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TAO Pretrained Mask Auto Labeler (MAL)

What is Train Adapt Optimize (TAO) Toolkit?

Train Adapt Optimize (TAO) Toolkit is a Python-based AI toolkit for taking purpose-built pre-trained AI models and customizing them with your own data. TAO adapts popular network architectures and backbones to your data, allowing you to train, fine tune, prune, and export highly optimized and accurate AI models for edge deployment.

Pre-trained models accelerate the AI training process and reduce costs associated with large scale data collection, labeling, and training models from scratch. Transfer learning with pre-trained models can be used for AI applications in smart cities, retail, healthcare, industrial inspection, and more.

Build end-to-end services and solutions for transforming pixels and sensor data to actionable insights using TAO DeepStream SDK and TensorRT. These models are suitable for object detection, classification, and segmentation.

Model Overview

Mask Auto-Labeler (MAL) is a high-quality Transformer-based mask auto-labeling framework for instance segmentation using only box annotations. MAL takes box-cropped images as inputs and conditionally generates their mask pseudo-labels. This model card contains pre-trained weights for MAL trained on the COCO dataset to facilitate transfer learning through TAO Toolkit.

Model Architecture

The model in this instance is an instance segmentator that takes color (RGB) images and bounding boxes as inputs and generates segmentation masks as outputs. The backbone feature extractor of this model is ViT-MAE-Base that were pre-trained on the ImageNet dataset.

Training

This model was trained using the MAL entrypoint in TAO. The training algorithm optimizes the network to minimize the Multiple Instance Learning (MIL) loss and the Conditional Random Field (CRF) loss for the objects.

Training Data

MAL was trained on the COCO 2017 dataset. The COCO dataset contains 118K training images and 5K validation images and corresponding annotation files. The annotation contains bounding boxes for 80 object categories.

Performance

Evaluation Data

We test the MAL model on the COCO 2017 validation dataset.

Methodology and KPI

The key performance indicator is the mean Intersection over Union (mIoU) following the standard evaluation protocol for segmentation. 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

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

model	precision	mIoU
mal_vit_base	FP32	0.788
mal_fan_base	FP32	0.782

How to Use This Model

These models need to be used with NVIDIA Hardware and Software. For Hardware, the models can run on any NVIDIA GPU. These models can only be used with TAO Toolkit.

The primary use case for these models is weakly supervised instance segmentation and auto-labeling.

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.

Input

Image: B X 3 X 512 X 512 (B C H W)
Label: Bounding box groundtruth in COCO format

Output

Segmentation mask for each detected objects in the input image.

Instructions to Use Pretrained Models with TAO

To use these models as pretrained weights for transfer learning, use the snippet below as atemplate for the model and train component of the experiment spec file to train a Deformable DETR model. For more information on the experiment spec file, please refer to the TAO Toolkit User Guide.

checkpoint: /path/to/vit-mae-base-16epoch=10.pth\nmodel:\n  arch: 'vit-mae-base/16'\n

Limitations

MAL was trained on the COCO dataset with 80 object categories. Hence, the model may not perform well on data from completely different domains. We recommend further fine tuning on the target domain to get higher mIoU.

Model Versions

mal_vit_base_trainable_v1.0 - Pre-trained ViT-base MAL model for finetune.
mal_fan_base_trainable_v1.0 - Pre-trained FAN-base MAL model for finetune.

Reference

Citations

Zhou D., Yu Z., Xie E., Xiao C., Anandkumar A., Feng J., Alvarez J.M.:Understanding The Robustness in Vision Transformers
Lan, S., Yang, X., Yu Z., Wu Z., Alvarez J.M., Anandkumar A.: Vision Transformers Are Good Mask Auto-Labelers
He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition
Hatamizadeh, A., Yin, H., Heinrich, G., Kautz, J., Molchanov, P.: Global Context Vision Transformers
Lin, T., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollar, P., Zitnick, C.: Microsoft COCO: Common Objects in Context

Using TAO Pre-trained Models

Get TAO Container
Get other purpose-built models from NGC model registry:\n
- TrafficCamNet
- DashCamNet
- FaceDetectIR
- VehicleMakeNet
- VehicleTypeNet
- ActionRecognitionNet
- PoseClassificationNet
- ReIdentificationNet
\n

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.

