[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10,"_11":12},"actionData","errors","root",{"_269":270,"_271":272,"_273":274,"_275":276,"_277":278,"_279":20,"_280":281,"_282":283,"_284":20,"_285":20,"_286":20,"_287":20,"_288":289,"_290":-5,"_291":292,"_293":294,"_295":296,"_297":298,"_299":300,"_301":302,"_303":-5,"_304":20,"_305":-5,"_306":307,"_79":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":20,"_15":16,"_17":318,"_19":20,"_21":319,"_23":320,"_25":321},"routes/orgs/$orgName/$teamName/$resourceType/$resourceId/$version/route",{"_65":66,"_13":67,"_68":69,"_70":71,"_72":20,"_73":74,"_75":20,"_76":20,"_77":78,"_79":80,"_81":-5,"_82":83,"_84":-5,"_85":86,"_87":88,"_89":90,"_91":92,"_15":16,"_17":93,"_19":20,"_21":94,"_23":95,"_25":96},"routes/orgs/$orgName/$teamName/$resourceType/$resourceId/$version/model-card/route",{"_15":16,"_17":59,"_19":20,"_21":60,"_23":61,"_25":62},"routes/orgs/$orgName/$teamName/$resourceType/$resourceId/$version/model-card/bias/route",{"_13":14,"_15":16,"_17":18,"_19":20,"_21":22,"_23":24,"_25":26},"html","\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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

Field	Response
Participation considerations from adversely impacted groups (protected classes) in model design and testing:	OCR: Not applicable OCD: Not applicable
Measures taken to mitigate against unwanted bias:	OCR: Not Applicable OCD: Not Applicable

","traceId","4208a8a23281723a0f77ccff9272015f","traceStartTime",1783688398639,"isStaleResponse",false,"notifications",[],"toasts",[],"apiCalls",[27,28,29,30,31,32],{"_33":48,"_35":57,"_37":52,"_39":53,"_41":58,"_43":44},{"_33":48,"_35":55,"_37":52,"_39":53,"_41":56,"_43":44},{"_33":48,"_35":51,"_37":52,"_39":53,"_41":54,"_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":38,"_39":40,"_41":42,"_43":44},"apiTag","subscription-api","path","/v2/users/me/subscriptions","status","error","httpStatus",401,"durationMs",2,"retryCount",0,"oms-api","/v2/users/me",3,"models-api","/v2/models/org/nvidia/team/tao/ocrnet/collections?page-number=0&page-size=1","pending","/v2/models/nvidia/tao/ocrnet/versions/deployable_v1.0","success",200,51,"/v2/models/nvidia/tao/ocrnet/versions?page-size=100&page-number=0&sort-order=CREATED_DATE_DESC",56,"/v2/models/nvidia/tao/ocrnet?resolve-labels=true&remove-unresolved-labels=true",80,1783688398548,[],[],[63,64],{"_33":45,"_35":46,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":34,"_35":36,"_37":50,"_39":-5,"_41":-5,"_43":44},"resource",{"_147":168,"_169":170,"_171":172,"_173":174,"_104":105,"_106":107,"_175":176,"_110":177,"_178":179,"_116":180,"_181":182,"_183":164,"_184":-7,"_185":-7,"_186":187,"_188":189,"_190":90,"_191":192,"_193":-5,"_194":-7,"_195":-7,"_196":197,"_198":199,"_200":201,"_202":203,"_204":205,"_206":207,"_208":209,"_210":211,"_212":213,"_214":20},"

This model card combines the relevant information of OCR and OCD models

OCRNet Model Overview

Description

Optical character recognition network recognizes characters from the gray images.

Terms of use

License to use these models 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.

References(s):

Citations

Baek, J., Kim, G., Lee, J., Park, S., Han, D., Yun, S., ... & Lee, H. (2019). What is wrong with scene text recognition model comparisons? dataset and model analysis. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 4715-4723).
Zhang, Y., Gueguen, L., Zharkov, I., Zhang, P., Seifert, K., & Kadlec, B. (2017, July). Uber-text: A large-scale dataset for optical character recognition from street-level imagery. In SUNw: Scene Understanding Workshop-CVPR (Vol. 2017, p. 5).
Singh, A., Pang, G., Toh, M., Huang, J., Galuba, W., & Hassner, T. (2021). Textocr: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 8802-8812)
Graves, Alex, et al. \"Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks.\" In: Proceedings of the 23rd international conference on Machine learning (2006)
He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: CVPR (2015)
Zhou, D., Yu, Z., Xie, E., Xiao, C., Anandkumar, A., Feng, J., & Alvarez, J. M. (2022, June). Understanding the robustness in vision transformers. In International Conference on Machine Learning (pp. 27378-27394). PMLR.
Kuo, C. W., Ashmore, J. D., Huggins, D., & Kira, Z. (2019, January). Data-efficient graph embedding learning for PCB component detection. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 551-560). IEEE.

Model Architecture:

Architecture Type: Convolution Neural Network (CNN)
\nNetwork Architecture: ResNet50
\nModel Version:

trainable_v1.0 - Pre-trained model with ResNet backbone on scene text.
deployable_v1.0 - Models deployable with ResNet backbone.
trainable_v2.0 - Pre-trained model with FAN backbone on scene text.
deployable_v2.0 - Model deployable with FAN backbone on scene text.
trainable_v2.1 - Pre-trained model with FAN backbone on PCB text.
deployable_v2.1 - Model deployable with FAN backbone on PCB text.

Input:

Input Type(s): Image
\nInput Format: Gray Image
\nInput Parameters: 3D
\nOther Properties Related to Input:

Gray Images of 1 X 32 X 100 (C H W) for trainable_v1.0/deployable_v1.0
Gray Images of 1 X 64 X 200 (C H W) for trainable_v2.0/trainable_v2.1/deployable_v2.0/deployable_v2.1

Output:

Output Type(s): Sequence of characters
\nOutput Format: Character Id sequence: Text String(s)
\nOther Properties Related to Output: None

Software Integration:

Runtime(s): NVIDIA AI Enterprise
\nToolkit: TAO Framework
\nSupported Hardware Platform(s): Ampere, Jetson, Hopper, Lovelace, Pascal, Turing
\nSupported Operating System(s): Linux

Training & Finetuning:

Dataset:

OCRNet pretrained model was trained on Uber-Text and TextOCR dataset. The Uber-Text contains street-level images collected from car mounted sensors and truths annotated by a team of image analysts. The TextOCR is the images with annotated texts from OpenImages dataset. After collecting the original data from Uber-text and TextOCR, we remove all the text images with * label in Uber-text and only keep alphanumeric text images with the maximum length is 25 in both datasets. We finally construct the dataset with 805007 text images for training and 24388 images for validation.

Inference:

Engine: TensorRT
\nTest Hardware:

Orin Nano
Orin NX
AGX Orin
L4
L40
T4
A2
A30
A100
H100

OCDNet Model Overview

Model Overview

The model described in this card is an optical characters detection network, which aims to detect text in images. Trainable and deployable OCDNet models are provided. These are trained on Uber-Text dataset and ICDAR2015 dataset respectively.

Terms of use

License to use these models 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.

Model Architecture

This model is based on a relatively sophisticated text detection network called DBNet. DBNet is a network architecture for real-time scene text detection with differentiable binarization. It aims to solve the problem of text localization and segmentation in natural images with complex backgrounds and various text shapes.

Training

The training algorithm inserts the binarization operation into the segmentation network and jointly optimizes it so that\nthe network can learn to separate foreground and background pixels more effectively. The binarization threshold is learned by minimizing the IoU loss between the predicted binary map and the ground truth binary map.

Training Data

The trainable models were trained on the Uber-Text dataset and ICDAR2015 dataset respectively. The Uber-Text dataset contains street-level images collected from car mounted sensors and truths annotated by a team of image analysts--including train_4Kx4K, train_1Kx1K, val_4Kx4K, val_1Kx1K, test_4Kx4K as the training datasets and test_1Kx1K as the validation dataset. The dataset was constructed with 107812 images for training and 10157 images for validation. The ICDAR2015 dataset contains 1000 training images and 500 test images. The deployable models were ONNX models that were exported using the trainable models.

Performance

Evaluation Data

We test the OCRNet model on three different datasets: Uber-Text + TextOCR validation dataset, ICDAR13 and ICDAR15 scene text benchmark dataset.

Methodology and KPI

The key performance indicator is the accuracy of character recognition. The accurate recognition means all the characters in a text area are recognized correctly.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\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\n\n\n\n\n

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

model	dataset	accuracy
ocrnet_resnet50_unpruned	Uber-Text + TextOCR validation	77.1%
ocrnet_resnet50_unpruned	ICDAR13	91.8%
ocrnet_resnet50_unpruned	ICDAR15	78.6%
ocrnet_resnet50_unpruned	Internal PCB validation	74.1%
ocrnet_resnet50_pruned	ICDAR13	92.6%
ocrnet-vit	Uber-Text + TextOCR validation	83.7%
ocrnet-vit	ICDAR13	95.5%
ocrnet-vit	ICDAR15	84.7%
ocrnet-vit-pcb	Internal PCB validation	84.2%

Real-time Inference Performance

The inference uses FP16 precision. The inference performance runs with trtexec on AGX Orin, Orin NX, Orin Nano and NVIDIA L4 GPU. The Jetson devices run at Max-N configuration for maximum system performance. The data is the inference only performance. The end-to-end performance with streaming video data might slightly vary depending on use cases of applications.

\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\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\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\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	Device	precision	batch_size	FPS
ocrnet_resnet50_pruned	Orin Nano	FP16	128	981
ocrnet_resnet50_pruned	Orin NX	FP16	128	1399
ocrnet_resnet50_pruned	AGX Orin	FP16	128	3921
ocrnet_resnet50_pruned	L4	FP16	512	6404
ocrnet-vit	Orin Nano	FP16+INT8	4	100
ocrnet-vit	Orin NX	FP16+INT8	4	147
ocrnet-vit	AGX Orin	FP16+INT8	8	393
ocrnet-vit	T4	FP32	16	680
ocrnet-vit	A2	FP32	32	428
ocrnet-vit	A30	FP32	32	1645
ocrnet-vit	L4	FP32	32	1471
ocrnet-vit	L40	FP32	32	4320
ocrnet-vit	A100	FP32	32	2513
ocrnet-vit	H100	FP32	32	3846

How to Use This Model

This model needs to be used with NVIDIA Hardware and Software: The model can run on any NVIDIA GPU, including NVIDIA Jetson devices, with TAO Toolkit, DeepStream SDK or TensorRT.

The primary use case for this model is to detect text on images.

