TAO with Synthetic Data

The ana_tao resource contains a Jupyter Notebook and supporting configuration files for training the FasterRCNN TAO model\nusing Rendered.ai synthetic imagery data.\nThe notebook is a tutorial that demonstrates the use of the Rendered.ai SDK in conjunction with the NVIDIA TAO toolkit\nas described in the article, \"Using the Rendered.ai Synthetic Data Platform with NVIDIA TAO to Improve Training Data through Closed-Loop ML\".

A user can access datasets on Rendered.ai and evaluate a production grade object detection model for various levels of object obstruction.\nThe process helps the user understand how CV models will perform on real-world datasets by enabling modification of data generation configurations,\nor graphs, to test hypotheses gleaned from viewing inferences. For example, how object detection accuracy is affected by training data with\nobjects at different densities or obstruction levels.

Using the ANA_TAO Resource

Extract the ana_tao resource files into a directory, e.g. ana_tao, and make a project based on that top level folder. The project will contain the obstruction_fasterrcnn experiment and will be the root location for Rendered.ai datasets collected when running the experiment.

The first few cells of obstruction_fasterrcnn.ipynb install the python environment dependencies. Optionally, if running locally, the notebook will run with the packages installed for your jupyter kernel.

The notebook concludes with the following results.

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Effect of number of objects in a Rendered.ai graph on OD performance

About Rendered.ai

Rendered.ai is a platform-as-a-service for synthetic data that enables data scientists to overcome the costs and challenges of acquiring and using real data\nfor training machine learning and artificial intelligence systems.

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View of Rendered.ai GUI

Other versions of ana_tao

Datasets are available for the 'sample_dataset' or 'datasets' versions.

The 'datasets' version contains 3 batches of annoteted images for download

ngc registry resource download-version \"isv-ngc-partner/renderedai/ana_tao:datasets\"

\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

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Dataset		Description
200runs_10objectScene		200 images with low object obstruction
200runs_40objectScene		200 images with moderate object obstruction
200runs_70objectScene		200 images with high object obstruction

The 'sample_dataset' version contains 10 images for viewing here in the file browser:

\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

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Item	Description
sample_dataset/	A sample dataset of 50 objects per scene
10 Object Scene.png	Sample of 10 objects per scene
40 Object Scene.png	Sample of 40 objects per scene
70 Object Scene.png	Sample of 70 objects per scene
100 Object Scene.png	Sample of 100 objects per scene

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For example, how object detection accuracy is affected by training data with\nobjects at different densities or obstruction levels.\n\n[Register now](https://www.rendered.ai/waitlist.html) to get started with synthetic data!\n\n## Using the ANA_TAO Resource\nExtract the ana_tao resource files into a directory, e.g. `ana_tao`, and make a project based on that top level folder. The project will contain the obstruction_fasterrcnn experiment and will be the root location for Rendered.ai datasets collected when running the experiment.\n\nThe first few cells of obstruction_fasterrcnn.ipynb install the python environment dependencies. Optionally, if running locally, the notebook will run with the packages installed for your jupyter kernel.\n\nThe notebook concludes with the following results.\n\n|![](https://renderedai-public-assets.s3.us-west-2.amazonaws.com/Accuracy_vs_Obstruction.png)|\n|:---:|\n|*Effect of number of objects in a Rendered.ai graph on OD performance*|\n\n## About Rendered.ai\n[Rendered.ai](https://rendered.ai) is a platform-as-a-service for synthetic data that enables data scientists to overcome the costs and challenges of acquiring and using real data\nfor training machine learning and artificial intelligence systems.\n\n|![](https://renderedai-public-assets.s3.us-west-2.amazonaws.com/Rendered.ai_Dashboard.png)|\n|:---:|\n|*View of Rendered.ai GUI*|\n\n## Other versions of ana_tao\nDatasets are available for the 'sample_dataset' or 'datasets' versions.\n\nThe 'datasets' version contains 3 batches of annoteted images for download\n\n`ngc registry resource download-version \"isv-ngc-partner/renderedai/ana_tao:datasets\"`\n\n| Dataset | | Description |\n| --- | --- | ----------- |\n| 200runs_10objectScene | | 200 images with low object obstruction |\n| 200runs_40objectScene | | 200 images with moderate object obstruction |\n| 200runs_70objectScene | | 200 images with high object obstruction |\n\nThe 'sample_dataset' version contains 10 images for viewing here in the file browser:\n\n| Item |Description |\n| --- | ----------- |\n| sample_dataset/ | A sample dataset of 50 objects per scene |\n| 10 Object Scene.png | Sample of 10 objects per scene |\n| 40 Object Scene.png | Sample of 40 objects per scene |\n| 70 Object Scene.png | Sample of 70 objects per scene |\n| 100 Object Scene.png | Sample of 100 objects per scene |","displayName","TAO with Synthetic Data",["D",1680648447210],"publisher","Rendedred.ai","latestVersion","collectionCounts","imageUrl","/api/image/partners/-/resources/ana_tao","rawImageUrl","https://renderedai-public-assets.s3.us-west-2.amazonaws.com/Logo+Silver+312x152.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[284,285],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_253":294,"_287":295,"_289":296,"_291":88},{"_253":286,"_287":288,"_289":290,"_291":88},"Synthetic Data Generation","value","uscs_synthetic_data_generation","filter",{"_292":293,"_287":288},"category","field","application","Annotation","uscs_annotation",{"_292":293,"_287":295},{"_165":1277,"_167":1278},"ngcApps",[1230],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1211":1212,"_1213":1214,"_1215":1216,"_1217":1218,"_1219":308,"_110":332,"_1220":1221,"_1222":59,"_1223":91,"_1224":91,"_1225":1226,"_1227":91,"_1228":1229},"amplitude",{"_1208":1209,"_845":-7,"_1210":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1202":1203,"_1204":1205,"_1206":1207},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1085":413,"_1086":1087,"_1088":1089,"_1090":1091},"announcements",[],"consentState",{"_370":1075,"_1076":35,"_1077":35,"_1078":91,"_1079":1080},"consentConfig",{"_352":353,"_354":355,"_356":357,"_358":359,"_360":361,"_362":363,"_364":365,"_366":367,"_368":369,"_370":371},"showContactAdmin",1785261830481,[],[],[346,347,348,349,350,351],{"_49":63,"_51":74,"_53":160,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":72,"_53":160,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":68,"_53":160,"_55":-5,"_57":-5,"_58":59},{"_49":63,"_51":64,"_53":160,"_55":-5,"_57":-5,"_58":59},{"_49":60,"_51":61,"_53":160,"_55":-5,"_57":-5,"_58":59},{"_49":50,"_51":52,"_53":160,"_55":-5,"_57":-5,"_58":59},"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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	Files	Compressed Size
obstruction_fasterrcnn_v1	4	39.26 KB	March 17, 2022 UTC
datasetsSelected	3039	5.55 GB	November 8, 2021 UTC
sample_dataset	47	115.7 MB	November 8, 2021 UTC