TAO Pretrained Non-commercial Backbone for DINO

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.

DINO Based Object Detection

Object detection is a popular computer vision technique that can detect one or multiple objects in a frame. Object detection will recognize the individual objects in an image and places bounding boxes around the object. This model card contains pretrained weights that may be used as a starting point with the DINO object detection networks in Train Adapt Optimize (TAO) Toolkit to facilitate transfer learning.

It is trained on the ImageNet-1K. Following backbones are supported with DINO networks.

Supported Backbone:

resnet_50
gc_vit_xxtiny / gc_vit_xtiny / gc_vit_tiny / gc_vit_small / gc_vit_base / gc_vit_large / gc_vit_large_384
fan_tiny / fan_small / fan_base / fan_large

Model Versions

gcvit_xxtiny_imagenet1k - ImageNet1K pre-trained GCViT-xxTiny model for finetune.
gcvit_xtiny_imagenet1k - ImageNet1K pre-trained GCViT-xTiny model for finetune.
gcvit_tiny_imagenet1k - ImageNet1K pre-trained GCViT-Tiny model for finetune.
gcvit_small_imagenet1k - ImageNet1K pre-trained GCViT-Small model for finetune.
gcvit_base_imagenet1k - ImageNet1K pre-trained GCViT-Base model for finetune.
gcvit_large_imagenet1k - ImageNet1K pre-trained GCViT-Large model for finetune.
gcvit_large_imagenet22k_384 - ImageNet22k pre-trained GCViT-Large model for finetune.
fan_hybrid_tiny - ImageNet1k pre-trained FAN-Hybrid-Tiny model for finetune. (224 resolution)
fan_hybrid_small - ImageNet1k pre-trained FAN-Hybrid-Small model for finetune. (224 resolution)
fan_hybrid_base_in22k - ImageNet22k pre-trained FAN-Hybrid-Small model for finetune. (224 resolution)
fan_hybrid_base_in22k_1k - ImageNet22K pre-trained FAN-Hybrid-Base model finetuned on Imagenet1k. (224 resolution)
fan_hybrid_base_in22k_1k_384 - ImageNet22K pre-trained FAN-Hybrid-Base model finetuned on ImageNet-1k.
fan_hybrid_large_in22k - ImageNet22k pre-trained FAN-Hybrid-Large model for finetune. (224 resolution)
fan_hybrid_large_in22k_384 - ImageNet22K pre-trained FAN-Hybrid-Large model for finetune.(384 resolution)
fan_hybrid_large_in22k_1k - ImageNet22K pre-trained FAN-Hybrid-Base model for finetune. (224 resolution)
fan_hybrid_large_in22k_1k_384 - ImageNet22K pre-trained FAN-Hybrid-Large model finetuned on Imagenet1k.(384 resolution)

Instructions to Use Pretrained Backbone Models with TAO

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

model:\n  pretrained_backbone_path: /path/to/the/resnet50.pth\n  backbone: resnet_50\n  train_backbone: True\n  num_feature_levels: 4\n  dec_layers: 6\n  enc_layers: 6\n  num_queries: 900\n  dropout_ratio: 0.0\n  dim_feedforward: 2048\n

Other TAO Pre-trained Models

\n
Get TAO Object Detection pre-trained models for YOLOV4, YOLOV3, FasterRCNN, SSD, DSSD, and RetinaNet architectures from NGC model registry
\n
\n
Get TAO DetectNet_v2 Object Detection pre-trained models for DetectNet_v2 architecture from NGC model registry
\n
\n
Get TAO EfficientDet Object Detection pre-trained models for DetectNet_v2 architecture from NGC model registry
\n
\n
Get TAO Instance segmentation pre-trained models for MaskRCNN architecture from NGC
\n
\n
Get TAO Semantic segmentation pre-trained models for UNet architecture from NGC
\n
\n
Get Purpose-built models from NGC model registry:
\n
- PeopleNet
- TrafficCamNet
- 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 Net
- ActionRecognitionNet
\n

License

This work is licensed under the Creative Commons Attribution NonCommercial ShareAlike 4.0 License (CC-BY-NC-SA-4.0). To view a copy of this license, please visit this link, or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

Technical blogs

Access the latest in Vision AI model development workflows with NVIDIA TAO Toolkit 5.0
Improve accuracy and robustness of vision AI apps with Vision Transformers and NVIDIA TAO
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 2 part blog on training and optimizing 2D body pose estimation model with TAO - Part 1 | Part 2
Learn how to train 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 AI