There are two types of models provided (both unpruned).

trainable
deployable

The trainable models are intended for training with the user's own dataset using TAO Toolkit. This can provide high-fidelity models that are adapted to the use case. A Jupyter notebook is available as a part of the TAO container and can be used to re-train.

The deployable models share the same structure as the trainable model, but in onnx format. The deployable models can be deployed using TensorRT, nvOCDR, and DeepStream.

Input

Images of C x H x W (H and W should be multiples of 32.)

Output

BBox or polygon coordinates for each detected text in the input image

Instructions to Use the Model with TAO

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

To use trainable_resnet18_v1.0 model:

model:\n  load_pruned_graph: False\n  pruned_graph_path: '/results/prune/pruned_0.1.pth'\n  pretrained_model_path: '/data/ocdnet/ocdnet_deformable_resnet18.pth'\n  backbone: deformable_resnet18\n

To use trainable_ocdnet_vit_v1.0 model:

model:\n  load_pruned_graph: False\n  pruned_graph_path: '/results/prune/pruned_0.1.pth'\n  pretrained_model_path: '/data/ocdnet/ocdnet_fan_tiny_2x_icdar.pth'\n  backbone: fan_tiny_8_p4_hybrid\n  enlarge_feature_map_size: True\n  activation_checkpoint: True\n

Instructions to deploy the model with DeepStream

To create the entire end-to-end video analytic application, deploy this model with DeepStream SDK. DeepStream SDK is a streaming analytic toolkit to accelerate building AI-based video analytic applications. DeepStream supports direct integration of this model into the Deepstream sample app.

To deploy this model with DeepStream, follow these instructions.

Limitations

Restricted Usage in Different Fields

The NVIDIA OCDNet trainable model is trained on Uber Text, ICDAR2015 and PCB text dataset, which contains street-view images only. To get better accuracy in a specific field, more data is usually required to fine tune the pre-trained model with TAO Toolkit.

Model versions:

trainable_resnet18_v1.0 - Pre-trained models with deformable-resnet18 backbone, trained on Uber-Text dataset.
trainable_resnet50_v1.0 - Pre-trained models with deformable-resnet50 backbone, trained on Uber-Text dataset.
trainable_ocdnet_vit_v1.0 - Pre-trained models with fan-tiny backbone, trained on ICDAR2015 dataset.
trainable_ocdnet_vit_v1.1 - Pre-trained models with fan-tiny backbone, trained on Uber-Text dataset.
trainable_ocdnet_vit_v1.2 - Pre-trained models with fan-tiny backbone, trained on PCB dataset.
trainable_ocdnet_vit_v1.3 - Pre-trained models with fan-tiny backbone, trained on ImageNet2012 dataset.
trainable_ocdnet_vit_v1.4 - Pre-trained models with fan-tiny backbone, trained on ICDAR2015 dataset and model are pruned.
deployable_v1.0 - Model deployable with deformable-resnet backbone.
deployable_v2.0 - Model deployable with fan-tiny backbone, trained on ICDAR2015.
deployable_v2.1 - Model deployable with fan-tiny backbone, trained on Uber-Text.
deployable_v2.2 - Model deployable with fan-tiny backbone, trained on PCB dataset.
deployable_v2.3 - Model deployable with fan-tiny backbone, trained on ICDAR2015 and model are pruned.

Reference

Citations

Liao M., Wan Z., Yao C., Chen K., Bai X.: Real-time Scene Text Detection with Differentiable Binarization (2020).
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y: Deformable convolutional networks. (2017).
He, W., Zhang, X., Yin, F., and Liu, C.: Deep direct regression for multi-oriented scene text detection. (2017).
Zhang, Y., Gueguen, L., Zharkov, I., Zhang, P., Seifert, K., & Kadlec, B. (2017, July). Uber-text: A large-scale dataset for optical character recognition from street-level imagery. In SUNw: Scene Understanding Workshop-CVPR (Vol. 2017, p. 5).
Zhou, D., Yu, Z., Xie, E., Xiao, C., Anandkumar, A., Feng, J., & Alvarez, J. M. (2022, June). Understanding the robustness in vision transformers. In International Conference on Machine Learning (pp. 27378-27394). PMLR.
Kuo, C. W., Ashmore, J. D., Huggins, D., & Kira, Z. (2019, January). Data-efficient graph embedding learning for PCB component detection. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 551-560). IEEE.

Using TAO Pre-trained Models

Get TAO Container
Get other purpose-built models from the NGC model registry:\n
- TrafficCamNet
- PeopleNet
- PeopleNet
- PeopleNet-Transformer
- 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 Estimation
- ActionRecognitionNet
- ActionRecognitionNet
- PoseClassificationNet
- People ReIdentification
- PointPillarNet
- CitySegFormer
- Retail Object Detection
- Retail Object Embedding
- Optical Inspection
- Optical Character Detection
- Optical Character Recognition
- PCB Classification
- PeopleSemSegFormer
- LPDNet
- License Plate Recognition
- Gaze Estimation
- Facial Landmark
- Heart Rate Estimation
- Gesture Recognition
- Emotion Recognition
- FaceDetect
- 2D Body Pose Estimation
- ActionRecognitionNet
- ActionRecognitionNet
- PoseClassificationNet
- People ReIdentification
- PointPillarNet
- CitySegFormer
- Retail Object Detection
- Retail Object Embedding
- Optical Inspection
- Optical Character Detection
- Optical Character Recognition
- PCB Classification
- PeopleSemSegFormer
\n

License

The license to use these models 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.

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 2 part blog on training and optimizing 2D body pose estimation model with TAO - Part 1 | Part 2
Learn how to traina real-time License plate detection and recognition app with TAO and DeepStream.
Model accuracy is extremely important. Learn how to achieve state-of-the-art accuracy for classification and object detection models using TAO.

Ethical AI

The NVIDIA OCDNet model detects optical characters.

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.

