Model Overview

A pre-trained model for the endoscopic tool segmentation task, trained using a flexible unet structure with an efficientnet-b2 [1] as the backbone and a UNet architecture [2] as the decoder. Datasets use private samples from Activ Surgical.

The PyTorch model and torchscript model are shared in google drive. Details can be found in large_files.yml file. Modify the \"bundle_root\" parameter specified in configs/train.json and configs/inference.json to reflect where models are downloaded. Expected directory path to place downloaded models is \"models/\" under \"bundle_root\".

$\"image\"$

Pre-trained weights

A pre-trained encoder weights would benefit the model training. In this bundle, the encoder is trained with pre-trained weights from some internal data. We provide two options to enable users to load pre-trained weights:

Via setting the use_imagenet_pretrain parameter in the config file to True, ImageNet pre-trained weights from the EfficientNet-PyTorch repo can be loaded. Please note that these weights are for non-commercial use. Each user is responsible for checking the content of the models/datasets and the applicable licenses and determining if suitable for the intended use.
Via adding a CheckpointLoader as the first handler to the handlers section of the train.json config file, weights from a local path can be loaded. Here is an example CheckpointLoader:

{\n    \"_target_\": \"CheckpointLoader\",\n    \"load_path\": \"/path/to/local/weight/model.pt\",\n    \"load_dict\": {\n        \"model\": \"@network\"\n    },\n    \"strict\": false,\n    \"map_location\": \"@device\"\n}\n

When executing the training command, if neither adding the CheckpointLoader to the train.json nor setting the use_imagenet_pretrain parameter to True, a training process would start from scratch.

Data

Datasets used in this work were provided by Activ Surgical.

Since datasets are private, existing public datasets like EndoVis 2017 can be used to train a similar model.

Preprocessing

When using EndoVis or any other dataset, it should be divided into \"train\", \"valid\" and \"test\" folders. Samples in each folder would better be images and converted to jpg format. Otherwise, \"images\", \"labels\", \"val_images\" and \"val_labels\" parameters in configs/train.json and \"datalist\" in configs/inference.json should be modified to fit given dataset. After that, \"dataset_dir\" parameter in configs/train.json and configs/inference.json should be changed to root folder which contains \"train\", \"valid\" and \"test\" folders.

Please notice that loading data operation in this bundle is adaptive. If images and labels are not in the same format, it may lead to a mismatching problem. For example, if images are in jpg format and labels are in npy format, PIL and Numpy readers will be used separately to load images and labels. Since these two readers have their own way to parse file's shape, loaded labels will be transpose of the correct ones and incur a missmatching problem.

Training configuration

The training as performed with the following:

GPU: At least 12GB of GPU memory
Actual Model Input: 736 x 480 x 3
Optimizer: Adam
Learning Rate: 1e-4
Dataset Manager: CacheDataset

Memory Consumption Warning

If you face memory issues with CacheDataset, you can either switch to a regular Dataset class or lower the caching rate cache_rate in the configurations within range [0, 1] to minimize the System RAM requirements.

Input

A three channel video frame

Output

Two channels:

Label 1: tools
Label 0: everything else

Performance

IoU was used for evaluating the performance of the model. This model achieves a mean IoU score of 0.86.

Training Loss

$\"A$

Validation IoU

$\"A$

TensorRT speedup

The endoscopic_tool_segmentation bundle supports acceleration with TensorRT. The table below displays the speedup ratios observed on an A100 80G GPU.

\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\">

method	torch_fp32(ms)	torch_amp(ms)	trt_fp32(ms)	trt_fp16(ms)	speedup amp	speedup fp32	speedup fp16	amp vs fp16
model computation	12.00	14.06	6.59	5.20	0.85	1.82	2.31	2.70
end2end	170.04	172.20	155.26	155.57	0.99	1.10	1.09	1.11

Where:

model computation means the speedup ratio of model's inference with a random input without preprocessing and postprocessing
end2end means run the bundle end-to-end with the TensorRT based model.
torch_fp32 and torch_amp are for the PyTorch models with or without amp mode.
trt_fp32 and trt_fp16 are for the TensorRT based models converted in corresponding precision.
speedup amp, speedup fp32 and speedup fp16 are the speedup ratios of corresponding models versus the PyTorch float32 model
amp vs fp16 is the speedup ratio between the PyTorch amp model and the TensorRT float16 based model.