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 developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction 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.\n

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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## DINO Based Object Detection \n\nObject detection is a popular computer vision technique that can detect one or multiple objects in a frame. Object detection will recognize the individual objects in an image and places bounding boxes around the object. This model card contains pretrained weights that may be used as a starting point with the **DINO** object detection networks in Train Adapt Optimize (TAO) Toolkit to facilitate transfer learning.\n\nIt is trained on the ImageNet-1K. Following backbones are supported with DINO networks.\n\nSupported Backbone:\n\n- resnet_50\n- gc_vit_xxtiny / gc_vit_xtiny / gc_vit_tiny / gc_vit_small / gc_vit_base / gc_vit_large / gc_vit_large_384\n- fan_tiny / fan_small / fan_base / fan_large\n\n## Model Versions\n\n- **gcvit_xxtiny_imagenet1k** - ImageNet1K pre-trained GCViT-xxTiny model for finetune.\n- **gcvit_xtiny_imagenet1k** - ImageNet1K pre-trained GCViT-xTiny model for finetune.\n- **gcvit_tiny_imagenet1k** - ImageNet1K pre-trained GCViT-Tiny model for finetune.\n- **gcvit_small_imagenet1k** - ImageNet1K pre-trained GCViT-Small model for finetune.\n- **gcvit_base_imagenet1k** - ImageNet1K pre-trained GCViT-Base model for finetune.\n- **gcvit_large_imagenet1k** - ImageNet1K pre-trained GCViT-Large model for finetune.\n- **gcvit_large_imagenet22k_384** - ImageNet22k pre-trained GCViT-Large model for finetune.\n- **fan_hybrid_tiny** - ImageNet1k pre-trained FAN-Hybrid-Tiny model for finetune. (224 resolution)\n- **fan_hybrid_small** - ImageNet1k pre-trained FAN-Hybrid-Small model for finetune. (224 resolution)\n- **fan_hybrid_base_in22k** - ImageNet22k pre-trained FAN-Hybrid-Small model for finetune. (224 resolution)\n- **fan_hybrid_base_in22k_1k** - ImageNet22K pre-trained FAN-Hybrid-Base model finetuned on Imagenet1k. (224 resolution)\n- **fan_hybrid_base_in22k_1k_384** - ImageNet22K pre-trained FAN-Hybrid-Base model finetuned on ImageNet-1k.\n- **fan_hybrid_large_in22k** - ImageNet22k pre-trained FAN-Hybrid-Large model for finetune. (224 resolution)\n- **fan_hybrid_large_in22k_384** - ImageNet22K pre-trained FAN-Hybrid-Large model for finetune.(384 resolution)\n- **fan_hybrid_large_in22k_1k** - ImageNet22K pre-trained FAN-Hybrid-Base model for finetune. (224 resolution)\n- **fan_hybrid_large_in22k_1k_384** - ImageNet22K pre-trained FAN-Hybrid-Large model finetuned on Imagenet1k.(384 resolution)\n\n### Instructions to Use Pretrained Backbone Models with TAO\n\nTo use these models as pretrained backbone weights for transfer learning, use the snippet below as a template for the `model` and `train` component of the experiment spec file to train a DINO 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\nmodel:\n pretrained_backbone_path: /path/to/the/resnet50.pth\n backbone: resnet_50\n train_backbone: True\n num_feature_levels: 4\n dec_layers: 6\n enc_layers: 6\n num_queries: 900\n dropout_ratio: 0.0\n dim_feedforward: 2048\n```\n\n## Other TAO Pre-trained Models \n\n- Get [TAO Object Detection](https://ngc.nvidia.com/catalog/models/nvidia:tao:pretrained_object_detection) pre-trained models for **YOLOV4, YOLOV3, FasterRCNN, SSD, DSSD, and RetinaNet** architectures from NGC model registry\n- Get [TAO DetectNet_v2 Object Detection](https://ngc.nvidia.com/catalog/models/nvidia:tao:pretrained_detectnet_v2) pre-trained models for **DetectNet_v2** architecture from NGC model registry\n- Get [TAO EfficientDet Object Detection](https://ngc.nvidia.com/catalog/models/nvidia:tao:pretrained_efficientdet) pre-trained models for **DetectNet_v2** architecture from NGC model registry\n- Get [TAO Instance segmentation](https://ngc.nvidia.com/catalog/models/nvidia:tao:pretrained_instance_segmentation) pre-trained models for **MaskRCNN** architecture from NGC\n- Get [TAO Semantic segmentation](https://ngc.nvidia.com/catalog/models/nvidia:tao:pretrained_semantic_segmentation) pre-trained models for **UNet** architecture from NGC\n\n- Get Purpose-built models from NGC model registry:\n - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)\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 - [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:facenet)\n - [2D Body Pose Net](https://ngc.nvidia.com/catalog/models/nvidia:tao:bodyposenet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n\n## License \n\nThis work is licensed under the Creative Commons Attribution NonCommercial ShareAlike 4.0 License (CC-BY-NC-SA-4.0). To view a copy of this license, please visit this [link](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode), or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.