Please report security vulnerabilities or NVIDIA AI Concerns here

","resourceVersions",[137,138,139,140,141,142,143,144,145,146],"resourceVersionsTotal",9,"hasMoreVersions","versionMetadata",{"_109":80,"_110":111,"_37":112,"_113":-5,"_114":115,"_116":117,"_118":119,"_120":121,"_122":20,"_123":-5,"_124":20,"_125":-5,"_126":20,"_127":-5,"_128":20,"_129":-5,"_130":20,"_131":-5,"_132":20,"_133":-5,"_134":20,"_135":136},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/tao/models/ocrnet/-","version","deployable_v1.0","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/tao/ocrnet:deployable_v1.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/tao/models/ocrnet/deployable_v1.0/version-history","accessCheckResult",{"_108":90},"hasRelatedCollections",true,"context",{"_104":105,"_106":107},1783688398654,[],[],[97,98,99,100,101,102],{"_33":48,"_35":57,"_37":52,"_39":53,"_41":58,"_43":44},{"_33":48,"_35":55,"_37":52,"_39":53,"_41":56,"_43":44},{"_33":48,"_35":51,"_37":52,"_39":53,"_41":54,"_43":44},{"_33":48,"_35":49,"_37":52,"_39":53,"_41":103,"_43":44},{"_33":45,"_35":46,"_37":38,"_39":40,"_41":47,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_43":44},87,"orgName","nvidia","teamName","tao","allowed","versionId","description","","UPLOAD_COMPLETE","releaseType","createdDate",["D",1687542975464],"updatedDate",["D",1740840432646],"totalSizeInBytes",300472033,"totalFileCount",4,"isSigned","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_147":166,"_148":167,"_113":-5,"_122":90,"_124":20,"_135":165,"_126":20},{"_147":164,"_148":164,"_113":-5,"_122":90,"_124":20,"_135":165,"_126":20},{"_147":162,"_148":162,"_113":-5,"_122":90,"_124":20,"_135":163,"_126":20},{"_147":160,"_148":160,"_113":-5,"_122":90,"_124":20,"_135":161,"_126":20},{"_147":158,"_148":158,"_113":-5,"_122":90,"_124":20,"_135":159,"_126":20},{"_147":156,"_148":156,"_113":-5,"_122":90,"_124":20,"_135":157,"_126":20},{"_147":154,"_148":154,"_113":-5,"_122":90,"_124":20,"_135":155,"_126":20},{"_147":152,"_148":152,"_113":-5,"_122":90,"_124":20,"_135":153,"_126":20},{"_147":150,"_148":150,"_113":-5,"_122":90,"_124":20,"_135":151,"_126":20},{"_147":80,"_148":80,"_113":-5,"_122":20,"_124":20,"_135":149,"_126":20},"id","name",[],"trainable_v1.0",[],"trainable_v2.1",[],"trainable_v2.0",[],"deployable_v2.1",[],"deployable_v2.0",[],"deployable_v1.0.1",[],"deployable_v2.0.1",[],"deployable_v2.1.1",[],"-","Latest (deployable_v2.1.1)","nvidia/tao/ocrnet","key","nvidia/tao/models/ocrnet","resourceType","models","resourceId","ocrnet","shortDescription","Model to recognise characters from a preceding OCDNet model.","This model card combines the relevant information of OCR and OCD models\n\n# OCRNet Model Overview\n\n## Description \nOptical character recognition network recognizes characters from the gray images.\n\n## Terms of use \nLicense to use these models 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## References(s): \n### Citations \n- Baek, J., Kim, G., Lee, J., Park, S., Han, D., Yun, S., ... & Lee, H. (2019). What is wrong with scene text recognition model comparisons? dataset and model analysis. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 4715-4723).\n- Zhang, Y., Gueguen, L., Zharkov, I., Zhang, P., Seifert, K., & Kadlec, B. (2017, July). Uber-text: A large-scale dataset for optical character recognition from street-level imagery. In SUNw: Scene Understanding Workshop-CVPR (Vol. 2017, p. 5).\n- Singh, A., Pang, G., Toh, M., Huang, J., Galuba, W., & Hassner, T. (2021). Textocr: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 8802-8812)\n- Graves, Alex, et al. \"Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks.\" In: Proceedings of the 23rd international conference on Machine learning (2006)\n- He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: CVPR (2015)\n- Zhou, D., Yu, Z., Xie, E., Xiao, C., Anandkumar, A., Feng, J., & Alvarez, J. M. (2022, June). Understanding the robustness in vision transformers. In International Conference on Machine Learning (pp. 27378-27394). PMLR.\n- Kuo, C. W., Ashmore, J. D., Huggins, D., & Kira, Z. (2019, January). Data-efficient graph embedding learning for PCB component detection. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 551-560). IEEE.\n\n## Model Architecture: \n**Architecture Type:** Convolution Neural Network (CNN)
\n**Network Architecture:** ResNet50
\n**Model Version:**
\n- **trainable_v1.0** - Pre-trained model with ResNet backbone on scene text.\n- **deployable_v1.0** - Models deployable with ResNet backbone.\n- **trainable_v2.0** - Pre-trained model with FAN backbone on scene text.\n- **deployable_v2.0** - Model deployable with FAN backbone on scene text.\n- **trainable_v2.1** - Pre-trained model with FAN backbone on PCB text.\n- **deployable_v2.1** - Model deployable with FAN backbone on PCB text.\n\n## Input: \n**Input Type(s):** Image
\n**Input Format:** Gray Image
\n**Input Parameters:** 3D
\n**Other Properties Related to Input:**
\n - Gray Images of 1 X 32 X 100 (C H W) for trainable_v1.0/deployable_v1.0\n - Gray Images of 1 X 64 X 200 (C H W) for trainable_v2.0/trainable_v2.1/deployable_v2.0/deployable_v2.1\n\n## Output: \n**Output Type(s):** Sequence of characters
\n**Output Format:** Character Id sequence: Text String(s)
\n**Other Properties Related to Output:** None
\n\n## Software Integration: \n**Runtime(s):** NVIDIA AI Enterprise
\n**Toolkit:** TAO Framework
\n**Supported Hardware Platform(s):** Ampere, Jetson, Hopper, Lovelace, Pascal, Turing
\n**Supported Operating System(s):** Linux
\n\n# Training & Finetuning:\n\n## Dataset: \n\nOCRNet pretrained model was trained on Uber-Text and TextOCR dataset. The Uber-Text contains street-level images collected from car mounted sensors and truths annotated by a team of image analysts. The TextOCR is the images with annotated texts from OpenImages dataset. After collecting the original data from Uber-text and TextOCR, we remove all the text images with `*` label in Uber-text and only keep alphanumeric text images with the maximum length is 25 in both datasets. We finally construct the dataset with 805007 text images for training and 24388 images for validation.\n\n## Inference: \n**Engine:** TensorRT
\n**Test Hardware:**
\n- Orin Nano\n- Orin NX\n- AGX Orin\n- L4\n- L40\n- T4\n- A2\n- A30\n- A100\n- H100\n\n# OCDNet Model Overview\n\n## Model Overview \n\nThe model described in this card is an optical characters detection network, which aims to detect text in images. Trainable and deployable OCDNet models are provided. These are trained on Uber-Text dataset and ICDAR2015 dataset respectively.\n\n## Terms of use \nLicense to use these models 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## Model Architecture \n\nThis model is based on a relatively sophisticated text detection network called DBNet. DBNet is a network architecture for real-time scene text detection with differentiable binarization. It aims to solve the problem of text localization and segmentation in natural images with complex backgrounds and various text shapes.\n\n## Training \n\nThe training algorithm inserts the binarization operation into the segmentation network and jointly optimizes it so that\nthe network can learn to separate foreground and background pixels more effectively. The binarization threshold is learned by minimizing the IoU loss between the predicted binary map and the ground truth binary map.\n\n\n### Training Data \n\nThe trainable models were trained on the [Uber-Text](https://s3-us-west-2.amazonaws.com/uber-common-public/ubertext/index.html) dataset and [ICDAR2015](https://rrc.cvc.uab.es/?ch=4) dataset respectively. The Uber-Text dataset contains street-level images collected from car mounted sensors and truths annotated by a team of image analysts--including train_4Kx4K, train_1Kx1K, val_4Kx4K, val_1Kx1K, test_4Kx4K as the training datasets and test_1Kx1K as the validation dataset. The dataset was constructed with 107812 images for training and 10157 images for validation. The ICDAR2015 dataset contains 1000 training images and 500 test images. The deployable models were ONNX models that were exported using the trainable models.\n\n## Performance \n\n### Evaluation Data \n\nWe test the OCRNet model on three different datasets: Uber-Text + TextOCR validation dataset, ICDAR13 and ICDAR15 scene text benchmark dataset.\n\n### Methodology and KPI\n\nThe key performance indicator is the accuracy of character recognition. The accurate recognition means all the characters in a text area are recognized correctly.The KPI for the evaluation data are reported below. \n\n|model|dataset|accuracy|\n|---|---|---|\n|ocrnet_resnet50_unpruned|Uber-Text + TextOCR validation|77.1%|\n|ocrnet_resnet50_unpruned|ICDAR13|91.8%|\n|ocrnet_resnet50_unpruned|ICDAR15|78.6%|\n|ocrnet_resnet50_unpruned|Internal PCB validation|74.1%|\n|ocrnet_resnet50_pruned|ICDAR13|92.6%|\n|ocrnet-vit|Uber-Text + TextOCR validation|83.7%|\n|ocrnet-vit|ICDAR13|95.5%|\n|ocrnet-vit|ICDAR15|84.7%|\n|ocrnet-vit-pcb|Internal PCB validation|84.2%|\n\n### Real-time Inference Performance \n\nThe inference uses FP16 precision. The inference performance runs with [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/master/samples/trtexec) on AGX Orin, Orin NX, Orin Nano and NVIDIA L4 GPU. The Jetson devices run at Max-N configuration for maximum system performance. The data is the inference only performance. The end-to-end performance with streaming video data might slightly vary depending on use cases of applications.\n\n|Model|Device|precision|batch_size|FPS|\n|---|---|---|---|---|\n|ocrnet_resnet50_pruned|Orin Nano|FP16|128|981|\n|ocrnet_resnet50_pruned|Orin NX|FP16|128|1399|\n|ocrnet_resnet50_pruned|AGX Orin|FP16|128|3921|\n|ocrnet_resnet50_pruned|L4|FP16|512|6404|\n|ocrnet-vit|Orin Nano|FP16+INT8|4|100|\n|ocrnet-vit|Orin NX|FP16+INT8|4|147|\n|ocrnet-vit|AGX Orin|FP16+INT8|8|393|\n|ocrnet-vit|T4|FP32|16|680|\n|ocrnet-vit|A2|FP32|32|428|\n|ocrnet-vit|A30|FP32|32|1645|\n|ocrnet-vit|L4|FP32|32|1471|\n|ocrnet-vit|L40|FP32|32|4320|\n|ocrnet-vit|A100|FP32|32|2513|\n|ocrnet-vit|H100|FP32|32|3846|\n\n\n## How to Use This Model \n\nThis model needs to be used with NVIDIA Hardware and Software: The model can run on any NVIDIA GPU, including NVIDIA Jetson devices, with [TAO Toolkit](https://developer.nvidia.com/tao-toolkit), [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) or [TensorRT](https://developer.nvidia.com/tensorrt).\n\nThe primary use case for this model is to detect text on images.\n\nThere are two types of models provided (both unpruned).\n\n- trainable\n- deployable\n\nThe `trainable` models are intended for training with the user's own dataset using TAO Toolkit. This can provide high-fidelity models that are adapted to the use case. A Jupyter notebook is available as a part of the [TAO container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit) and can be used to re-train.\n\nThe `deployable` models share the same structure as the `trainable` model, but in `onnx` format. The `deployable` models can be deployed using TensorRT, nvOCDR, and DeepStream.\n\n\n### Input\n\nImages of C x H x W (H and W should be multiples of 32.)\n\n### Output\n\nBBox or polygon coordinates for each detected text in the input image\n\n### Instructions to Use the Model with TAO\n\nTo use these models as pretrained weights for transfer learning, use the snippet below as a template for the `model` component of the experiment spec file to train an OCDNet model. For more information on the experiment spec file, refer to the [TAO Toolkit User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\nTo use trainable_resnet18_v1.0 model:\n```yaml\nmodel:\n load_pruned_graph: False\n pruned_graph_path: '/results/prune/pruned_0.1.pth'\n pretrained_model_path: '/data/ocdnet/ocdnet_deformable_resnet18.pth'\n backbone: deformable_resnet18\n```\nTo use trainable_ocdnet_vit_v1.0 model:\n```yaml\nmodel:\n load_pruned_graph: False\n pruned_graph_path: '/results/prune/pruned_0.1.pth'\n pretrained_model_path: '/data/ocdnet/ocdnet_fan_tiny_2x_icdar.pth'\n backbone: fan_tiny_8_p4_hybrid\n enlarge_feature_map_size: True\n activation_checkpoint: True\n```\n\n### Instructions to deploy the model with DeepStream\n\nTo create the entire end-to-end video analytic application, deploy this model with [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk). DeepStream SDK is a streaming analytic toolkit to accelerate building AI-based video analytic applications. DeepStream supports direct integration of this model into the Deepstream sample app.\n\nTo deploy this model with [DeepStream](https://developer.nvidia.com/deepstream-sdk), follow [these instructions](https://docs.nvidia.com/tao/tao-toolkit/text/ds_tao/nvocdr_ds.html).\n\n## Limitations \n\n### Restricted Usage in Different Fields\n\nThe NVIDIA OCDNet trainable model is trained on Uber Text, ICDAR2015 and PCB text dataset, which contains street-view images only. To get better accuracy in a specific field, more data is usually required to fine tune the pre-trained model with TAO Toolkit.