - [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection)\n - [Retail Object Embedding](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_recognition)\n - [Optical Inspection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/optical_inspection)\n - [Optical Character Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocdnet)\n - [Optical Character Recognition](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocrnet)\n - [PCB Classification](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pcb_classification)\n - [PeopleSemSegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplesemsegformer)\n - [LPDNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:lpdnet)\n - [License Plate Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:lprnet)\n - [Gaze Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:gazenet)\n - [Facial Landmark](https://ngc.nvidia.com/catalog/models/nvidia:tao:fpenet)\n - [Heart Rate Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:heartratenet)\n - [Gesture Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:gesturenet)\n - [Emotion Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:emotionnet)\n - [FaceDetect](https://ngc.nvidia.com/catalog/models/nvidia:tao:facenet)\n - [2D Body Pose Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:bodyposenet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [PoseClassificationNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:poseclassificationnet)\n - [People ReIdentification](https://ngc.nvidia.com/catalog/models/nvidia:tao:reidentificationnet)\n - [PointPillarNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pointpillarnet)\n - [CitySegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/citysemsegformer)\n - [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection)\n - [Retail Object Embedding](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_recognition)\n - [Optical Inspection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/optical_inspection)\n - [Optical Character Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocdnet)\n - [Optical Character Recognition](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocrnet)\n - [PCB Classification](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pcb_classification)\n - [PeopleSemSegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplesemsegformer)\n\n## Technical Blogs \n\n- [Train like a ‘pro’ without being an AI expert using TAO AutoML](https://developer.nvidia.com/blog/training-like-an-ai-pro-using-tao-automl/)\n- [Create Custom AI models using NVIDIA TAO Toolkit with Azure Machine Learning ](https://developer.nvidia.com/blog/creating-custom-ai-models-using-nvidia-tao-toolkit-with-azure-machine-learning/)\n- [Developing and Deploying AI-powered Robots with NVIDIA Isaac Sim and NVIDIA TAO](https://developer.nvidia.com/blog/developing-and-deploying-ai-powered-robots-with-nvidia-isaac-sim-and-nvidia-tao/)\n- Learn endless ways to adapt and supercharge your AI workflows with TAO - [Whitepaper](https://developer.nvidia.com/tao-toolkit-usecases-whitepaper/1-introduction)\n- [Customize Action Recognition with TAO and deploy with DeepStream](https://developer.nvidia.com/blog/developing-and-deploying-your-custom-action-recognition-application-without-any-ai-expertise-using-tao-and-deepstream/)\n- Read the two-part blog on training and optimizing 2D body pose estimation model with TAO - [Part 1](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-1) | [Part 2](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-2)\n- Learn how to train a [real-time License plate detection and recognition app](https://developer.nvidia.com/blog/creating-a-real-time-license-plate-detection-and-recognition-app) with TAO and DeepStream.\n- Model accuracy is extremely important; learn how you can achieve [state of the art accuracy for classification and object detection models](https://developer.nvidia.com/blog/preparing-state-of-the-art-models-for-classification-and-object-detection-with-tao-toolkit/) using TAO.\n\n## Suggested Reading \n\n- More information on TAO Toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/tao-toolkit)\n- Refer to the [TAO documentation](https://docs.nvidia.com/tao/tao-toolkit/index.html)\n- Read the [TAO Toolkit Quick Start Guide](https://docs.nvidia.com/tao/tao-toolkit/text/tao_quick_start_guide.html) and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html).\n- If you have any questions or feedback, please refer to the discussions on the [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17)\n- Deploy your models for video analytics application using the [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk).\n- Deploy your models in [Riva](https://developer.nvidia.com/riva) for ConvAI use case.