TAO Pretrained Non-commercial Backbone for Deformable DETR

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.

Deformable-DETR 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 Deformable-DETR 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 Deformable-DETR 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

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.

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 Deformable DETR 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: 300\n  with_box_refine: True\n  dropout_ratio: 0.3\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## Deformable-DETR 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 **Deformable-DETR** 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 Deformable-DETR 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\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\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 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\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: 300\n with_box_refine: True\n dropout_ratio: 0.3\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 Deformable DETR ImageNet weights",["D",1697481819901],"publisher","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/pretrained_deformable_detr_imagenet","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",367540965,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[250,251,252,253,254,255,256,257,258,259,260,261,262],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_218":298,"_264":299,"_265":300,"_267":268},{"_218":294,"_264":295,"_265":296,"_267":268},{"_218":292,"_264":292,"_265":293,"_267":268},{"_218":290,"_264":290,"_265":291,"_267":268},{"_218":287,"_264":288,"_265":289,"_267":268},{"_218":283,"_264":284,"_265":285,"_267":268},{"_218":281,"_264":281,"_265":282,"_267":268},{"_218":279,"_264":279,"_265":280,"_267":268},{"_218":277,"_264":277,"_265":278,"_267":268},{"_218":274,"_264":274,"_265":275,"_267":268},{"_218":272,"_264":272,"_265":273,"_267":268},{"_218":270,"_264":270,"_265":271,"_267":268},{"_218":263,"_264":263,"_265":266,"_267":268},"Transfer Learning Toolkit","value","filter",{"_269":268,"_264":263},"category","label","field","Transfer Learning",{"_269":268,"_264":270},"TLT",{"_269":268,"_264":272},"TAO Toolkit",{"_269":276,"_264":274},"framework","TAO",{"_269":268,"_264":277},"Smart Infrastructure",{"_269":268,"_264":279},"Smart City",{"_269":268,"_264":281},"Metropolis","pltfm_metropolis",{"_269":286,"_264":284},"platform","DeepStream","pltfm_deepstream",{"_269":286,"_264":288},"Deep Learning",{"_269":268,"_264":290},"CV",{"_269":268,"_264":292},"Computer Vision","soln_computer_vision",{"_269":297,"_264":295},"technology","AI","soln_ai",{"_269":297,"_264":299},{"_105":1280,"_107":1281},"ngcApps",[1233],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1214":1215,"_1216":1217,"_1218":1219,"_1220":1221,"_1222":312,"_137":340,"_1223":1224,"_1225":55,"_1226":84,"_1227":84,"_1228":1229,"_1230":84,"_1231":1232},"amplitude",{"_1211":1212,"_852":-7,"_1213":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1205":1206,"_1207":1208,"_1209":1210},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1786826669230,"1.11.0","defaultSearchSuggestions",{"_1092":167,"_1093":1094,"_1095":1096,"_1097":1098},"announcements",[],"consentState",{"_378":1082,"_1083":84,"_1084":30,"_1085":30,"_1086":1087},"consentConfig",{"_360":361,"_362":363,"_364":365,"_366":367,"_368":369,"_370":371,"_372":373,"_374":375,"_376":377,"_378":379},"showContactAdmin",1786826669231,[],[],[354,355,356,357,358,359],{"_44":58,"_46":68,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":67,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":65,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":59,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":100,"_50":-5,"_52":-5,"_54":55},"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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They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",[516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537],[401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417],{"_418":511,"_218":511,"_166":167,"_392":512},{"_418":507,"_218":507,"_166":167,"_392":508},{"_418":502,"_218":502,"_166":167,"_392":503},{"_418":498,"_218":498,"_166":167,"_392":499},{"_418":493,"_218":493,"_166":167,"_392":494},{"_418":488,"_218":488,"_166":167,"_392":489},{"_418":480,"_218":480,"_166":167,"_392":481},{"_418":472,"_218":472,"_166":167,"_392":473},{"_418":467,"_218":467,"_166":167,"_392":468},{"_418":462,"_218":462,"_166":167,"_392":463},{"_418":457,"_218":457,"_166":167,"_392":458},{"_418":452,"_218":452,"_166":167,"_392":453},{"_418":447,"_218":447,"_166":167,"_392":448},{"_418":440,"_218":440,"_166":167,"_392":441},{"_418":435,"_218":435,"_166":167,"_392":436},{"_418":428,"_218":428,"_166":167,"_392":429},{"_418":419,"_218":419,"_166":167,"_392":420},"hostName","amazon-adsystem.com",[421,422],{"_76":427,"_424":419,"_425":30,"_166":426},{"_76":423,"_424":419,"_425":30,"_166":426},"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",[430,431],{"_76":434,"_424":428,"_425":30,"_166":433},{"_76":432,"_424":428,"_425":30,"_166":433},"IDE","This domain is owned by Doubleclick (Google). 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. 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. If you do not allow these cookies, then some or all of these services may not function properly.",4,[609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633],[580,581,582,583,584],{"_418":493,"_218":493,"_166":167,"_392":604},{"_418":467,"_218":467,"_166":167,"_392":599},{"_418":594,"_218":594,"_166":167,"_392":595},{"_418":589,"_218":589,"_166":167,"_392":590},{"_418":585,"_218":585,"_166":167,"_392":586},"nvidia.partners",[587],{"_76":588,"_424":585,"_425":30,"_166":461},"edge","app.hushly.com",[591],{"_76":592,"_424":589,"_425":30,"_166":593},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[596],{"_76":597,"_424":594,"_425":30,"_166":598},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. 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It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. 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