\n\n## Model versions:\n\n- **trainable_resnet18_v1.0** - Pre-trained models with deformable-resnet18 backbone, trained on Uber-Text dataset.\n- **trainable_resnet50_v1.0** - Pre-trained models with deformable-resnet50 backbone, trained on Uber-Text dataset.\n- **trainable_ocdnet_vit_v1.0** - Pre-trained models with fan-tiny backbone, trained on ICDAR2015 dataset.\n- **trainable_ocdnet_vit_v1.1** - Pre-trained models with fan-tiny backbone, trained on Uber-Text dataset.\n- **trainable_ocdnet_vit_v1.2** - Pre-trained models with fan-tiny backbone, trained on PCB dataset.\n- **trainable_ocdnet_vit_v1.3** - Pre-trained models with fan-tiny backbone, trained on ImageNet2012 dataset.\n- **trainable_ocdnet_vit_v1.4** - Pre-trained models with fan-tiny backbone, trained on ICDAR2015 dataset and model are pruned.\n- **deployable_v1.0** - Model deployable with deformable-resnet backbone.\n- **deployable_v2.0** - Model deployable with fan-tiny backbone, trained on ICDAR2015.\n- **deployable_v2.1** - Model deployable with fan-tiny backbone, trained on Uber-Text.\n- **deployable_v2.2** - Model deployable with fan-tiny backbone, trained on PCB dataset.\n- **deployable_v2.3** - Model deployable with fan-tiny backbone, trained on ICDAR2015 and model are pruned.\n\n## Reference\n\n### Citations \n\n- Liao M., Wan Z., Yao C., Chen K., Bai X.: Real-time Scene Text Detection with Differentiable Binarization (2020).\n- Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y: Deformable convolutional networks. (2017).\n- He, W., Zhang, X., Yin, F., and Liu, C.: Deep direct regression for multi-oriented scene text detection. (2017).\n- Zhang, Y., Gueguen, L., Zharkov, I., Zhang, P., Seifert, K., & Kadlec, B. (2017, July). Uber-text: A large-scale dataset for optical character recognition from street-level imagery. In SUNw: Scene Understanding Workshop-CVPR (Vol. 2017, p. 5).\n- Zhou, D., Yu, Z., Xie, E., Xiao, C., Anandkumar, A., Feng, J., & Alvarez, J. M. (2022, June). Understanding the robustness in vision transformers. In International Conference on Machine Learning (pp. 27378-27394). PMLR.\n- Kuo, C. W., Ashmore, J. D., Huggins, D., & Kira, Z. (2019, January). Data-efficient graph embedding learning for PCB component detection. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 551-560). IEEE.\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 the NGC model registry:\n - [TrafficCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:trafficcamnet)\n - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)\n - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)\n - [PeopleNet-Transformer](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet_transformer)\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:tao:facenet)\n - [2D Body Pose Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:bodyposenet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [PoseClassificationNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:poseclassificationnet)\n - [People ReIdentification](https://ngc.nvidia.com/catalog/models/nvidia:tao:reidentificationnet)\n - [PointPillarNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pointpillarnet)\n - [CitySegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/citysemsegformer)\n - [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection)\n - [Retail Object Embedding](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_recognition)\n - [Optical Inspection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/optical_inspection)\n - [Optical Character Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocdnet)\n - [Optical Character Recognition](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocrnet)\n - [PCB Classification](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pcb_classification)\n - [PeopleSemSegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplesemsegformer)\n - [LPDNet](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:tao:facenet)\n - [2D Body Pose Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:bodyposenet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [PoseClassificationNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:poseclassificationnet)\n - [People ReIdentification](https://ngc.nvidia.com/catalog/models/nvidia:tao:reidentificationnet)\n - [PointPillarNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pointpillarnet)\n - [CitySegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/citysemsegformer)\n - [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection)\n - [Retail Object Embedding](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_recognition)\n - [Optical Inspection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/optical_inspection)\n - [Optical Character Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocdnet)\n - [Optical Character Recognition](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocrnet)\n - [PCB Classification](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pcb_classification)\n - [PeopleSemSegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplesemsegformer)\n\n## License \n\nThe license to use these models 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- [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 traina [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 to 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 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 Toolkit Quick Start Guide](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_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 model on the edge using the [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk).\n\n## Ethical AI \n\nThe NVIDIA OCDNet model detects optical characters.\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.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability)","displayName","Optical Character Recognition",["D",1732745347085],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/ocrnet","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes",46420752,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","| Field | Response |\n| -- | -- |\n|Participation considerations from adversely impacted groups [(protected classes)](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | OCR: Not applicable
OCD: Not applicable |\n| Measures taken to mitigate against unwanted bias: | OCR: Not Applicable
OCD: Not Applicable |","explainability","| Field | Response |\n| -- | -- |\n| Intended Application(s) & Domain(s): | OCR: This model is intended to be used in computer vision application for recognizing the optical characters in a scene.
OCD: This model is intended to be used for detecting text in images. |\n| Model Type: | OCR: This model is intended for developers who want to recognize/identify text from image data.
OCD: This model is intended for developers who want to detect optical characters from image data. |\n| Intended Users: | OCR: This model is intended for developers who want to customize optical character recognition models.
OCD: This model is intended to be used for detecting text in images. |\n| Output: | OCR: Sequence of characters
OCD: BBox or polygon coordinates for each detected text in the input image |\n| Describe how the model works: | OCR: The training algorithm minimizes the connectionist temporal classification (CTC) loss between a ground truth character sequence from the image and a predicted characters sequence. Characters are decoded from the sequence output by a best-path decoding method.
OCD: Based on DBNet, a network architecture for real-time scene text detection, this model aims to solve the problem of text localization and segmentation in natural images with complex backgrounds and various text shapes. |\n| Technical Limitations: | OCR: This model performs best on images representing its training set: Uber Text and TextOCR. Uber Text contains street view images. TextOCR has images with text in various scenes. Further fine-tuning might be required for domain-specific accuracy.
OCD: The NVIDIA OCDNet trainable model is trained on Uber Text, which contains street-view images only. Further fine-tuning might be required for domain-specific accuracy. |\n| Verified to have met prescribed NVIDIA standards: | OCR: Yes
OCD: Yes |\n| Performance Metrics: | OCR: Accuracy
OCD: hmean |\n| Potential Risks: | OCR: None Known
OCR: None Known |\n| Licensing: | [https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/) |","privacy","| Field | Response |\n| -- | -- |\n| Generatable or reverse engineerable personally-identifiable information (PII)? | OCR: Neither
OCD: Neither |\n| If applicable, was a notice provided to the individuals prior to the collection of any personal data used? | OCR: No PII was used
OCD: Not applicable |\n| How often is the dataset reviewed? | OCR: Before Release
OCD: Before Release |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | OCR: No
OCD: No |\n| If PII was collected for the development of the model, was it collected directly by NVIDIA? | OCR: No PII was collected
OCD: No PII was collected |\n| If PII was collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | OCR: No PII was collected
OCD: No PII was collected |\n| If PII collected for the development of this AI model, was it minimized to only what was required? | OCR: No
OCD: No |\n| Is there provenance for all datasets used in training? | OCR: No
OCD: No |\n| Does data labeling (annotation, metadata) comply with privacy laws? | OCR: Yes
OCD: Yes|\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | OCR: No, not possible with externally-sourced data.
OCD: No, not possible with externally-sourced data.|\n| Applicable NVIDIA Privacy Policy | [https://www.nvidia.com/en-us/about-nvidia/privacy-policy/](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) |","safetyAndSecurity","| Field | Response |\n| -- | -- |\n| Model Application(s): | OCD: Detect visual regions containing text
OCR: Recognize the text in visual data |\n| Describe the life-critical application (if present). | OCR: No physical safety impacts.
OCD: No physical safety impacts. |\n| Use Case Restrictions: | OCD: Images that do not contain text
OCR: Images that do not contain text |\n| Describe access restrictions (if any): | None. This was trained on public datasets. |","featureLabels",[],"displayLabels",[215,216,217,218,219,220,221,222,223,224,225,226,227,228],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_178":266,"_230":267,"_231":268,"_233":234},{"_178":262,"_230":263,"_231":264,"_233":234},{"_178":260,"_230":260,"_231":261,"_233":234},{"_178":258,"_230":258,"_231":259,"_233":234},{"_178":255,"_230":256,"_231":257,"_233":234},{"_178":251,"_230":252,"_231":253,"_233":234},{"_178":249,"_230":249,"_231":250,"_233":234},{"_178":247,"_230":247,"_231":248,"_233":234},{"_178":245,"_230":245,"_231":246,"_233":234},{"_178":243,"_230":243,"_231":244,"_233":234},{"_178":240,"_230":240,"_231":241,"_233":234},{"_178":238,"_230":238,"_231":239,"_233":234},{"_178":236,"_230":236,"_231":237,"_233":234},{"_178":229,"_230":229,"_231":232,"_233":234},"Transfer Learning Toolkit","value","filter",{"_235":234,"_230":229},"category","label","field","Transfer Learning",{"_235":234,"_230":236},"TLT",{"_235":234,"_230":238},"TAO Toolkit",{"_235":242,"_230":240},"framework","TAO",{"_235":234,"_230":243},"Smart Infrastructure",{"_235":234,"_230":245},"Smart City",{"_235":234,"_230":247},"NSPECT-U6E7-WAPV",{"_235":234,"_230":249},"Metropolis","pltfm_metropolis",{"_235":254,"_230":252},"platform","DeepStream","pltfm_deepstream",{"_235":254,"_230":256},"Deep Learning",{"_235":234,"_230":258},"CV",{"_235":234,"_230":260},"Computer Vision","soln_computer_vision",{"_235":265,"_230":263},"technology","AI","soln_ai",{"_235":265,"_230":267},"appConfig",{"_1446":1447,"_303":1448},"ngcApps",[1399],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1380":1381,"_1382":1383,"_1384":1385,"_1386":1387,"_1388":281,"_79":308,"_1389":1390,"_1391":44,"_1392":90,"_1393":90,"_1394":1395,"_1396":90,"_1397":1398},"amplitude",{"_1377":1378,"_981":-7,"_1379":20},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1371":1372,"_1373":1374,"_1375":1376},"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",{"_1256":111,"_1257":1258,"_1259":1260,"_1261":1262},"announcements",[],"consentState",{"_346":1246,"_1247":90,"_1248":20,"_1249":20,"_1250":1251},"consentConfig",{"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345,"_346":347},"showContactAdmin",1783688398565,[],[],[322,323,324,325,326,327],{"_33":48,"_35":57,"_37":50,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":55,"_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":50,"_39":-5,"_41":-5,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":121,"_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. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

