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

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:	Not Applicable.

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TAO Pretrained Mask2former

Model Overview

Description

Masked-attention Mask Transformer or Mask2Former is a segmentation architecture capable of addressing panoptic, instance or semantic image segmentation tasks. The masked attention is essential in efficiently extracting localized features by constraining cross-attention within predicted mask regions.

This model is ready for non-commercial use.

Reference

B. Cheng, I. Misra, A.G. Schwing, A. Kirillov, R. Girdhar: Masked-attention Mask Transformer for Universal Image Segmentation

License

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.

Model Architecture

Architecture Type: The model in this instance is an instance segmentor that takes color (RGB) images as inputs and generates segmentation masks and associated labels as outputs.\nNetwork Architecture: The backbone feature extractor of this model is Swin-T model pretrained on ImageNet dataset.

Input

Input Type: Image
Input Formats: Red, Green, Blue (RGB)
Other Properties Related to Input:** Minimum 32 x 32 Resolution required; no alpha channel or bits

Output

Output Type: Label, Mask and Score for each detected object in the input image.
Output Format: Label and Score: One-Dimensional (1D), Mask: Two Dimensional (2D)
Other Properties Related to Output**\n
- pred_classes: Batch size x Number of queries
- pred_masks: Batch size x Number of queries x Height x Width
- pred_scores: Batch size x Number of queries
\n

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 instance segmentation.

It is intended for training and fine-tune using Train Adapt Optimize (TAO) Toolkit and the users' dataset of object detection. 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 Mask2former model. For more information on the experiment spec file, see the TAO Toolkit User Guide.

model:\n  mode: \"instance\"\n  backbone:\n    type: \"swin\"\n    swin:\n      type: \"tiny\"\n      window_size: 7\n      ape: False\n      pretrain_img_size: 224\n  mask_former:\n    num_object_queries: 100\n  sem_seg_head:\n    norm: \"GN\"\n    num_classes: 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

mask2former_swint_trainable_v1.0 - Pre-trained Mask2former model for finetuning or inference.
mask2former_swint_deployable_v1.0 - Pre-trained Mask2former model deployable to deepstream.

Training and Evaluation Datasets

Training Datasets

Link: https://cocodataset.org/

Data Collection Method by dataset: Unknown

Labeling Method by dataset: Human

Properties: The COCO dataset contains 118K training images and corresponding annotation files. The annotation includes bounding boxes and segmentation masks of the 80 thing categories. The categories were mapped to a single category or \"object\" to train the binary instance segmentation model.

Evaluation Datasets

Link: https://cocodataset.org/

Data Collection Method by dataset: Unknown

Labeling Method by dataset: Human

Properties: The COCO dataset contains 5K validation images and corresponding annotation files. The annotation includes bounding boxes and segmentation masks of the 80 thing categories. The categories were mapped to a single category or \"object\" to train the binary instance segmentation model.

Performance

Evaluation Data

We test the Mask2former model on the modified COCO 2017 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

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

model	Precision	mIoU
Mask2former	FP16	0.96

Inference

Engine: Tensor(RT)
\nTest Hardware:

A2
A30
DGX H100
DGX A100
DGX H100
JAO 64GB
Jetson AGX Xavier
L4
L40
NVIDIA T4
Orin
Orin Nano 8GB
Orin NX
Orin NX16GB
T4
Xavier NX

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\">

Platform	BS	FPS
AGX Orin 64GB	8	17.53
Jetson Orin 16GB	8	7.19
Jetson Nano 8GB	8	2.13
T4	16	23.54
A30	16	73.94
A2	16	14.43
L4	16	35.27
L40	16	104.42
RTX4090	16	122.55
A100	16	147.11
H100	16	251.99