This result is benchmarked under:

TensorRT: 8.5.3+cuda11.8
Torch-TensorRT Version: 1.4.0
CPU Architecture: x86-64
OS: ubuntu 20.04
Python version:3.8.10
CUDA version: 12.0
GPU models and configuration: A100 80G

MONAI Bundle Commands

In addition to the Pythonic APIs, a few command line interfaces (CLI) are provided to interact with the bundle. The CLI supports flexible use cases, such as overriding configs at runtime and predefining arguments in a file.

For more details usage instructions, visit the MONAI Bundle Configuration Page.

Execute training:

python -m monai.bundle run --config_file configs/train.json\n

Please note that if the default dataset path is not modified with the actual path in the bundle config files, you can also override it by using --dataset_dir:

\n\">

python -m monai.bundle run --config_file configs/train.json --dataset_dir <actual dataset path>\n

Override the `train` config to execute multi-GPU training:

torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file \"['configs/train.json','configs/multi_gpu_train.json']\"\n

Please note that the distributed training-related options depend on the actual running environment; thus, users may need to remove --standalone, modify --nnodes, or do some other necessary changes according to the machine used. For more details, please refer to pytorch's official tutorial.

Override the `train` config to execute evaluation with the trained model:

python -m monai.bundle run --config_file \"['configs/train.json','configs/evaluate.json']\"\n

Override the `train` config and `evaluate` config to execute multi-GPU evaluation:

torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file \"['configs/train.json','configs/evaluate.json','configs/multi_gpu_evaluate.json']\"\n

Execute inference:

python -m monai.bundle run --config_file configs/inference.json\n

Export checkpoint to TorchScript file:

python -m monai.bundle ckpt_export network_def --filepath models/model.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.json\n

Export checkpoint to TensorRT based models with fp32 or fp16 precision:

\n\">

python -m monai.bundle trt_export --net_id network_def --filepath models/model_trt.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.json --precision <fp32/fp16>\n

Execute inference with the TensorRT model:

python -m monai.bundle run --config_file \"['configs/inference.json', 'configs/inference_trt.json']\"\n

References

[1] Tan, M. and Le, Q. V. Efficientnet: Rethinking model scaling for convolutional neural networks. ICML, 2019a. https://arxiv.org/pdf/1905.11946.pdf

[2] O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015. https://arxiv.org/pdf/1505.04597.pdf

License

Licensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0\n

Unless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.