Under certain privacy laws, you have the right to direct us not to \"sell\" or \"share\" your personal information for targeted advertising. To opt-out of the \"sale\" and \"sharing\" of personal information through cookies, you must opt-out of optional cookies using the toggles below. To opt out of the \"sale\" and \"sharing\" of data collected by other means (e.g., online forms) you must also update your data sharing preferences through the NVIDIA Preference Center.

Click on the different category headings below to find out more and change the settings according to your preference. You cannot opt out of Required Cookies as they are deployed to ensure the proper functioning of our website (such as prompting the cookie banner and remembering your settings, etc.). By clicking \"Save and Accept\" or \"Decline All\" at the bottom, you consent to the use of cookies and other tools as described in our Cookie Policy in accordance with your settings and accept our Terms of Service (which contains important waivers). For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1238":1239,"_1240":1241,"_1242":1243,"_1244":1245},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[348,349,350,351,352],{"_353":889,"_148":890,"_110":891,"_357":90,"_358":892,"_360":893,"_362":894},{"_353":679,"_148":680,"_110":681,"_357":20,"_358":47,"_360":682,"_362":683},{"_353":579,"_148":580,"_110":581,"_357":20,"_358":121,"_360":582,"_362":583},{"_353":364,"_148":365,"_110":366,"_357":20,"_358":367,"_360":368,"_362":369},{"_353":354,"_148":355,"_110":356,"_357":20,"_358":359,"_360":361,"_362":363},"optanonGroupId","C0005","Social Media Cookies","These cookies are set by a range of social media services that we have added to the site to enable you to share our content with your friends and networks. 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.",5,[497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531],[370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386],{"_387":493,"_178":493,"_110":111,"_360":494},{"_387":488,"_178":488,"_110":111,"_360":489},{"_387":484,"_178":484,"_110":111,"_360":485},{"_387":479,"_178":479,"_110":111,"_360":480},{"_387":474,"_178":474,"_110":111,"_360":475},{"_387":462,"_178":462,"_110":111,"_360":463},{"_387":450,"_178":450,"_110":111,"_360":451},{"_387":445,"_178":445,"_110":111,"_360":446},{"_387":434,"_178":434,"_110":111,"_360":435},{"_387":429,"_178":429,"_110":111,"_360":430},{"_387":424,"_178":424,"_110":111,"_360":425},{"_387":419,"_178":419,"_110":111,"_360":420},{"_387":414,"_178":414,"_110":111,"_360":415},{"_387":409,"_178":409,"_110":111,"_360":410},{"_387":404,"_178":404,"_110":111,"_360":405},{"_387":397,"_178":397,"_110":111,"_360":398},{"_387":388,"_178":388,"_110":111,"_360":389},"hostName","amazon-adsystem.com",[390,391],{"_148":396,"_393":388,"_394":20,"_110":395},{"_148":392,"_393":388,"_394":20,"_110":395},"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",[399,400],{"_148":403,"_393":397,"_394":20,"_110":402},{"_148":401,"_393":397,"_394":20,"_110":402},"IDE","This domain is owned by Doubleclick (Google). The main business activity is: Doubleclick is Googles real time bidding advertising exchange","test_cookie","bing.com",[406],{"_148":407,"_393":404,"_394":20,"_110":408},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[411],{"_148":412,"_393":409,"_394":20,"_110":413},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","If you know anything about this host and its uses please add your contributions here. ","tiktok.com",[416],{"_148":417,"_393":414,"_394":20,"_110":418},"_ttp","TikTok is a social media platform owned by ByteDance, known for short-form video sharing. The service is used to create and share videos, and it utilizes cookies for various purposes including advertising and analytics.","t.co",[421],{"_148":422,"_393":419,"_394":20,"_110":423},"muc_ads","This domain is owned by Twitter. It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[426],{"_148":427,"_393":424,"_394":20,"_110":428},"sp","Testing","google.com",[431],{"_148":432,"_393":429,"_394":20,"_110":433},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","tags.srv.stackadapt.com",[436,437,438,439],{"_148":444,"_393":434,"_394":20,"_110":441},{"_148":443,"_393":434,"_394":20,"_110":441},{"_148":442,"_393":434,"_394":20,"_110":441},{"_148":440,"_393":434,"_394":20,"_110":441},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[447],{"_148":448,"_393":445,"_394":20,"_110":449},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. For this reason it is classified as a primarily tracking/targeting domain.","twitter.com",[452,453,454,455],{"_148":461,"_393":450,"_394":20,"_110":457},{"_148":459,"_393":450,"_394":20,"_110":460},{"_148":458,"_393":450,"_394":20,"_110":457},{"_148":456,"_393":450,"_394":20,"_110":457},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[464,465,466,467],{"_148":473,"_393":462,"_394":20,"_110":471},{"_148":472,"_393":462,"_394":20,"_110":471},{"_148":470,"_393":462,"_394":20,"_110":471},{"_148":468,"_393":462,"_394":20,"_110":469},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[476],{"_148":477,"_393":474,"_394":20,"_110":478},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[481],{"_148":482,"_393":479,"_394":20,"_110":483},"receive-cookie-deprecation","This domain is owned by im-apps.net which provides information and resources about various instant messaging (IM) applications to help users make informed decisions based on their communication needs.","influ2.com",[486],{"_148":487,"_393":484,"_394":20,"_110":428},"R","6sc.co",[490],{"_148":491,"_393":488,"_394":20,"_110":492},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. It is primarily used for tracking and targeting in marketing automation and advertising.","hushly.com",[495],{"_148":496,"_393":493,"_394":20,"_110":428},"_hly_gvid",{"_148":578,"_393":533,"_394":20,"_110":552},{"_148":576,"_393":533,"_394":20,"_110":577},{"_148":440,"_393":537,"_394":20,"_110":566},{"_148":574,"_393":533,"_394":20,"_110":575},{"_148":443,"_393":537,"_394":20,"_110":540},{"_148":572,"_393":533,"_394":20,"_110":573},{"_148":417,"_393":533,"_394":20,"_110":571},{"_148":549,"_393":570,"_394":20,"_110":550},{"_148":442,"_393":537,"_394":20,"_110":569},{"_148":442,"_393":539,"_394":20,"_110":569},{"_148":444,"_393":537,"_394":20,"_110":553},{"_148":568,"_393":533,"_394":20,"_110":552},{"_148":547,"_393":567,"_394":20,"_110":111},{"_148":440,"_393":539,"_394":20,"_110":566},{"_148":565,"_393":533,"_394":20,"_110":111},{"_148":547,"_393":537,"_394":20,"_110":111},{"_148":563,"_393":533,"_394":20,"_110":564},{"_148":561,"_393":533,"_394":20,"_110":562},{"_148":560,"_393":533,"_394":20,"_110":111},{"_148":558,"_393":533,"_394":20,"_110":559},{"_148":556,"_393":533,"_394":20,"_110":557},{"_148":555,"_393":533,"_394":20,"_110":111},{"_148":549,"_393":537,"_394":20,"_110":550},{"_148":549,"_393":554,"_394":20,"_110":550},{"_148":444,"_393":539,"_394":20,"_110":553},{"_148":551,"_393":533,"_394":20,"_110":552},{"_148":549,"_393":533,"_394":20,"_110":550},{"_148":547,"_393":539,"_394":20,"_110":548},{"_148":545,"_393":533,"_394":20,"_110":546},{"_148":543,"_393":533,"_394":20,"_110":544},{"_148":541,"_393":533,"_394":20,"_110":542},{"_148":443,"_393":539,"_394":20,"_110":540},{"_148":536,"_393":537,"_394":20,"_110":538},{"_148":535,"_393":533,"_394":20,"_110":111},{"_148":532,"_393":533,"_394":90,"_110":534},"AMCVS_xxxxxAdobeOrg","nvidia.com","This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. This is a pattern type cookie where a random 13-digit number is appended to the prefix _cs_.","_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. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_twpid","This cookie is likely used to track user identity by assigning a unique user ID when the user engages with the website. It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","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.",[619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639],[584,585,586,587,588],{"_387":479,"_178":479,"_110":111,"_360":610},{"_387":445,"_178":445,"_110":111,"_360":605},{"_387":598,"_178":598,"_110":111,"_360":599},{"_387":594,"_178":594,"_110":111,"_360":595},{"_387":589,"_178":589,"_110":111,"_360":590},"nvcrm.my.site.com",[591],{"_148":592,"_393":589,"_394":20,"_110":593},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[596],{"_148":597,"_393":594,"_394":20,"_110":428},"edge","github.com",[600,601],{"_148":604,"_393":598,"_394":20,"_110":603},{"_148":602,"_393":598,"_394":20,"_110":603},"logged_in","This domain is owned by Github. The main business activity is: Source code repository and collaboration platform","_octo",[606,607],{"_148":609,"_393":445,"_394":20,"_110":449},{"_148":608,"_393":445,"_394":20,"_110":449},"lidc","li_gc",[611,612,613,614],{"_148":618,"_393":479,"_394":20,"_110":483},{"_148":617,"_393":479,"_394":20,"_110":483},{"_148":616,"_393":479,"_394":20,"_110":483},{"_148":615,"_393":479,"_394":20,"_110":483},"imid_secure","imid_created_secure","imid","imid_created",{"_148":678,"_393":533,"_394":20,"_110":675},{"_148":676,"_393":533,"_394":20,"_110":677},{"_148":674,"_393":533,"_394":20,"_110":675},{"_148":672,"_393":533,"_394":90,"_110":673},{"_148":670,"_393":641,"_394":20,"_110":671},{"_148":669,"_393":650,"_394":90,"_110":111},{"_148":667,"_393":641,"_394":20,"_110":668},{"_148":665,"_393":533,"_394":20,"_110":666},{"_148":662,"_393":663,"_394":20,"_110":664},{"_148":661,"_393":537,"_394":20,"_110":111},{"_148":659,"_393":533,"_394":20,"_110":660},{"_148":656,"_393":657,"_394":20,"_110":658},{"_148":654,"_393":655,"_394":20,"_110":111},{"_148":653,"_393":641,"_394":20,"_110":111},{"_148":652,"_393":533,"_394":20,"_110":111},{"_148":651,"_393":537,"_394":20,"_110":111},{"_148":649,"_393":650,"_394":90,"_110":111},{"_148":647,"_393":641,"_394":20,"_110":648},{"_148":645,"_393":533,"_394":90,"_110":646},{"_148":643,"_393":533,"_394":20,"_110":644},{"_148":640,"_393":641,"_394":90,"_110":642},"nv_country_code","developer.nvidia.com","This cookie is likely used to store the country code of the visitor to provide localized content and possibly for legal compliance with regional data privacy laws.","_biz_kvpA","This cookie is likely related to business analytics and key-value pairs for storing user preferences, which may be used to enhance functionality and user experience on the website.","_gdxxxxxxxxxxxxx","This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv.modal.cuda","This cookie name suggests it is related to a modal popup functionality on NVIDIA's website, possibly to remember user preferences or interactions with the modal.","readme_library","docs.api.nvidia.com","_im_ses.1006927","geo_locator","nv.modal.deepstream","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","_im_id.1006927","_hjUserAttributesHash","docs.nvidia.com","This cookie name is associated with Hotjar which is used to know when any user attribute has changed and needs to be updated.","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","nv.modal.cudnn","This cookie name suggests it is related to modal popup handling or user interface preferences on NVIDIA's website, specifically for the cuDNN library section.","readme_language","nv.modal.hpc","This cookie name suggests it is related to a modal pop-up on a website, possibly to control the display of a high-priority cookie notice or other important information to the user.