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Getting Start with U-Net Industrial

Image Segmentation

Image segmentation is the process of partitioning a digital image into multiple segments. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image.

Model Overview

This U-Net model is adapted from the original version of the U-Net model https://arxiv.org/abs/1505.04597 which is a convolutional auto-encoder for 2D image segmentation.

This work proposes a modified version of U-Net, called TinyUNet which performs efficiently and with very high accuracy on the industrial anomaly dataset DAGM2007. TinyUNet, like the original U-Net is composed of two parts:

An encoding sub-network\nA decoding sub-network:\nWhat is encoder decoder in deep learning models? The Encoder converts the input sequence into a single dimensional vector. The decoder uses the output of the encoder as the input and it converts this vector into the output sequence.

This model repeatedly applies 3 downsampling blocks composed of two 2D convolutions followed by a 2D max pooling layer in the encoding sub-network. In the decoding sub-network, 3 upsampling blocks are composed of a upsample2D layer followed by a 2D convolution, a concatenation operation with the residual connection and two 2D convolutions.

Default Configuration

This model trains in 2500 epochs, under the following setup:

\n
\n
Global Batch Size: 16
\n
Optimizer RMSProp:\ndecay: 0.9\nmomentum: 0.8\ncentered: True
\n
Learning Rate Schedule: Exponential Step Decay\ndecay: 0.8\nsteps: 500\ninitial learning rate: 1e-4
\n
Weight Initialization: He Uniform Distribution (introduced by Kaiming He et al. in 2015 to address issues related ReLU activations in deep neural networks)
\n
Loss Function:\nWhen DICE Loss < 0.3, Loss = Binary Cross Entropy\nElse, Loss = DICE Loss
\n
Data Augmentation\nRandom Horizontal Flip (50% chance)\nRandom Rotation 90Â°
\n
Activation Functions:\nReLU is used for all layers\nSigmoid is used at the output to ensure that the ouputs are between [0, 1]
\n
Weight decay: 1e-5
\n
\n

Features

Automatic Mixed Precision (AMP)

This implementation of UNet uses AMP to implement mixed precision training. It allows us to use FP16 training with FP32 master weights by modifying just a few lines of code.

Horovod

Horovod is a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use. For more information about how to get started with Horovod, see the Horovod: Official repository.

Multi-GPU Training with Horovod

Our model uses Horovod to implement efficient multi-GPU training with NCCL

Requirements

This repository contains Dockerfile which extends the TensorFlow NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:

\n
\n
NVIDIA Docker
\n
TensorFlow NGC container
\n
GPU-based architecture:
\n
\n
\n
\n
NVIDIA Volta
\n
NVIDIA Turing
\n
NVIDIA Ampere architecture
\n
\n
\n

Follow the rest of the process from building the container to train and test the model in quick start guide section.