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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The masked attention is essential in efficiently extracting localized features by constraining cross-attention within predicted mask regions.\n\nThis model is ready for non-commercial use.\n\n## Reference\n\n- B. Cheng, I. Misra, A.G. Schwing, A. Kirillov, R. Girdhar: Masked-attention Mask Transformer for Universal Image Segmentation\n\n## License \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### Model Architecture \n**Architecture Type:** The model in this instance is an instance segmentor that takes color (RGB) images as inputs and generates segmentation masks and associated labels as outputs. \n**Network Architecture:** The backbone feature extractor of this model is Swin-T model pretrained on ImageNet dataset.\n\n### Input\n- Input Type: Image\n- Input Formats: Red, Green, Blue (RGB)\n- Other Properties Related to Input:** Minimum 32 x 32 Resolution required; no alpha channel or bits\n\n### Output\n- Output Type: Label, Mask and Score for each detected object in the input image.\n- Output Format: Label and Score: One-Dimensional (1D), Mask: Two Dimensional (2D)\n- Other Properties Related to Output**\n - pred_classes: Batch size x Number of queries\n - pred_masks: Batch size x Number of queries x Height x Width\n - pred_scores: Batch size x Number of queries\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 instance segmentation.\n\nIt is intended for training and fine-tune using Train Adapt Optimize (TAO) Toolkit and the users' dataset of object detection. 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 Mask2former 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\nmodel:\n mode: \"instance\"\n backbone:\n type: \"swin\"\n swin:\n type: \"tiny\"\n window_size: 7\n ape: False\n pretrain_img_size: 224\n mask_former:\n num_object_queries: 100\n sem_seg_head:\n norm: \"GN\"\n num_classes: 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- **mask2former_swint_trainable_v1.0** - Pre-trained Mask2former model for finetuning or inference.\n- **mask2former_swint_deployable_v1.0** - Pre-trained Mask2former model deployable to deepstream.\n\n## Training and Evaluation Datasets\n\n### Training Datasets\n\n **Link:** https://cocodataset.org/\n\n **Data Collection Method by dataset:** Unknown\n\n **Labeling Method by dataset:** Human\n\n **Properties:** The COCO dataset contains 118K training images and corresponding annotation files. The annotation includes bounding boxes and segmentation masks of the 80 thing categories. The categories were mapped to a single category or \"object\" to train the binary instance segmentation model.\n\n### Evaluation Datasets\n\n **Link:** https://cocodataset.org/\n\n **Data Collection Method by dataset:** Unknown\n\n **Labeling Method by dataset:** Human\n\n **Properties:** The COCO dataset contains 5K validation images and corresponding annotation files. The annotation includes bounding boxes and segmentation masks of the 80 thing categories. The categories were mapped to a single category or \"object\" to train the binary instance segmentation model.\n\n## Performance\n\n### Evaluation Data \n\nWe test the Mask2former model on the modified COCO 2017 validation dataset.\n\n### Methodology and KPI\nThe KPI for the evaluation data are reported below. \n\n|model|Precision|mIoU|\n|---|---|---|\n|Mask2former|FP16|0.96|\n\n## Inference \n\n**Engine:** Tensor(RT)
\n**Test Hardware:**
\n- A2\n- A30\n- DGX H100\n- DGX A100\n- DGX H100\n- JAO 64GB\n- Jetson AGX Xavier\n- L4\n- L40\n- NVIDIA T4\n- Orin\n- Orin Nano 8GB\n- Orin NX\n- Orin NX16GB\n- T4\n- Xavier NX\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| Platform | BS | FPS |\n|------------------|----|-------|\n| AGX Orin 64GB | 8 | 17.53 |\n| Jetson Orin 16GB | 8 | 7.19 |\n| Jetson Nano 8GB | 8 | 2.13 |\n| T4 | 16 | 23.54 |\n| A30 | 16 | 73.94 |\n| A2 | 16 | 14.43 |\n| L4 | 16 | 35.27 |\n| L40 | 16 | 104.42|\n| RTX4090 | 16 | 122.55|\n| A100 | 16 | 147.11|\n| H100 | 16 | 251.99|\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","Mask2Former",["D",1731434349444],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/mask2former","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: | Not Applicable.|","explainability","| Field | Response |\n| -- | -- |\n| Intended Applications & Domains: | Segmenting (Semantic, Instance, Panoptic) |\n| Model Type: | Object Segmentation |\n| Intended Users: | This model is intended for developers building object segmentation applications. |\n| Output: | Segmentation Masks, Category Labels |\n| Describe how the model works: | This model predicts masks and the associated labels. |\n| Technical Limitations: | The model may have difficulties in non-Flickr style data like medical, satellite, and industrial data. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Mean Intersection over Union (mIoU) |\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 personally-identifiable information (PII)? 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