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","_biz_uid","This cookie is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.\n","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","_biz_nA","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807],[684,685,686,687,688,689],{"_387":710,"_178":710,"_110":111,"_360":711},{"_387":705,"_178":705,"_110":111,"_360":706},{"_387":701,"_178":701,"_110":111,"_360":702},{"_387":462,"_178":462,"_110":111,"_360":698},{"_387":598,"_178":598,"_110":111,"_360":695},{"_387":409,"_178":409,"_110":111,"_360":690},[691,692],{"_148":694,"_393":409,"_394":20,"_110":413},{"_148":693,"_393":409,"_394":20,"_110":413},"ncid","VisitorID",[696],{"_148":697,"_393":598,"_394":90,"_110":603},"_gh_sess",[699],{"_148":700,"_393":462,"_394":90,"_110":471},"YSC","t.contentsquare.net",[703],{"_148":704,"_393":701,"_394":90,"_110":557},"_cs_same_site","bizible.com",[707],{"_148":708,"_393":705,"_394":20,"_110":709},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[712],{"_148":713,"_393":710,"_394":20,"_110":714},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_148":887,"_393":533,"_394":20,"_110":888},{"_148":826,"_393":537,"_394":20,"_110":646},{"_148":860,"_393":641,"_394":90,"_110":861},{"_148":886,"_393":533,"_394":20,"_110":863},{"_148":885,"_393":533,"_394":20,"_110":863},{"_148":858,"_393":663,"_394":90,"_110":859},{"_148":811,"_393":537,"_394":20,"_110":111},{"_148":841,"_393":537,"_394":20,"_110":646},{"_148":826,"_393":641,"_394":20,"_110":646},{"_148":826,"_393":554,"_394":20,"_110":838},{"_148":828,"_393":537,"_394":20,"_110":660},{"_148":826,"_393":881,"_394":20,"_110":646},{"_148":814,"_393":554,"_394":20,"_110":646},{"_148":828,"_393":831,"_394":20,"_110":660},{"_148":883,"_393":533,"_394":20,"_110":884},{"_148":882,"_393":533,"_394":20,"_110":557},{"_148":841,"_393":881,"_394":20,"_110":646},{"_148":826,"_393":823,"_394":20,"_110":838},{"_148":814,"_393":881,"_394":20,"_110":646},{"_148":858,"_393":554,"_394":90,"_110":859},{"_148":866,"_393":641,"_394":20,"_110":111},{"_148":880,"_393":533,"_394":20,"_110":552},{"_148":814,"_393":641,"_394":20,"_110":646},{"_148":841,"_393":650,"_394":20,"_110":872},{"_148":878,"_393":533,"_394":20,"_110":879},{"_148":841,"_393":823,"_394":20,"_110":872},{"_148":841,"_393":850,"_394":20,"_110":646},{"_148":876,"_393":533,"_394":90,"_110":877},{"_148":841,"_393":554,"_394":20,"_110":646},{"_148":826,"_393":850,"_394":20,"_110":646},{"_148":858,"_393":537,"_394":90,"_110":859},{"_148":826,"_393":650,"_394":20,"_110":838},{"_148":822,"_393":641,"_394":20,"_110":111},{"_148":874,"_393":650,"_394":20,"_110":875},{"_148":858,"_393":850,"_394":90,"_110":859},{"_148":819,"_393":873,"_394":20,"_110":820},{"_148":841,"_393":539,"_394":20,"_110":872},{"_148":819,"_393":537,"_394":20,"_110":820},{"_148":870,"_393":533,"_394":90,"_110":871},{"_148":866,"_393":663,"_394":20,"_110":111},{"_148":869,"_393":641,"_394":90,"_110":840},{"_148":867,"_393":533,"_394":90,"_110":868},{"_148":841,"_393":831,"_394":20,"_110":646},{"_148":866,"_393":537,"_394":20,"_110":111},{"_148":864,"_393":533,"_394":20,"_110":865},{"_148":814,"_393":831,"_394":20,"_110":646},{"_148":862,"_393":533,"_394":20,"_110":863},{"_148":825,"_393":537,"_394":20,"_110":111},{"_148":860,"_393":533,"_394":90,"_110":861},{"_148":858,"_393":641,"_394":90,"_110":859},{"_148":856,"_393":533,"_394":20,"_110":857},{"_148":810,"_393":537,"_394":20,"_110":111},{"_148":836,"_393":533,"_394":20,"_110":837},{"_148":854,"_393":533,"_394":90,"_110":855},{"_148":851,"_393":852,"_394":20,"_110":853},{"_148":819,"_393":641,"_394":20,"_110":820},{"_148":814,"_393":850,"_394":20,"_110":646},{"_148":814,"_393":539,"_394":20,"_110":824},{"_148":841,"_393":827,"_394":20,"_110":646},{"_148":849,"_393":533,"_394":20,"_110":557},{"_148":814,"_393":827,"_394":20,"_110":646},{"_148":821,"_393":537,"_394":20,"_110":111},{"_148":847,"_393":533,"_394":20,"_110":848},{"_148":844,"_393":845,"_394":20,"_110":846},{"_148":842,"_393":533,"_394":90,"_110":843},{"_148":811,"_393":641,"_394":20,"_110":111},{"_148":841,"_393":641,"_394":20,"_110":646},{"_148":839,"_393":641,"_394":90,"_110":840},{"_148":814,"_393":650,"_394":20,"_110":824},{"_148":826,"_393":539,"_394":20,"_110":838},{"_148":836,"_393":663,"_394":90,"_110":837},{"_148":834,"_393":533,"_394":90,"_110":835},{"_148":832,"_393":533,"_394":20,"_110":833},{"_148":826,"_393":831,"_394":20,"_110":646},{"_148":829,"_393":533,"_394":20,"_110":830},{"_148":821,"_393":641,"_394":20,"_110":111},{"_148":828,"_393":533,"_394":20,"_110":660},{"_148":825,"_393":641,"_394":20,"_110":111},{"_148":826,"_393":827,"_394":20,"_110":646},{"_148":822,"_393":537,"_394":20,"_110":111},{"_148":825,"_393":663,"_394":20,"_110":111},{"_148":810,"_393":663,"_394":20,"_110":111},{"_148":814,"_393":823,"_394":20,"_110":824},{"_148":822,"_393":663,"_394":20,"_110":111},{"_148":821,"_393":663,"_394":20,"_110":111},{"_148":819,"_393":567,"_394":20,"_110":820},{"_148":817,"_393":533,"_394":20,"_110":818},{"_148":815,"_393":533,"_394":20,"_110":816},{"_148":814,"_393":537,"_394":20,"_110":646},{"_148":812,"_393":533,"_394":90,"_110":813},{"_148":811,"_393":663,"_394":20,"_110":111},{"_148":810,"_393":641,"_394":20,"_110":111},{"_148":808,"_393":533,"_394":20,"_110":809},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","__sec__token","__sec__cid","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","_gd_visitor","_biz_dfsA","This cookie is associated with CloudFlare, used to speed up page load times. It is not used to track users but rather to limit the number of requests that have to be made to doubleclick.net.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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).","__sec_tid","__sec__fid","crowdsource.nvidia.com","This cookie name is associated with 6sense which is used to collect visitor data related to the user’s visit to the website, such as number of visits, average time spent on the website and which pages have been loaded, for the purpose of serving targeted advertisements.This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","__sec__ghost","_gd_svisitor","info.nvidia.com","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.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. It is either correlated to the 6suuid or a unique value, and can be changed during the lifetime of this cookie.This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","s_ips","This cookie is associated with Adobe Analytics. The cookie is used for measuring a visitor’s scroll activity to see how much of a page they view before moving on to another page.","_gd_session","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","dmn_chk_-xxxxxxxxxxxx","com","Posthog Analytics","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_cs_id","blogs.nvidia.com","_session_id","resources.nvidia.com","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. This ensures that behavior in subsequent visits to the same site will be attributed to the same user ID.","_hjHasCachedUserAttributes","This cookie name is associated with Hotjar which is used to know whether the data set in _hjUserAttributes Local Storage item is up to date or not.","s_ppv","This cookie is associated with Adobe Analytics site tracking and is used to measure a user's scroll activity to see how much of a page they view before moving to another page.","nv.referrerHref","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","_gid","This cookie name is associated with Google Universal Analytics. This appears to be a new cookie and as of Spring 2017 no information is available from Google. It appears to store and update a unique value for each page visited.","__sec_crid","_hjTLDTest","When the Hotjar script executes we try to determine the most generic cookie path we should use, instead of the page hostname. This is done so that cookies can be shared across subdomains (where applicable). To determine this, we try to store the _hjTLDTest cookie for different URL substring alternatives until it fails. After this check, the cookie is removed.","s_tp","s_plt","This cookie is used to measure the time taken for pages to load, allowing website owners to identify slow loading pages and improve user experience.","This _gd_session cookie is used to collect visitor data related to the user's visit to the website, such as number of visits, average time spent on the website and which pages have been loaded, for the purpose of serving targeted advertisements.This cookie contains no identifying information and is used for diagnostic purposes by Impact Radius, a service we use to track signup commissions for our affiliate program. We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","catalog.ngc.nvidia.com","ekfls-2025-03-27","This cookie name is associated with readme.io which is used to help maintain data for the API reference support page.","s_sq","Adobe Site Catalyst cookie, stores information about the previous link clicked within the site","s_getNewRepeat","This cookie is used to determine whether a visitor is a new visitor or a repeat visitor to the site, and is used for analytical purposes.","AMP_92dea1e784","nvidianews.nvidia.com","_cs_s","_hly_sid","This cookie is likely associated with website analytics, as the prefix '_hly_' suggests a connection to Holistics, a business intelligence and analytics platform.","nv.buttonReferrer","nv.referrerId","_hjSession_xxxxxx","A cookie that holds the current session data. This ensues that subsequent requests within the session window will be attributed to the same Hotjar session.","C0001","Required Cookies","These cookies enable core functionality such as security, network management, and accessibility. These cookies are required for the site to function and cannot be turned