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Datasets use private samples from [Activ Surgical](https://www.activsurgical.com/).\n\nThe [PyTorch model](https://drive.google.com/file/d/1I7UtWDKDEcezMqYiA-i_hsRTCrvWwJ61/view?usp=sharing) and [torchscript model](https://drive.google.com/file/d/1e_wYd1HjJQ0dz_HKdbthRcMOyUL02aLG/view?usp=sharing) are shared in google drive. Details can be found in `large_files.yml` file. Modify the \"bundle_root\" parameter specified in configs/train.json and configs/inference.json to reflect where models are downloaded. Expected directory path to place downloaded models is \"models/\" under \"bundle_root\".\n\n![image](https://developer.download.nvidia.com/assets/Clara/Images/monai_endoscopic_tool_segmentation_workflow.png)\n\n### Pre-trained weights\n\nA pre-trained encoder weights would benefit the model training. In this bundle, the encoder is trained with pre-trained weights from some internal data. We provide two options to enable users to load pre-trained weights:\n\n1. Via setting the `use_imagenet_pretrain` parameter in the config file to `True`, [ImageNet](https://ieeexplore.ieee.org/document/5206848) pre-trained weights from the [EfficientNet-PyTorch repo](https://github.com/lukemelas/EfficientNet-PyTorch) can be loaded. Please note that these weights are for non-commercial use. Each user is responsible for checking the content of the models/datasets and the applicable licenses and determining if suitable for the intended use.\n2. Via adding a `CheckpointLoader` as the first handler to the `handlers` section of the `train.json` config file, weights from a local path can be loaded. Here is an example `CheckpointLoader`:\n\n```json\n{\n \"_target_\": \"CheckpointLoader\",\n \"load_path\": \"/path/to/local/weight/model.pt\",\n \"load_dict\": {\n \"model\": \"@network\"\n },\n \"strict\": false,\n \"map_location\": \"@device\"\n}\n```\n\nWhen executing the training command, if neither adding the `CheckpointLoader` to the `train.json` nor setting the `use_imagenet_pretrain` parameter to `True`, a training process would start from scratch.\n\n### Data\n\nDatasets used in this work were provided by [Activ Surgical](https://www.activsurgical.com/).\n\nSince datasets are private, existing public datasets like [EndoVis 2017](https://endovissub2017-roboticinstrumentsegmentation.grand-challenge.org/Data/) can be used to train a similar model.\n\n#### Preprocessing\n\nWhen using EndoVis or any other dataset, it should be divided into \"train\", \"valid\" and \"test\" folders. Samples in each folder would better be images and converted to jpg format. Otherwise, \"images\", \"labels\", \"val_images\" and \"val_labels\" parameters in `configs/train.json` and \"datalist\" in `configs/inference.json` should be modified to fit given dataset. After that, \"dataset_dir\" parameter in `configs/train.json` and `configs/inference.json` should be changed to root folder which contains \"train\", \"valid\" and \"test\" folders.\n\nPlease notice that loading data operation in this bundle is adaptive. If images and labels are not in the same format, it may lead to a mismatching problem. For example, if images are in jpg format and labels are in npy format, PIL and Numpy readers will be used separately to load images and labels. Since these two readers have their own way to parse file's shape, loaded labels will be transpose of the correct ones and incur a missmatching problem.\n\n### Training configuration\n\nThe training as performed with the following:\n- GPU: At least 12GB of GPU memory\n- Actual Model Input: 736 x 480 x 3\n- Optimizer: Adam\n- Learning Rate: 1e-4\n- Dataset Manager: CacheDataset\n\n#### Memory Consumption Warning\n\nIf you face memory issues with CacheDataset, you can either switch to a regular Dataset class or lower the caching rate `cache_rate` in the configurations within range [0, 1] to minimize the System RAM requirements.\n\n#### Input\n\nA three channel video frame\n\n#### Output\n\nTwo channels:\n- Label 1: tools\n- Label 0: everything else\n\n### Performance\n\nIoU was used for evaluating the performance of the model. This model achieves a mean IoU score of 0.86.\n\n##### Training Loss\n\n![A graph showing the training loss over 100 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_endoscopic_tool_segmentation_train_loss_v3.png)\n\n##### Validation IoU\n\n![A graph showing the validation mean IoU over 100 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_endoscopic_tool_segmentation_val_iou_v3.png)\n\n##### TensorRT speedup\n\nThe `endoscopic_tool_segmentation` bundle supports acceleration with TensorRT. The table below displays the speedup ratios observed on an A100 80G GPU.