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These steps enable you to build the UNet TensorFlow NGC container, train and evaluate your model, and generate predictions on the test data.\n### Build the Container:\nFind the resources related to this model in ngc/resources \"https://ngc.nvidia.com/catalog/resources/nvidia:unet_industrial_for_tensorflow \". In this address, you can download resources manually from top right part of the page or use wget resource command to download it.\n1. Make a folder using mkdir command\n2. Go to that folder using cd command\n3. Use the wget command to download resources as zip file inside the folder.\n4. Unzip the zip file using unzip command\n5. Build the container using the Dockerfile inside this directory.\n\n```\nmkdir unet\n\ncd unet\n\nwget --content-disposition https://api.ngc.nvidia.com/v2/resources/nvidia/unet_industrial_for_tensorflow/versions/20.06.3/zip -O unet_industrial_for_tensorflow_20.06.3.zip\nunzip unet_industrial_for_tensorflow_20.06.3.zip\n\ndocker build . --rm -t unet_industrial:latest\n\n```\n**Start an interactive session in the NGC container to run preprocessing/training/inference.**\nThese are the steps that we need to do to run the container in the interactive mode:\n\n\n1.Make a directory for the dataset, for example ./dataset:\n\n\n> mkdir \n\n\n2. Make a directory for results, for example ./results :\n\n> mkdir \n\n3.Start the container with nvidia-docker\n\ndocker run -it --rm \\\n -v :/data/ \\\n -v :/results \\\n -p 8888:8888 unet_industrial:latest\n\n```\nmkdir ./dataset\nmkdir ./results\ndocker run -it --rm \\\n --shm-size=2g --ulimit memlock=-1 --ulimit stack=67108864 \\\n -v :/data/ \\\n -v :/results \\\n -p 8888:8888 unet_industrial:latest\n```\n\n### Download and preprocess the dataset: DAGM2007\n\nIn order to download the dataset. You can execute the command in the next cell.\n**Important Information: some files of the dataset require an account to be downloaded, the script will invite you to download them manually and put them in the correct directory.**\nIf you face permission problem while running any of these scripts, use chmod to add the scripts and rebuild the container.\n\n *chmod +x scriptname*\n > For example: chmod +x download_and_preprocess_dagm2007.sh\n\n```\n./download_and_preprocess_dagm2007.sh /data\n```\n### Download the Rest of the Dataset\nGo to the address that you can see in the structure that is provided by script. You make a request for having access to private data on that link and a link will be send to the email that you provided so you can download 10 groups of dataset in your local machine.\nOpen another terminal in your local machine and use following command in the terminal to cpoy downloaded files to your running container.\ndocker cp /home/// :/data/raw_images/private\n\nFor example, I used following command to copy the class1 folder:\ndocker cp /home/skouchak/Class1.zip 1877b7cc7625:/data/raw_images/private\n\nAfter that, unzip the folder using:\nunzip /folder/path/.zip\n\n## Start Training\n\nTo run training for a default configuration (as described in the default configuration, for example 1/4/8 GPUs, FP32/TF-AMP), launch one of the scripts in the ./scripts directory called ./scripts/UNet{_AMP}_{1, 4, 8}GPU.sh\n\n Each script requires three parameters:\n\n> - Path to the results directory of the model as the first argument\n> - Path to the dataset as the second argument\n> - Class ID from DAGM used (between 1-10)\n\nFor example, for class 1:\n```\ncd scripts/\n./UNet_1GPU.sh /results /data 1\n```\n## Run Evaluation\nModel evaluation on a checkpoint can be launched by running one of the scripts in the ./scripts directory called ./scripts/UNet{_AMP}_EVAL.sh.\n\nEach script requires three parameters:\n\n> - Path to the results directory of the model as the first argument\n> - Path to the dataset as a second argument\n> - Class ID from DAGM used (between 1-10)\n\n```\ncd scripts/\n./UNet_EVAL.sh /results /data 1\n```\n\n## Advanced\nThe following sections provide greater details of the dataset, running training and inference, and the training results.\nTo see the full list of available options and their descriptions, use the -h or --help command line option, for example:\npython main.py --help\n\n### General Arguments\n**--exec_mode=train_and_evaluate** Which execution mode to run the model into.\n\n**--iter_unit=batch** Will the model be run for X batches or X epochs ?\n\n**--num_iter=2500** Number of iterations to run.\n\n**--batch_size=16** Size of each minibatch per GPU.\n\n**--results_dir=/path/to/results** Directory in which to write training logs, summaries and checkpoints.\n\n**--data_dir=/path/to/dataset** Directory which contains the DAGM2007 dataset.\n\n**--dataset_name=DAGM2007** Name of the dataset used in this run (only DAGM2007 is supported atm).