off.",1,[1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103,1104,1105,1106,1107,1108,1109,1110,1111,1112,1113,1114,1115,1116,1117,1118,1119,1120,1121,1122,1123,1124,1125,1126,1127,1128,1129,1130,1131],[895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919],{"_387":1038,"_178":1038,"_110":111,"_360":1039},{"_387":1032,"_178":1032,"_110":111,"_360":1033},{"_387":1020,"_178":1020,"_110":111,"_360":1021},{"_387":1017,"_178":1017,"_110":111,"_360":1018},{"_387":1014,"_178":1014,"_110":111,"_360":1015},{"_387":1009,"_178":1009,"_110":111,"_360":1010},{"_387":1004,"_178":1004,"_110":111,"_360":1005},{"_387":462,"_178":462,"_110":111,"_360":1001},{"_387":997,"_178":997,"_110":111,"_360":998},{"_387":450,"_178":450,"_110":111,"_360":995},{"_387":445,"_178":445,"_110":111,"_360":993},{"_387":988,"_178":988,"_110":111,"_360":989},{"_387":983,"_178":983,"_110":111,"_360":984},{"_387":978,"_178":978,"_110":111,"_360":979},{"_387":975,"_178":975,"_110":111,"_360":976},{"_387":419,"_178":419,"_110":111,"_360":973},{"_387":965,"_178":965,"_110":111,"_360":966},{"_387":594,"_178":594,"_110":111,"_360":959},{"_387":953,"_178":953,"_110":111,"_360":954},{"_387":949,"_178":949,"_110":111,"_360":950},{"_387":944,"_178":944,"_110":111,"_360":945},{"_387":940,"_178":940,"_110":111,"_360":941},{"_387":935,"_178":935,"_110":111,"_360":936},{"_387":589,"_178":589,"_110":111,"_360":928},{"_387":920,"_178":920,"_110":111,"_360":921},"pexels.com",[922,923],{"_148":926,"_393":920,"_394":20,"_110":927},{"_148":924,"_393":920,"_394":90,"_110":925},"_cfuvid","Pexels provides high quality and completely free stock photos licensed under the Pexels license. These photos are often used for personal and commercial purposes.","__cf_bm","This is a CloudFoundry cookie",[929,930],{"_148":933,"_393":589,"_394":20,"_110":934},{"_148":931,"_393":589,"_394":20,"_110":932},"CookieConsentPolicy","Used to apply end-user cookie consent preferences set by our client-side utility.","LSKey-c$CookieConsentPolicy","Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","www.recaptcha.net",[937],{"_148":938,"_393":935,"_394":20,"_110":939},"_GRECAPTCHA","This domain is used by Google reCAPTCHA, which provides spam protection services to websites by detecting if a user is a human or a bot.","api2.hcaptcha.com",[942],{"_148":943,"_393":940,"_394":20,"_110":428},"__cflb","instagram.com",[946],{"_148":947,"_393":944,"_394":20,"_110":948},"csrftoken","Instagram is a social networking service owned by Facebook, Inc. It's designed for sharing photos and videos from a smartphone. The service also uses cookies for various purposes, including analytics, personalization, and advertising.","readme.io",[951],{"_148":926,"_393":949,"_394":20,"_110":952},"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.","nvidiacorp.us-5.evergage.com",[955,956],{"_148":958,"_393":953,"_394":20,"_110":428},{"_148":957,"_393":953,"_394":20,"_110":428},"AWSALBTG","AWSALBTGCORS",[960,961],{"_148":963,"_393":594,"_394":20,"_110":964},{"_148":962,"_393":594,"_394":20,"_110":428},"ak_bmsc","bm_mi","This domain is likely associated with NVIDIA's partner program, which could involve marketing, sales, and support services for partners.","app.hushly.com",[967,968,969],{"_148":972,"_393":965,"_394":20,"_110":428},{"_148":971,"_393":965,"_394":90,"_110":428},{"_148":970,"_393":965,"_394":20,"_110":428},"AWSALBCORS","JSESSIONID","AWSALB",[974],{"_148":926,"_393":419,"_394":20,"_110":927},"hcaptcha.com",[977],{"_148":926,"_393":975,"_394":20,"_110":927},"tracking.intentsify.io",[980],{"_148":981,"_393":978,"_394":20,"_110":982},"userId","Used to track usage of the embedded video player.","static.reo.dev",[985],{"_148":986,"_393":983,"_394":20,"_110":987},"cookietest","Common cookie name could have a number of different origins. Where this is first party and a session cookie, its most likely to do with checking to see if the browser is set to block or allow cookies.","www.datadoghq-browser-agent.com",[990],{"_148":991,"_393":988,"_394":20,"_110":992},"dd_cookie_test_","dd_cookie_test",[994],{"_148":926,"_393":445,"_394":20,"_110":927},[996],{"_148":926,"_393":450,"_394":20,"_110":927},"mynvidia.force.com",[999,1000],{"_148":933,"_393":997,"_394":20,"_110":934},{"_148":931,"_393":997,"_394":20,"_110":932},[1002],{"_148":1003,"_393":462,"_394":20,"_110":471},"VISITOR_PRIVACY_METADATA","prod.cloudmatchbeta.nvidiagrid.net",[1006],{"_148":1007,"_393":1004,"_394":90,"_110":1008},"SERVERID","Usually used for load balancing. Identifies the server that delivered the last page to the browser. Associated with the HAProxy Load Balancer software.","dosa-prd.azurewebsites.net",[1011],{"_148":1012,"_393":1009,"_394":20,"_110":1013},"access_token","This cookie is used to obtain the permissions for users of the portal to allow for API access in order to retrieve information required to perform portal functions.","w.hcaptcha.com",[1016],{"_148":926,"_393":1014,"_394":20,"_110":927},"cdn.readme.io",[1019],{"_148":926,"_393":1017,"_394":20,"_110":927},"queue-it.net",[1022,1023,1024,1025,1026],{"_148":1031,"_393":1020,"_394":20,"_110":111},{"_148":1030,"_393":1020,"_394":20,"_110":111},{"_148":1029,"_393":1020,"_394":20,"_110":111},{"_148":1028,"_393":1020,"_394":20,"_110":111},{"_148":1027,"_393":1020,"_394":20,"_110":111},"Queue-it-{queueId}","Queue-it-{dmonvidia }________{eventId}","Queue-it-token","queueitsoftblock_{queueId}","Queue-it-{nvidia}________{eventId}","api.hcaptcha.com",[1034,1035],{"_148":1037,"_393":1032,"_394":20,"_110":428},{"_148":943,"_393":1032,"_394":20,"_110":1036},"This domain is associated with hCaptcha, a service that provides CAPTCHA and reCAPTCHA alternatives for user verification and bot protection.","hmt_id","vimeo.com",[1040,1041],{"_148":926,"_393":1038,"_394":20,"_110":927},{"_148":924,"_393":1038,"_394":90,"_110":1042},"This domain is owned by Vimeo. The main business activity is: Video Hosting/Sharing",{"_148":1228,"_393":1198,"_394":90,"_110":1229},{"_148":1157,"_393":533,"_394":20,"_110":1237},{"_148":1235,"_393":827,"_394":90,"_110":1236},{"_148":1224,"_393":1191,"_394":90,"_110":1225},{"_148":1234,"_393":567,"_394":90,"_110":111},{"_148":1233,"_393":533,"_394":90,"_110":552},{"_148":1231,"_393":533,"_394":20,"_110":1232},{"_148":958,"_393":1212,"_394":20,"_110":1143},{"_148":1184,"_393":873,"_394":90,"_110":1230},{"_148":1228,"_393":1191,"_394":90,"_110":1229},{"_148":1150,"_393":1223,"_394":90,"_110":1152},{"_148":957,"_393":1142,"_394":20,"_110":1143},{"_148":931,"_393":1223,"_394":20,"_110":932},{"_148":1226,"_393":533,"_394":90,"_110":1227},{"_148":1224,"_393":1198,"_394":90,"_110":1225},{"_148":933,"_393":1223,"_394":20,"_110":1149},{"_148":1221,"_393":533,"_394":20,"_110":1222},{"_148":957,"_393":1134,"_394":20,"_110":1143},{"_148":1139,"_393":1158,"_394":20,"_110":1141},{"_148":1219,"_393":533,"_394":20,"_110":1220},{"_148":970,"_393":1140,"_394":20,"_110":1155},{"_148":958,"_393":1134,"_394":20,"_110":1143},{"_148":972,"_393":1140,"_394":20,"_110":1216},{"_148":931,"_393":1148,"_394":20,"_110":932},{"_148":1217,"_393":641,"_394":20,"_110":1218},{"_148":958,"_393":1163,"_394":20,"_110":1164},{"_148":972,"_393":850,"_394":20,"_110":1216},{"_148":1214,"_393":533,"_394":20,"_110":1215},{"_148":963,"_393":850,"_394":20,"_110":1208},{"_148":1181,"_393":831,"_394":90,"_110":111},{"_148":1213,"_393":881,"_394":90,"_110":111},{"_148":957,"_393":1212,"_394":20,"_110":1143},{"_148":1210,"_393":1142,"_394":90,"_110":1211},{"_148":1169,"_393":533,"_394":20,"_110":1156},{"_148":1139,"_393":533,"_394":20,"_110":1141},{"_148":1209,"_393":1162,"_394":20,"_110":111},{"_148":963,"_393":533,"_394":20,"_110":1208},{"_148":972,"_393":1134,"_394":20,"_110":1207},{"_148":1205,"_393":852,"_394":90,"_110":1206},{"_148":1203,"_393":881,"_394":90,"_110":1204},{"_148":1201,"_393":533,"_394":90,"_110":1202},{"_148":1200,"_393":533,"_394":20,"_110":111},{"_148":1157,"_393":1140,"_394":20,"_110":1159},{"_148":1199,"_393":1162,"_394":20,"_110":111},{"_148":1190,"_393":1198,"_394":90,"_110":1192},{"_148":1196,"_393":831,"_394":20,"_110":1197},{"_148":1194,"_393":567,"_394":20,"_110":1195},{"_148":1193,"_393":554,"_394":20,"_110":111},{"_148":1190,"_393":1191,"_394":90,"_110":1192},{"_148":1188,"_393":533,"_394":20,"_110":1189},{"_148":1186,"_393":1148,"_394":90,"_110":1187},{"_148":1184,"_393":554,"_394":90,"_110":1185},{"_148":1182,"_393":533,"_394":20,"_110":1183},{"_148":1181,"_393":537,"_394":90,"_110":111},{"_148":933,"_393":1151,"_394":20,"_110":1149},{"_148":962,"_393":1170,"_394":20,"_110":1156},{"_148":1179,"_393":537,"_394":20,"_110":1180},{"_148":1178,"_393":533,"_394":20,"_110":675},{"_148":991,"_393":873,"_394":20,"_110":1172},{"_148":1176,"_393":533,"_394":20,"_110":1177},{"_148":1174,"_393":533,"_394":20,"_110":1175},{"_148":1173,"_393":533,"_394":20,"_110":111},{"_148":991,"_393":537,"_394":20,"_110":1172},{"_148":1169,"_393":850,"_394":20,"_110":1156},{"_148":926,"_393":827,"_394":20,"_110":1171},{"_148":1169,"_393":1170,"_394":20,"_110":1156},{"_148":1168,"_393":1162,"_394":20,"_110":111},{"_148":1167,"_393":533,"_394":20,"_110":1137},{"_148":1165,"_393":1148,"_394":20,"_110":1166},{"_148":957,"_393":1163,"_394":20,"_110":1164},{"_148":1161,"_393":1162,"_394":20,"_110":111},{"_148":1160,"_393":533,"_394":20,"_110":111},{"_148":1150,"_393":1148,"_394":90,"_110":1152},{"_148":962,"_393":533,"_394":20,"_110":1156},{"_148":1157,"_393":1158,"_394":20,"_110":1159},{"_148":962,"_393":850,"_394":20,"_110":1156},{"_148":970,"_393":850,"_394":20,"_110":1155},{"_148":1153,"_393":533,"_394":20,"_110":1154},{"_148":931,"_393":1151,"_394":20,"_110":932},{"_148":1150,"_393":1151,"_394":90,"_110":1152},{"_148":933,"_393":1148,"_394":20,"_110":1149},{"_148":1146,"_393":1142,"_394":90,"_110":1147},{"_148":1144,"_393":655,"_394":20,"_110":1145},{"_148":958,"_393":1142,"_394":20,"_110":1143},{"_148":1139,"_393":1140,"_394":20,"_110":1141},{"_148":926,"_393":650,"_394":20,"_110":1138},{"_148":1136,"_393":533,"_394":20,"_110":1137},{"_148":970,"_393":1134,"_394":20,"_110":1135},{"_148":1132,"_393":533,"_394":20,"_110":1133},"LoginHints","Keeps users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","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.\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. It is a temporary cookie used to determine if the browser accepts cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","login_session","bm_ss","This cookie is used to help protect websites from automated bot traffic.","AWS ELB application load balancerUsed 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.","_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. Usually used to maintain an anonymised user session by the server.","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","ApplicationGatewayAffinityCORS","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure all requests from a user during the session are sent to the same server.","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.Supports Amplitude product analytics.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","AMP_TLDTEST","c_code","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. 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","