\n\n| method | torch_fp32(ms) | torch_amp(ms) | trt_fp32(ms) | trt_fp16(ms) | speedup amp | speedup fp32 | speedup fp16 | amp vs fp16|\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| model computation | 12.00 | 14.06 | 6.59 | 5.20 | 0.85 | 1.82 | 2.31 | 2.70 |\n| end2end |170.04 | 172.20 | 155.26 | 155.57 | 0.99 | 1.10 | 1.09 | 1.11 |\n\nWhere:\n- `model computation` means the speedup ratio of model's inference with a random input without preprocessing and postprocessing\n- `end2end` means run the bundle end-to-end with the TensorRT based model.\n- `torch_fp32` and `torch_amp` are for the PyTorch models with or without `amp` mode.\n- `trt_fp32` and `trt_fp16` are for the TensorRT based models converted in corresponding precision.\n- `speedup amp`, `speedup fp32` and `speedup fp16` are the speedup ratios of corresponding models versus the PyTorch float32 model\n- `amp vs fp16` is the speedup ratio between the PyTorch amp model and the TensorRT float16 based model.\n\nThis result is benchmarked under:\n- TensorRT: 8.5.3+cuda11.8\n- Torch-TensorRT Version: 1.4.0\n- CPU Architecture: x86-64\n- OS: ubuntu 20.04\n- Python version:3.8.10\n- CUDA version: 12.0\n- GPU models and configuration: A100 80G\n\n### MONAI Bundle Commands\n\nIn addition to the Pythonic APIs, a few command line interfaces (CLI) are provided to interact with the bundle. The CLI supports flexible use cases, such as overriding configs at runtime and predefining arguments in a file.\n\nFor more details usage instructions, visit the [MONAI Bundle Configuration Page](https://docs.monai.io/en/latest/config_syntax.html).\n\n##### Execute training:\n\n```\npython -m monai.bundle run --config_file configs/train.json\n```\n\nPlease note that if the default dataset path is not modified with the actual path in the bundle config files, you can also override it by using `--dataset_dir`:\n\n```\npython -m monai.bundle run --config_file configs/train.json --dataset_dir \n```\n\n##### Override the `train` config to execute multi-GPU training:\n\n```\ntorchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file \"['configs/train.json','configs/multi_gpu_train.json']\"\n```\n\nPlease note that the distributed training-related options depend on the actual running environment; thus, users may need to remove `--standalone`, modify `--nnodes`, or do some other necessary changes according to the machine used. For more details, please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html).\n\n##### Override the `train` config to execute evaluation with the trained model:\n\n```\npython -m monai.bundle run --config_file \"['configs/train.json','configs/evaluate.json']\"\n```\n\n##### Override the `train` config and `evaluate` config to execute multi-GPU evaluation:\n\n```\ntorchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file \"['configs/train.json','configs/evaluate.json','configs/multi_gpu_evaluate.json']\"\n```\n\n##### Execute inference:\n\n```\npython -m monai.bundle run --config_file configs/inference.json\n```\n\n##### Export checkpoint to TorchScript file:\n\n```\npython -m monai.bundle ckpt_export network_def --filepath models/model.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.json\n```\n\n##### Export checkpoint to TensorRT based models with fp32 or fp16 precision:\n\n```\npython -m monai.bundle trt_export --net_id network_def --filepath models/model_trt.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.json --precision \n```\n\n##### Execute inference with the TensorRT model:\n\n```\npython -m monai.bundle run --config_file \"['configs/inference.json', 'configs/inference_trt.json']\"\n```\n\n## References\n\n[1] Tan, M. and Le, Q. V. Efficientnet: Rethinking model scaling for convolutional neural networks. ICML, 2019a. https://arxiv.org/pdf/1905.11946.pdf\n\n[2] O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015. https://arxiv.org/pdf/1505.04597.pdf\n\n## License\n\nCopyright (c) MONAI Consortium\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.","displayName",["D",1731434176913],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/monaitoolkit/models/monai_endoscopic_tool_segmentation","rawImageUrl","https://developer.download.nvidia.com/assets/Clara/Images/monai_toolkit_logo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[352,353,354,355,356,357,358,359,360,361],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_321":387,"_363":387,"_364":388,"_366":367},{"_321":385,"_363":385,"_364":386,"_366":367},{"_321":381,"_363":382,"_364":383,"_366":367},{"_321":379,"_363":379,"_364":380,"_366":367},{"_321":377,"_363":377,"_364":378,"_366":367},{"_321":375,"_363":375,"_364":376,"_366":367},{"_321":373,"_363":373,"_364":374,"_366":367},{"_321":371,"_363":371,"_364":372,"_366":367},{"_321":369,"_363":369,"_364":370,"_366":367},{"_321":362,"_363":362,"_364":365,"_366":367},"Segmentation","value","filter",{"_368":367,"_363":362},"category","label","field","Pytorch",{"_368":367,"_363":369},"NSPECT-NAQW-LTW8",{"_368":367,"_363":371},"MONAI",{"_368":367,"_363":373},"Medical