\n\n**--dataset_classID=1** ClassID to consider to train or evaluate the network (used for DAGM).\n\n## Dataset Guideline\nThe UNet model was trained with the Weakly Supervised Learning for Industrial Optical Inspection (DAGM 2007) dataset. The provided data is artificially generated, but similar to real world problems. It consists of multiple data sets, each consisting of 1000 images showing the background texture without defects, and of 150 images with one labeled defect each on the background texture. The images in a single data set are very similar, but each data set is generated by a different texture model and defect model. Not all deviations from the texture are necessarily defects. The algorithm will need to use the weak labels provided during the training phase to learn the properties that characterize a defect.\n\n## Training Process\n**Laplace Smoothing**: We use this technique in the DICE loss to improve the training efficiency. This technique consists in replacing the epsilon parameter (used to avoid dividing by zero and very small: +/- 1e-7) by 1.\n\n**Adaptive Loss**: The DICE Loss is not able to provide a meaningful gradient at initialisation. This leads to a model instability which often push the model to diverge. Nonetheless, once the model starts to converge, DICE loss is able to very efficiently fully train the model. Therefore, we implemented an adaptive loss which is composed of two sub-losses:\n\n> - Binary Cross-Entropy (BCE)\n> - DICE Loss\n\nThe model is trained with the BCE loss until the DICE Loss reach a experimentally defined threshold (0.3). Thereafter, DICE loss is used to finish training.\n\n**Weak Labelling**: This dataset is referred as weakly labelled. That means that the segmentation labels are not given at the pixel level but rather in an approximate fashion.\n\n## Performance\n### Benchmarking\n\nThis sections shows how to run benchmarks measuring the model performance in training and inference modes.\n\nTo benchmark the training performance, you can run one of the scripts in the ./scripts/benchmarking/ directory called ./scripts/benchmarking/UNet_trainbench{_AMP}_{1, 4, 8}GPU.sh.\n\nEach of the scripts requires two parameters:\n\n> - path to the dataset as the first argument\n> - class ID from DAGM used (between 1-10)\n\nFor example:\n```\ncd scripts/benchmarking/\n./UNet_trainbench_1GPU.sh /data 1\n```\n## Inference Performance Benchmark\n\nTo benchmark the inference performance, you can run one of the scripts in the ./scripts/benchmarking/ directory called ./scripts/benchmarking/UNet_evalbench{_AMP}.sh.\n\nEach of the scripts requires:\n\n> - Path to the dataset as the first argument\n> - Class ID from DAGM used (between 1-10)\n\n```\ncd scripts/benchmarking/\n./UNet_evalbench_AMP.sh /data 1\n```\n\nWe can have access to the jupyterlb inside the container using following command inside the container bash:\n\n\n```\njupyter notebook --ip 0.0.0.0 --port 8888 --allow-root\n```\n\nRunning the previous command results in a link to the jupyterlab inside the container. Go to the jupyterlab and uplaod \"unet-industrial-demo\" notebook in the jupyterlab. By following unet-industrial-demo notebook, you can download 10 pre-trained and fine-tuned models similar to the model that you just trained. You can test these models using images in the public folders. Public datasets haven't used for training models so they can be used for testing.\n\nYou can see the process of usig a trained model and new data to test the accuracy of that trained model. If you want to use the model that you trained instead of pretrained models, you can change the checkpoints folder path to the checkpoints of model that you just trained and test your trained model using images in the public folder.","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_74":198,"_76":199,"_164":-5,"_170":30,"_172":30,"_184":197,"_174":30},{"_74":196,"_76":196,"_164":-5,"_170":30,"_172":30,"_184":197,"_174":30},{"_74":194,"_76":194,"_164":-5,"_170":30,"_172":30,"_184":195,"_174":30},{"_74":192,"_76":192,"_164":-5,"_170":30,"_172":30,"_184":193,"_174":30},{"_74":21,"_76":21,"_164":-5,"_170":30,"_172":30,"_184":191,"_174":30},[],"third-upload",[],"second-upload",[],"first-upload",[],"-","Latest (first-upload)","nvidia/unet","key","nvidia/resources/unet","resourceType","resources","resourceId","unet","shortDescription","NVIDIA pre-trained U-Net model is adapted from the original version of the U-Net model which is a convolutional auto-encoder for 2D image segmentation.","# Getting Start with U-Net Industrial\n\n## Image Segmentation\nImage segmentation is the process of partitioning a digital image into multiple segments. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image.