\n\n## Technical blogs \n\n- [Access the latest in Vision AI model development workflows with NVIDIA TAO Toolkit 5.0](https://developer.nvidia.com/blog/access-the-latest-in-vision-ai-model-development-workflows-with-nvidia-tao-toolkit-5-0/)\n- [Improve accuracy and robustness of vision AI apps with Vision Transformers and NVIDIA TAO](https://developer.nvidia.com/blog/improve-accuracy-and-robustness-of-vision-ai-apps-with-vision-transformers-and-nvidia-tao/)\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 2 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 [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\n## Suggested reading \n\n- More information on about TAO Toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/tao-toolkit)\n- Read the [TAO getting Started](hhttps://docs.nvidia.com/tao/tao-toolkit/index.html) guide 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 [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17)\n- Deploy your model on the edge using DeepStream. Learn more about [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk)\n\n## Ethical AI \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 developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction 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.\n","displayName","Pre-trained DINO ImageNet weights",["D",1697481820591],"publisher","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/pretrained_dino_imagenet","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",864054197,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[285,286,287,288,289,290,291,292,293,294,295,296,297],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_253":333,"_299":334,"_300":335,"_302":303},{"_253":329,"_299":330,"_300":331,"_302":303},{"_253":327,"_299":327,"_300":328,"_302":303},{"_253":325,"_299":325,"_300":326,"_302":303},{"_253":322,"_299":323,"_300":324,"_302":303},{"_253":318,"_299":319,"_300":320,"_302":303},{"_253":316,"_299":316,"_300":317,"_302":303},{"_253":314,"_299":314,"_300":315,"_302":303},{"_253":312,"_299":312,"_300":313,"_302":303},{"_253":309,"_299":309,"_300":310,"_302":303},{"_253":307,"_299":307,"_300":308,"_302":303},{"_253":305,"_299":305,"_300":306,"_302":303},{"_253":298,"_299":298,"_300":301,"_302":303},"Transfer Learning Toolkit","value","filter",{"_304":303,"_299":298},"category","label","field","Transfer Learning",{"_304":303,"_299":305},"TLT",{"_304":303,"_299":307},"TAO Toolkit",{"_304":311,"_299":309},"framework","TAO",{"_304":303,"_299":312},"Smart Infrastructure",{"_304":303,"_299":314},"Smart City",{"_304":303,"_299":316},"Metropolis","pltfm_metropolis",{"_304":321,"_299":319},"platform","DeepStream","pltfm_deepstream",{"_304":321,"_299":323},"Deep Learning",{"_304":303,"_299":325},"CV",{"_304":303,"_299":327},"Computer Vision","soln_computer_vision",{"_304":332,"_299":330},"technology","AI","soln_ai",{"_304":332,"_299":334},{"_107":1318,"_109":1319},"ngcApps",[1271],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1252":1253,"_1254":1255,"_1256":1257,"_1258":1259,"_1260":347,"_139":373,"_1261":1262,"_1263":55,"_1264":86,"_1265":86,"_1266":1267,"_1268":86,"_1269":1270},"amplitude",{"_1249":1250,"_886":-7,"_1251":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1243":1244,"_1245":1246,"_1247":1248},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1789257213516,"1.15.0","defaultSearchSuggestions",{"_1126":169,"_1127":1128,"_1129":1130,"_1131":1132},"announcements",[],"consentState",{"_412":1116,"_1117":86,"_1118":30,"_1119":30,"_1120":1121},"consentConfig",{"_394":395,"_396":397,"_398":399,"_400":401,"_402":403,"_404":405,"_406":407,"_408":409,"_410":411,"_412":413},"showContactAdmin",1789257213517,[],[],[387,388,389,390,391,392],{"_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":393,"_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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