Imaging",{"_368":367,"_363":375},"Medical",{"_368":367,"_363":377},"Imaging",{"_368":367,"_363":379},"Healthcare","indus_healthcare",{"_368":384,"_363":382},"industry","Deep Learning",{"_368":367,"_363":385},"Bundle",{"_368":367,"_363":387},{"_212":1563,"_214":1564},"ngcApps",[1516],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1497":1498,"_1499":1500,"_1501":1502,"_1503":1504,"_1505":400,"_243":424,"_1506":1507,"_1508":56,"_1509":191,"_1510":191,"_1511":1512,"_1513":191,"_1514":1515},"amplitude",{"_1494":1495,"_1096":-7,"_1496":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1488":1489,"_1490":1491,"_1492":1493},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1371":273,"_1372":1373,"_1374":1375,"_1376":1377},"announcements",[],"consentState",{"_462":1361,"_1362":191,"_1363":31,"_1364":31,"_1365":1366},"consentConfig",{"_444":445,"_446":447,"_448":449,"_450":451,"_452":453,"_454":455,"_456":457,"_458":459,"_460":461,"_462":463},"showContactAdmin",1784116389837,[],[],[438,439,440,441,442,443],{"_45":60,"_47":70,"_49":207,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":207,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":67,"_49":207,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":207,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":207,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":207,"_51":-5,"_53":-5,"_55":56},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1353":1354,"_1355":1356,"_1357":1358,"_1359":1360},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[464,465,466,467,468],{"_469":1005,"_82":1006,"_272":1007,"_473":191,"_474":18,"_476":1008,"_478":1009},{"_469":795,"_82":796,"_272":797,"_473":31,"_474":59,"_476":798,"_478":799},{"_469":695,"_82":696,"_272":697,"_473":31,"_474":54,"_476":698,"_478":699},{"_469":480,"_82":481,"_272":482,"_473":31,"_474":483,"_476":484,"_478":485},{"_469":470,"_82":471,"_272":472,"_473":31,"_474":475,"_476":477,"_478":479},"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,[613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647],[486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502],{"_503":609,"_321":609,"_272":273,"_476":610},{"_503":604,"_321":604,"_272":273,"_476":605},{"_503":600,"_321":600,"_272":273,"_476":601},{"_503":595,"_321":595,"_272":273,"_476":596},{"_503":590,"_321":590,"_272":273,"_476":591},{"_503":578,"_321":578,"_272":273,"_476":579},{"_503":566,"_321":566,"_272":273,"_476":567},{"_503":561,"_321":561,"_272":273,"_476":562},{"_503":550,"_321":550,"_272":273,"_476":551},{"_503":545,"_321":545,"_272":273,"_476":546},{"_503":540,"_321":540,"_272":273,"_476":541},{"_503":535,"_321":535,"_272":273,"_476":536},{"_503":530,"_321":530,"_272":273,"_476":531},{"_503":525,"_321":525,"_272":273,"_476":526},{"_503":520,"_321":520,"_272":273,"_476":521},{"_503":513,"_321":513,"_272":273,"_476":514},{"_503":504,"_321":504,"_272":273,"_476":505},"hostName","amazon-adsystem.com",[506,507],{"_82":512,"_509":504,"_510":31,"_272":511},{"_82":508,"_509":504,"_510":31,"_272":511},"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",[515,516],{"_82":519,"_509":513,"_510":31,"_272":518},{"_82":517,"_509":513,"_510":31,"_272":518},"IDE","This domain is owned by Doubleclick (Google). 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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",[552,553,554,555],{"_82":560,"_509":550,"_510":31,"_272":557},{"_82":559,"_509":550,"_510":31,"_272":557},{"_82":558,"_509":550,"_510":31,"_272":557},{"_82":556,"_509":550,"_510":31,"_272":557},"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",[563],{"_82":564,"_509":561,"_510":31,"_272":565},"bcookie","This domain is owned by LinkedIn, the business networking platform. 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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. 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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.",[735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755],[700,701,702,703,704],{"_503":595,"_321":595,"_272":273,"_476":726},{"_503":561,"_321":561,"_272":273,"_476":721},{"_503":714,"_321":714,"_272":273,"_476":715},{"_503":710,"_321":710,"_272":273,"_476":711},{"_503":705,"_321":705,"_272":273,"_476":706},"nvcrm.my.site.com",[707],{"_82":708,"_509":705,"_510":31,"_272":709},"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",[712],{"_82":713,"_509":710,"_510":31,"_272":544},"edge","github.com",[716,717],{"_82":720,"_509":714,"_510":31,"_272":719},{"_82":718,"_509":714,"_510":31,"_272":719},"logged_in","This domain is owned by Github. 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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 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LICENSE	11.09 KB	February 28, 2025 UTC	curl wget Download