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’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developers to ensure that it meets the requirements for the relevant industry and use case, that the necessary instructions and documentation are provided to understand error rates, confidence intervals, and results, and that the model is being used under the conditions and in the manner intended.

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TAO adapts popular network architectures and backbones to your data, allowing you to train, fine tune, prune, and export highly optimized and accurate AI models for edge deployment.\n\nPre-trained models accelerate the AI training process and reduce costs associated with large scale data collection, labeling, and training models from scratch. Transfer learning with pre-trained models can be used for AI applications in smart cities, retail, healthcare, industrial inspection, and more.\n\nBuild end-to-end services and solutions for transforming pixels and sensor data to actionable insights using TAO [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) and [TensorRT](https://developer.nvidia.com/tensorrt). These models are suitable for object detection, classification, and segmentation.\n\n## Model Overview \n\nMask Auto-Labeler (MAL) is a high-quality Transformer-based mask auto-labeling framework for instance segmentation using only box annotations. MAL takes box-cropped images as inputs and conditionally generates their mask pseudo-labels. This model card contains pre-trained weights for [MAL](https://arxiv.org/abs/2301.03992) trained on the COCO dataset to facilitate transfer learning through TAO Toolkit.\n\n## Model Architecture \n\nThe model in this instance is an instance segmentator that takes color (RGB) images and bounding boxes as inputs and generates segmentation masks as outputs. The backbone feature extractor of this model is ViT-MAE-Base that were pre-trained on the ImageNet dataset.\n\n## Training \n\nThis model was trained using the MAL entrypoint in TAO. The training algorithm optimizes the network to minimize the Multiple Instance Learning (MIL) loss and the Conditional Random Field (CRF) loss for the objects.\n\n### Training Data \n\nMAL was trained on the [COCO 2017 dataset](https://cocodataset.org/). The COCO dataset contains 118K training images and 5K validation images and corresponding annotation files. The annotation contains bounding boxes for 80 object categories.\n\n## Performance\n\n### Evaluation Data \n\nWe test the MAL model on the COCO 2017 validation dataset.\n\n### Methodology and KPI\n\nThe key performance indicator is the mean Intersection over Union (mIoU) following the standard evaluation protocol for segmentation. The KPI for the evaluation data are reported below. \n\n|model|precision|mIoU|\n|---|---|---|\n|mal_vit_base|FP32|0.788|\n|mal_fan_base|FP32|0.782|\n\n## How to Use This Model\n\nThese models need to be used with NVIDIA Hardware and Software. For Hardware, the models can run on any NVIDIA GPU. These models can only be used with [TAO Toolkit](https://developer.nvidia.com/tao-toolkit).\n\nThe primary use case for these models is weakly supervised instance segmentation and auto-labeling.\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### Input\n\n- Image: B X 3 X 512 X 512 (B C H W)\n- Label: Bounding box groundtruth in COCO format\n\n### Output\n\nSegmentation mask for each detected objects in the input image.\n\n### Instructions to Use Pretrained Models with TAO\n\nTo use these models as pretrained weights for transfer learning, use the snippet below as atemplate for the `model` and `train` component of the experiment spec file to train a Deformable DETR model. For more information on the experiment spec file, please refer to the [TAO Toolkit User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\n```yaml\ncheckpoint: /path/to/vit-mae-base-16epoch=10.pth\nmodel:\n arch: 'vit-mae-base/16'\n```\n\n## Limitations\n\nMAL was trained on the COCO dataset with 80 object categories. Hence, the model may not perform well on data from completely different domains. We recommend further fine tuning on the target domain to get higher mIoU.\n\n## Model Versions\n\n- **mal_vit_base_trainable_v1.0** - Pre-trained ViT-base MAL model for finetune.\n- **mal_fan_base_trainable_v1.0** - Pre-trained FAN-base MAL model for finetune.