\n\n## Ethical Considerations \n\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 supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Promise and the Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","Pretrained ConvNeXtV2",["D",1752711227592],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/pretrained_convnextv2","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 |\n| Measures taken to mitigate against unwanted bias: | None |","explainability","| Field | Response |\n| -- | -- |\n| Intended Applications & Domains: | Generating image embedding for image classification, segmentation, and detection tasks. |\n| Model Type: | Object Classification |\n| Intended Users: | This model is intended for developers building CV applications. |\n| Output: | Embedding |\n| Describe how the model works: | This model takes RGB images as input and outputs classifier embeddings. |\n| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable |\n| Technical Limitations: | This model may experience challenges with detection and classification for non-Flicker style data, such as medical, satellite and industrial data. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Zero-shot KNN |\n| Potential Known Risks: | This model may inaccurately predict object class. |\n| Licensing: | [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) |","privacy","| Field | Response |\n| -- | -- |\n| Generatable or reverse engineerable personal data? | None |\n| Personal data used to create this model? | None Known |\n| How often is dataset reviewed? | \tBefore Release |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | Yes |\n| Applicable NVIDIA Privacy Policy\t| [https://www.nvidia.com/en-us/about-nvidia/privacy-policy/](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) |","safetyAndSecurity","| Field | Response |\n| -- | -- |\n| Model Application Field(s): | Embedding Generation and Classification |\n| Describe the life-critical application (if present). | Not applicable |\n| Use Case Restrictions: | Abide by [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) |\n| Model 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",[250,251],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_214":259,"_253":259,"_254":260,"_256":257},{"_214":252,"_253":252,"_254":255,"_256":257},"NSPECT-K2O1-WCT9","value","filter",{"_258":257,"_253":252},"category","label","field","NSPECT-FY5H-V6CO",{"_258":257,"_253":259},{"_107":1434,"_109":1435},"ngcApps",[1387],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1368":1369,"_1370":1371,"_1372":1373,"_1374":1375,"_1376":272,"_139":296,"_1377":1378,"_1379":55,"_1380":86,"_1381":86,"_1382":1383,"_1384":86,"_1385":1386},"amplitude",{"_1365":1366,"_969":-7,"_1367":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1359":1360,"_1361":1362,"_1363":1364},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1244":169,"_1245":1246,"_1247":1248,"_1249":1250},"announcements",[],"consentState",{"_335":1234,"_1235":86,"_1236":30,"_1237":30,"_1238":1239},"consentConfig",{"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336},"showContactAdmin",1784188047991,[],[],[310,311,312,313,314,315],{"_44":59,"_46":70,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":68,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":316,"_54":55},4,"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. 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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,[486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520],[359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375],{"_376":482,"_214":482,"_168":169,"_349":483},{"_376":477,"_214":477,"_168":169,"_349":478},{"_376":473,"_214":473,"_168":169,"_349":474},{"_376":468,"_214":468,"_168":169,"_349":469},{"_376":463,"_214":463,"_168":169,"_349":464},{"_376":451,"_214":451,"_168":169,"_349":452},{"_376":439,"_214":439,"_168":169,"_349":440},{"_376":434,"_214":434,"_168":169,"_349":435},{"_376":423,"_214":423,"_168":169,"_349":424},{"_376":418,"_214":418,"_168":169,"_349":419},{"_376":413,"_214":413,"_168":169,"_349":414},{"_376":408,"_214":408,"_168":169,"_349":409},{"_376":403,"_214":403,"_168":169,"_349":404},{"_376":398,"_214":398,"_168":169,"_349":399},{"_376":393,"_214":393,"_168":169,"_349":394},{"_376":386,"_214":386,"_168":169,"_349":387},{"_376":377,"_214":377,"_168":169,"_349":378},"hostName","amazon-adsystem.com",[379,380],{"_78":385,"_382":377,"_383":30,"_168":384},{"_78":381,"_382":377,"_383":30,"_168":384},"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",[388,389],{"_78":392,"_382":386,"_383":30,"_168":391},{"_78":390,"_382":386,"_383":30,"_168":391},"IDE","This domain is owned by Doubleclick (Google). 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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. 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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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