\n## Model Overview\nThis U-Net model is adapted from the original version of the U-Net model which is a convolutional auto-encoder for 2D image segmentation.\n\nThis work proposes a modified version of U-Net, called TinyUNet which performs efficiently and with very high accuracy on the industrial anomaly dataset DAGM2007. TinyUNet, like the original U-Net is composed of two parts:\n\n*An encoding sub-network*\n*A decoding sub-network*:\nWhat is encoder decoder in deep learning models? The Encoder converts the input sequence into a single dimensional vector. The decoder uses the output of the encoder as the input and it converts this vector into the output sequence.\n\nThis model repeatedly applies 3 downsampling blocks composed of two 2D convolutions followed by a 2D max pooling layer in the encoding sub-network. In the decoding sub-network, 3 upsampling blocks are composed of a upsample2D layer followed by a 2D convolution, a concatenation operation with the residual connection and two 2D convolutions.\n\n## Default Configuration\nThis model trains in 2500 epochs, under the following setup:\n\n> - Global Batch Size: 16\n> - Optimizer RMSProp:\n decay: 0.9\n momentum: 0.8\n centered: True\n> - Learning Rate Schedule: Exponential Step Decay\n decay: 0.8\n steps: 500\n initial learning rate: 1e-4\n> - Weight Initialization: He Uniform Distribution (introduced by Kaiming He et al. in 2015 to address issues related ReLU activations in deep neural networks)\n> - Loss Function:\n When DICE Loss < 0.3, Loss = Binary Cross Entropy\n Else, Loss = DICE Loss\n> - Data Augmentation\n Random Horizontal Flip (50% chance)\n Random Rotation 90Â°\n> - Activation Functions:\n ReLU is used for all layers\n Sigmoid is used at the output to ensure that the ouputs are between [0, 1]\n> - Weight decay: 1e-5\n\n## Features\n### Automatic Mixed Precision (AMP)\n\nThis implementation of UNet uses AMP to implement mixed precision training. It allows us to use FP16 training with FP32 master weights by modifying just a few lines of code.\n\n### Horovod\n\nHorovod is a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use. For more information about how to get started with Horovod, see the Horovod: Official repository.\n\n### Multi-GPU Training with Horovod\n\nOur model uses Horovod to implement efficient multi-GPU training with NCCL\n\n\n## Requirements\nThis repository contains Dockerfile which extends the TensorFlow NGC container and encapsulates some dependencies. Aside from these dependencies, ensure you have the following components:\n\n> - NVIDIA Docker\n> - TensorFlow NGC container\n> - GPU-based architecture:\n> > - NVIDIA Volta\n> > - NVIDIA Turing\n> > - NVIDIA Ampere architecture\n\nFollow the rest of the process from building the container to train and test the model in quick start guide section.","displayName","Getting Start with U-Net Industrial",["D",1680631768210],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/-/resources/unet","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Tutorials.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[242,243,244],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_210":254,"_246":255,"_247":256,"_249":250},{"_210":252,"_246":252,"_247":253,"_249":250},{"_210":245,"_246":245,"_247":248,"_249":250},"Tutorial","value","filter",{"_251":71,"_246":245},"category","label","field","Segmentation",{"_251":250,"_246":252},"DL","soln_dl",{"_251":257,"_246":255},"technology",{"_105":1237,"_107":1238},"ngcApps",[1190],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1171":1172,"_1173":1174,"_1175":1176,"_1177":1178,"_1179":269,"_136":295,"_1180":1181,"_1182":55,"_1183":84,"_1184":84,"_1185":1186,"_1187":84,"_1188":1189},"amplitude",{"_1168":1169,"_809":-7,"_1170":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1162":1163,"_1164":1165,"_1166":1167},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787731009938,"1.12.0","defaultSearchSuggestions",{"_1049":376,"_1050":1051,"_1052":1053,"_1054":1055},"announcements",[],"consentState",{"_333":1039,"_1040":84,"_1041":30,"_1042":30,"_1043":1044},"consentConfig",{"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334},"showContactAdmin",1787731009939,[],[],[309,310,311,312,313,314],{"_44":58,"_46":68,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":66,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":64,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":58,"_46":59,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":100,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":100,"_50":-5,"_52":-5,"_54":55},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1031":1032,"_1033":1034,"_1035":1036,"_1037":1038},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[335,336,337,338,339],{"_340":721,"_76":722,"_162":723,"_344":84,"_345":16,"_347":724,"_349":725},{"_340":623,"_76":624,"_162":625,"_344":