NVIDIA uses cookies to improve your experience on our web site. We and our third-party partners also use cookies and other tools to collect and record information you provide as well as information about your interactions with our websites for performance improvement, analytics, and to assist in marketing efforts. By clicking \"Accept All\", you consent to our use of cookies and other tools as described in our Cookie Policy. You can manage your cookie settings by clicking on \"Manage Settings.\" By continuing to use this site or by clicking one of the buttons below, you agree to our Terms of Service (which contains important waivers). Please see our Privacy Policy for more information on our privacy practices.

","gpc","

We have detected the Global Privacy Control (GPC) signal and recorded your rejection of all optional cookies on this site for this browser. You can manage your cookie settings by clicking on \"Manage Settings\". Please see our Cookie Policy for more information. To opt out of non-cookie personal information \"sales\" / \"sharing\" for targeted advertising purposes, please visit the NVIDIA Preference Center. Please see our Privacy Policy for more information on our privacy practices.

","gpcLoggedIn","

We have detected the Global Privacy Control Signal (GPC) and have opted you out of all optional cookies on this browser. You can manage your cookie settings by clicking on \"Manage Settings\". Please see our Cookie Policy for more information. We have also opted you out of \"sharing\"/\"sales\" of personal information outside of cookies. You can manage these settings in the NVIDIA NVIDIA Preference Center. Please see our Privacy Policy for more information.

","gpcLoggedInOverridden","

We have detected the Global Privacy Control Signal (GPC) and have opted you out of all optional cookies on this browser. You can manage your cookie settings by clicking on \"Manage Settings\". Please see our Cookie Policy for more information. We have also opted you out of \"sharing\"/\"sales\" of personal information outside of cookies which overrides at least one of your previous settings. You can manage them in the NVIDIA Preference Center. Please see our Privacy Policy for more information.

",{"_889":892,"_679":44,"_579":44,"_364":44,"_354":44},"hasConsented","gpcApplied","showBanner","geolocation",{"_1252":1253,"_1254":1255},"country","US","state","OR","query","categories",[1275,1276,1277,1278,1279],"optionUrls",[1263,1264,1265,1266,1267,1268,1269,1270,1271,1272,1273,1274],"total",12,"/orgs/nim/deepseek-ai/containers/deepseek-r1/-","/orgs/nim/nvidia/containers/llama-3.1-nemotron-70b-instruct/-","/orgs/nvidia/-/containers/pytorch/-","/orgs/nvidia/omniverse/collections/kit/-","/orgs/nvidia/-/collections/deepstream_sdk/-","/orgs/nvidia/omniverse/collections/kit-appstreaming-collection/-","/orgs/nvidia/research/models/stylegan3/-","/orgs/nvidia/tao/models/peoplenet/-","/orgs/nvidia/tao/models/trafficcamnet/-","/orgs/nvidia/riva/resources/riva_quickstart/-","/orgs/nvidia/-/helm-charts/gpu-operator/-","/orgs/nvidia/-/helm-charts/k8s-nim-operator/-",{"_171":1351,"_1281":1352,"_1283":44},{"_171":1316,"_1281":1317,"_1283":47},{"_171":172,"_1281":1301,"_1283":1302},{"_171":1294,"_1281":1295,"_1283":71},{"_171":1280,"_1281":1282,"_1283":1284},"helm-charts","items",[1285,1286],"startIndex",10,{"_171":1280,"_178":1291,"_104":105,"_106":-7,"_173":1292,"_181":182,"_212":1293,"_1290":1273},{"_171":1280,"_178":1287,"_104":105,"_106":-7,"_173":1288,"_181":182,"_212":1289,"_1290":1274},"NVIDIA NIM Operator","k8s-nim-operator",[],"url","GPU Operator","gpu-operator",[],"resources",[1296],{"_171":1294,"_178":1297,"_104":105,"_106":1298,"_173":1299,"_181":182,"_212":1300,"_1290":1272},"Riva Skills Quick Start","riva","riva_quickstart",[],[1303,1304,1305],6,{"_171":172,"_178":1312,"_104":105,"_106":1313,"_173":1314,"_181":182,"_212":1315,"_1290":1269},{"_171":172,"_178":1309,"_104":105,"_106":107,"_173":1310,"_181":182,"_212":1311,"_1290":1270},{"_171":172,"_178":1306,"_104":105,"_106":107,"_173":1307,"_181":182,"_212":1308,"_1290":1271},"TrafficCamNet","trafficcamnet",[],"PeopleNet","peoplenet",[],"StyleGAN3 pretrained models","research","stylegan3",[],"collections",[1318,1319,1320],{"_171":1316,"_178":1348,"_104":105,"_106":1322,"_173":1349,"_181":182,"_212":1350,"_1290":1266},{"_171":1316,"_178":1345,"_104":105,"_106":-7,"_173":1346,"_181":182,"_212":1347,"_1290":1267},{"_171":1316,"_178":1321,"_104":105,"_106":1322,"_173":1323,"_181":182,"_212":1324,"_1290":1268},"Omniverse Kit App Streaming","omniverse","kit-appstreaming-collection",[1325,1326,1327],{"_148":1339,"_178":1340,"_175":1341,"_186":1342,"_1332":1343},{"_148":1322,"_178":1335,"_175":1336,"_186":1331,"_1332":1337},{"_148":1328,"_178":1329,"_175":1330,"_186":1331,"_1332":1333},"omniverse-dev","NVIDIA Developer Program","Join the Developer Program for access to free tools, support, and tech resources.","https://store-images.s-microsoft.com/image/apps.33936.6f1bcf42-55d0-45d1-b4b6-f08c68a50bbe.e91ca707-db64-49d6-9761-4781590a9685.7ba64fc5-7c44-4864-a5e6-7bde05126935","supportedFeatures",[1334],"UNIFIED_CATALOG","NVIDIA Omniverse","NVIDIA Omniverse is a platform of APIs, services, and software development kits (SDKs) that enable developers to build generative AI-enabled tools and applications.",[1334,1338],"SHAREABLE_TO_USER_GROUPS","nv-ai-enterprise","NVIDIA AI Enterprise","Accelerate your AI agent development","https://assets.ngc.nvidia.com/products/catalog/images/product-NVAIE.jpg",[1334,1338,1344],"NGC_CONSOLE","DeepStream SDK","deepstream_sdk",[],"Omniverse Kit (FB)","kit",[1325,1326,1327],"containers",[1353,1354,1355],{"_171":1351,"_178":1367,"_104":1360,"_106":1368,"_173":1369,"_181":182,"_212":1370,"_1290":1263},{"_171":1351,"_178":1359,"_104":1360,"_106":105,"_173":1361,"_181":182,"_212":1362,"_1290":1264},{"_171":1351,"_178":1356,"_104":105,"_106":-7,"_173":1357,"_181":182,"_212":1358,"_1290":1265},"PyTorch","pytorch",[],"Llama-3.1-Nemotron-70B-Instruct","nim","llama-3.1-nemotron-70b-instruct",[1363,1325],{"_148":1364,"_178":1329,"_175":1330,"_186":1365,"_1332":1366},"nim-dev","https://assets.ngc.nvidia.com/products/catalog/images/product-nim-dev.jpg",[1334],"DeepSeek-R1","deepseek-ai","deepseek-r1",[1363,1325],"ownerOrgs",[],"memberOrgs",[],"externalOrgs",[],"apiKey","2b44f86fea50e90ffd2db29ee2028419","consentGranted","applicationId","4c857e4f-5ffa-45fa-b75e-af773c22f0ad","clientToken","pubb6927db33dae60ba5031a186cde0242a","site","datadoghq.com","service","catalog-ui","env","sessionSampleRate",100,"sessionReplaySampleRate","compressIntakeRequests","storeContextsAcrossPages","defaultPrivacyLevel","mask-user-input","trackViewsManually","trackingConsent","not-granted",{"_1400":1401,"_1402":302,"_1403":20,"_1404":1405,"_1281":1406,"_1407":1408,"_1409":1401,"_1410":20,"_1411":20,"_1412":20,"_1413":1414},"appName","ngc-catalog","appOrigin","hideOnPrivateOrg","icon","ObjectsModules2",[1415,1416,1417,1418,1419,1420,1421],"menuIcon","editor-swatches","productName","requiresAllScopes","requiresLogin","requiresOrgLevelScopesOnly","title","Catalog",{"_1413":1443,"_35":1444,"_1407":1445},{"_1436":1437,"_1413":1438,"_35":1439,"_1409":1339},{"_1413":1434,"_35":1435,"_1407":1408},{"_1413":1431,"_35":1432,"_1407":1433},{"_1413":1428,"_35":1429,"_1407":1430},{"_1413":1425,"_35":1426,"_1407":1427},{"_1413":1422,"_35":1423,"_1407":1424},"Resources","/resources","misc-library","Models","/models","misc-neural-network","Helm Charts","/helm-charts","misc-ship-wheel","Containers","/containers","3d-cube","Collections","/collections","access",[1440],"Enterprise Catalog","/enterprise",[1441,1442],"NVAIE","READ","Explore Catalog","/","common-home","urls",{"_1450":1451,"_1452":298,"_1453":300,"_1454":1455,"_1456":1457,"_1458":1459},{"_1449":20},"loggedIn","build","https://build.nvidia.com","ngcApi","ngcHelm","ngcRegistry","https://registry.ngc.nvidia.com","orgUi","https://org.ngc.nvidia.com","ngcUi","https://ngc.nvidia.com"]