\n\n## Reference\n\n### Citations \n\n- Zhou D., Yu Z., Xie E., Xiao C., Anandkumar A., Feng J., Alvarez J.M.:Understanding The Robustness in Vision Transformers\n- Lan, S., Yang, X., Yu Z., Wu Z., Alvarez J.M., Anandkumar A.: Vision Transformers Are Good Mask Auto-Labelers\n- He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition\n- Hatamizadeh, A., Yin, H., Heinrich, G., Kautz, J., Molchanov, P.: Global Context Vision Transformers\n- Lin, T., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollar, P., Zitnick, C.: Microsoft COCO: Common Objects in Context\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 NGC model registry:\n - [TrafficCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:trafficcamnet)\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 - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [PoseClassificationNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/poseclassificationnet)\n - [ReIdentificationNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/reidentificationnet)\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## 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’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developers to ensure that it meets the requirements for the relevant industry and use case, that the necessary instructions and documentation are provided to understand error rates, confidence intervals, and results, and that the model is being used under the conditions and in the manner intended.","displayName","Mask Auto Label",["D",1774299173473],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/mask_auto_label","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",1440075960,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","| Field | Response |\n| -- | -- |\n| Licensing: | [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) |","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[191,192,193,194,195,196,197,198,199,200,201,202,203,204,205],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_157":245,"_207":246,"_208":247,"_210":211},{"_157":241,"_207":242,"_208":243,"_210":211},{"_157":239,"_207":239,"_208":240,"_210":211},{"_157":237,"_207":237,"_208":238,"_210":211},{"_157":234,"_207":235,"_208":236,"_210":211},{"_157":230,"_207":231,"_208":232,"_210":211},{"_157":228,"_207":228,"_208":229,"_210":211},{"_157":226,"_207":226,"_208":227,"_210":211},{"_157":224,"_207":224,"_208":225,"_210":211},{"_157":222,"_207":222,"_208":223,"_210":211},{"_157":220,"_207":220,"_208":221,"_210":211},{"_157":217,"_207":217,"_208":218,"_210":211},{"_157":215,"_207":215,"_208":216,"_210":211},{"_157":213,"_207":213,"_208":214,"_210":211},{"_157":206,"_207":206,"_208":209,"_210":211},"Transfer Learning Toolkit","value","filter",{"_212":211,"_207":206},"category","label","field","Transfer Learning",{"_212":211,"_207":213},"TLT",{"_212":211,"_207":215},"TAO Toolkit",{"_212":219,"_207":217},"framework","TAO",{"_212":211,"_207":220},"Smart Infrastructure",{"_212":211,"_207":222},"Smart City",{"_212":211,"_207":224},"NSPECT-GYNA-NPOW",{"_212":211,"_207":226},"NSPECT-FY5H-V6CO",{"_212":211,"_207":228},"Metropolis","pltfm_metropolis",{"_212":233,"_207":231},"platform","DeepStream","pltfm_deepstream",{"_212":233,"_207":235},"Deep Learning",{"_212":211,"_207":237},"CV",{"_212":211,"_207":239},"Computer Vision","soln_computer_vision",{"_212":244,"_207":242},"technology","AI","soln_ai",{"_212":244,"_207":246},"appConfig",{"_1425":1426,"_282":1427},"ngcApps",[1378],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1359":1360,"_1361":1362,"_1363":1364,"_1365":1366,"_1367":260,"_77":287,"_1368":1369,"_1370":44,"_1371":108,"_1372":108,"_1373":1374,"_1375":108,"_1376":1377},"amplitude",{"_1356":1357,"_959":-7,"_1358":20},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1350":1351,"_1352":1353,"_1354":1355},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","https://api.ngc.nvidia.com","ngcHelmOrigin","https://helm.ngc.nvidia.com","origin","https://catalog.ngc.nvidia.com","user","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1234":111,"_1235":1236,"_1237":1238,"_1239":1240},"announcements",[],"consentState",{"_325":1224,"_1225":108,"_1226":20,"_1227":20,"_1228":1229},"consentConfig",{"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326},"showContactAdmin",1784200846782,[],[],[301,302,303,304,305,306],{"_33":48,"_35":53,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":52,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":51,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":49,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":45,"_35":46,"_37":38,"_39":40,"_41":47,"_43":44},{"_33":34,"_35":36,"_37":50,"_39":-5,"_41":-5,"_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. 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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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