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

","code-1pykvd","model","code-1tuzuw","train","nvidia/tao/pretrained_deformable_detr_nvimagenet","key","nvidia/tao/models/pretrained_deformable_detr_nvimagenet","resourceType","models","resourceId","pretrained_deformable_detr_nvimagenet","shortDescription","Pre-trained deformable_detr weights trained on NvImageNet to facilitate transfer learning using TAO Toolkit.","# TAO Pretrained Commercial Backbone for Deformable DETR\n\n## What is Train Adapt Optimize (TAO) Toolkit? \n\n[Train Adapt Optimize (TAO) Toolkit](https://developer.nvidia.com/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.\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 NVImageNet that is permitted for commercial uses. 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- **resnet50** - NVImageNet pre-trained ResNet-50 model for finetune.\n- **gcvit_xxtiny_nvimagenet** - NVImageNet pre-trained GCViT-xxTiny model for finetune.\n- **gcvit_xtiny_nvimagenet** - NVImageNet pre-trained GCViT-xTiny model for finetune.\n- **gcvit_tiny_nvimagenet** - NVImageNet pre-trained GCViT-Tiny model for finetune.\n- **gcvit_small_nvimagenet** - NVImageNet pre-trained GCViT-Small model for finetune.\n- **gcvit_base_nvimagenet** - NVImageNet pre-trained GCViT-Base 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\nThe licenses to use this model is covered by the Model EULA. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of these [licenses](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/)\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 NvImageNet weights",["D",1697481820677],"publisher","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/pretrained_deformable_detr_nvimagenet","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[255,256,257,258,259,260,261,262,263,264,265,266,267],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_224":303,"_269":304,"_270":305,"_272":273},{"_224":299,"_269":300,"_270":301,"_272":273},{"_224":297,"_269":297,"_270":298,"_272":273},{"_224":295,"_269":295,"_270":296,"_272":273},{"_224":292,"_269":293,"_270":294,"_272":273},{"_224":288,"_269":289,"_270":290,"_272":273},{"_224":286,"_269":286,"_270":287,"_272":273},{"_224":284,"_269":284,"_270":285,"_272":273},{"_224":282,"_269":282,"_270":283,"_272":273},{"_224":279,"_269":279,"_270":280,"_272":273},{"_224":277,"_269":277,"_270":278,"_272":273},{"_224":275,"_269":275,"_270":276,"_272":273},{"_224":268,"_269":268,"_270":271,"_272":273},"Transfer Learning Toolkit","value","filter",{"_274":273,"_269":268},"category","label","field","Transfer Learning",{"_274":273,"_269":275},"TLT",{"_274":273,"_269":277},"TAO Toolkit",{"_274":281,"_269":279},"framework","TAO",{"_274":273,"_269":282},"Smart Infrastructure",{"_274":273,"_269":284},"Smart City",{"_274":273,"_269":286},"Metropolis","pltfm_metropolis",{"_274":291,"_269":289},"platform","DeepStream","pltfm_deepstream",{"_274":291,"_269":293},"Deep Learning",{"_274":273,"_269":295},"CV",{"_274":273,"_269":297},"Computer Vision","soln_computer_vision",{"_274":302,"_269":300},"technology","AI","soln_ai",{"_274":302,"_269":304},{"_107":1488,"_109":1489},"ngcApps",[1441],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1423":1424,"_1425":1426,"_1427":1428,"_1429":1430,"_1431":317,"_135":341,"_1432":1433,"_1421":56,"_1434":86,"_1435":86,"_1436":1437,"_1438":86,"_1439":1440},"amplitude",{"_1419":1420,"_1421":56,"_1012":-7,"_1422":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1413":1414,"_1415":1416,"_1417":1418},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.2.3","defaultSearchSuggestions",{"_1287":165,"_1288":1289,"_1290":1291,"_1292":1293},"announcements",[],"consentState",{"_378":1277,"_1278":86,"_1279":31,"_1280":31,"_1281":1282},"consentConfig",{"_360":361,"_362":363,"_364":365,"_366":367,"_368":369,"_370":371,"_372":373,"_374":375,"_376":377,"_378":379},"showContactAdmin",1782185656254,[],[],[355,356,357,358,359],{"_45":60,"_47":70,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":102,"_51":-5,"_53":-5,"_55":56},"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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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a one year lifespan and contains no personal information.","default","

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