30,"_345":626,"_347":627,"_349":628},{"_340":531,"_76":532,"_162":533,"_344":30,"_345":53,"_347":534,"_349":535},{"_340":351,"_76":352,"_162":353,"_344":30,"_345":354,"_347":355,"_349":356},{"_340":341,"_76":342,"_162":343,"_344":30,"_345":346,"_347":348,"_349":350},"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,[473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494],[357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373],{"_374":468,"_210":468,"_162":376,"_347":469},{"_374":464,"_210":464,"_162":376,"_347":465},{"_374":459,"_210":459,"_162":376,"_347":460},{"_374":455,"_210":455,"_162":376,"_347":456},{"_374":450,"_210":450,"_162":376,"_347":451},{"_374":445,"_210":445,"_162":376,"_347":446},{"_374":437,"_210":437,"_162":376,"_347":438},{"_374":429,"_210":429,"_162":376,"_347":430},{"_374":424,"_210":424,"_162":376,"_347":425},{"_374":419,"_210":419,"_162":376,"_347":420},{"_374":414,"_210":414,"_162":376,"_347":415},{"_374":409,"_210":409,"_162":376,"_347":410},{"_374":404,"_210":404,"_162":376,"_347":405},{"_374":397,"_210":397,"_162":376,"_347":398},{"_374":392,"_210":392,"_162":376,"_347":393},{"_374":385,"_210":385,"_162":376,"_347":386},{"_374":375,"_210":375,"_162":376,"_347":377},"hostName","amazon-adsystem.com","",[378,379],{"_76":384,"_381":375,"_382":30,"_162":383},{"_76":380,"_381":375,"_382":30,"_162":383},"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",[387,388],{"_76":391,"_381":385,"_382":30,"_162":390},{"_76":389,"_381":385,"_382":30,"_162":390},"IDE","This domain is owned by Doubleclick (Google). 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","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","_zitok","_twpid","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. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589],[536,537,538,539,540],{"_374":450,"_210":450,"_162":376,"_347":560},{"_374":424,"_210":424,"_162":376,"_347":555},{"_374":550,"_210":550,"_162":376,"_347":551},{"_374":545,"_210":545,"_162":376,"_347":546},{"_374":541,"_210":541,"_162":376,"_347":542},"nvidia.partners",[543],{"_76":544,"_381":541,"_382":30,"_162":418},"edge","app.hushly.com",[547],{"_76":548,"_381":545,"_382":30,"_162":549},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[552],{"_76":553,"_381":550,"_382":30,"_162":554},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",3,[649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677],[629,630,631,632],{"_374":644,"_210":644,"_162":376,"_347":645},{"_374":639,"_210":639,"_162":376,"_347":640},{"_374":437,"_210":437,"_162":376,"_347":636},{"_374":550,"_210":550,"_162":376,"_347":633},[634],{"_76":635,"_381":550,"_382":84,"_162":554},"_gh_sess",[637],{"_76":638,"_381":437,"_382":84,"_162":444},"YSC","bizible.com",[641],{"_76":642,"_381":639,"_382":30,"_162":643},"_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",[646],{"_76":647,"_381":644,"_382":30,"_162":648},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_76":719,"_381":496,"_382":30,"_162":720},{"_76":704,"_381":718,"_382":84,"_162":705},{"_76":687,"_381":499,"_382":30,"_162":611},{"_76":687,"_381":599,"_382":30,"_162":611},{"_76":716,"_381":496,"_382":30,"_162":717},{"_76":704,"_381":502,"_382":84,"_162":705},{"_76":714,"_381":496,"_382":30,"_162":715},{"_76":704,"_381":499,"_382":84,"_162":705},{"_76":712,"_381":598,"_382":30,"_162":713},{"_76":704,"_381":620,"_382":84,"_162":705},{"_76":684,"_381":499,"_382":30,"_162":686},{"_76":710,"_381":496,"_382":84,"_162":711},{"_76":708,"_381":496,"_382":30,"_162":709},{"_76":706,"_381":496,"_382":84,"_162":707},{"_76":704,"_381":594,"_382":84,"_162":705},{"_76":702,"_381":496,"_382":30,"_162":703},{"_76":700,"_381":496,"_382":30,"_162":701},{"_76":698,"_381":496,"_382":84,"_162":699},{"_76":696,"_381":603,"_382":30,"_162":697},{"_76":684,"_381":594,"_382":30,"_162":686},{"_76":694,"_381":496,"_382":30,"_162":695},{"_76":692,"_381":496,"_382":84,"_162":693},{"_76":690,"_381":496,"_382":30,"_162":691},{"_76":688,"_381":496,"_382":30,"_162":689},{"_76":687,"_381":496,"_382":30,"_162":611},{"_76":684,"_381":685,"_382":30,"_162":686},{"_76":682,"_381":496,"_382":30,"_162":683},{"_76":680,"_381":496,"_382":84,"_162":681},{"_76":678,"_381":496,"_382":30,"_162":679},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","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","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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It allows us to engage with a user that has previously visited our website.","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.","_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.","_session_id","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.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. 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