Model Overview

A pre-trained model for the endoscopic inbody classification task and trained using the SEResNet50 structure, whose details can be found in [1]. All datasets are from private samples of Activ Surgical. Samples in training and validation dataset are from the same 4 videos, while test samples are from different two videos.

The PyTorch model and torchscript model are shared in google drive. 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.

$\"image\"$

Data

The datasets used in this work were provided by Activ Surgical.

Since datasets are private, we provide a link of 20 samples (10 in-body and 10 out-body) to show what they look like.

Preprocessing

After downloading this dataset, python script in scripts folder named data_process can be used to generate label json files by running the command below and modifying datapath to path of unziped downloaded data. Generated label json files will be stored in label folder under the bundle path.

python scripts/data_process.py --datapath /path/to/data/root\n

By default, label path parameter in train.json and inference.json of this bundle is point to the generated label folder under bundle path. If you move these generated label files to another place, please modify the train_json, val_json and test_json parameters specified in configs/train.json and configs/inference.json to where these label files are.

The input label json should be a list made up by dicts which includes image and label keys. An example format is shown below.

[\n    {\n        \"image\":\"/path/to/image/image_name0.jpg\",\n        \"label\": 0\n    },\n    {\n        \"image\":\"/path/to/image/image_name1.jpg\",\n        \"label\": 0\n    },\n    {\n        \"image\":\"/path/to/image/image_name2.jpg\",\n        \"label\": 1\n    },\n    ....\n    {\n        \"image\":\"/path/to/image/image_namek.jpg\",\n        \"label\": 0\n    },\n]\n

Training configuration

The training as performed with the following:

GPU: At least 12GB of GPU memory
Actual Model Input: 256 x 256 x 3
Optimizer: Adam
Learning Rate: 1e-3

Input

A three channel video frame

Output

Two Channels

Label 0: in body
Label 1: out body

Performance

Accuracy was used for evaluating the performance of the model. This model achieves an accuracy score of 0.99

Training Loss

$\"A$

Validation Accuracy

$\"A$

TensorRT speedup

The endoscopic_inbody_classification bundle supports acceleration with TensorRT through the ONNX-TensorRT method. 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	6.50	9.23	2.78	2.31	0.70	2.34	2.81	4.00
end2end	23.54	23.78	7.37	7.14	0.99	3.19	3.30	3.33

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.

Currently, the only available method to accelerate this model is through ONNX-TensorRT. However, the Torch-TensorRT method is under development and will be available in the near future.

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 \\\n    --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.

In addition, if using the 20 samples example dataset, the preprocessing script will divide the samples to 16 training samples, 2 validation samples and 2 test samples. However, pytorch multi-gpu training requires number of samples in dataloader larger than gpu numbers. Therefore, please use no more than 2 gpus to run this bundle if using the 20 samples example dataset.

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

Execute inference:

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

The classification result of every images in test.json will be printed to the screen.

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:

--use_onnx "True" --use_trace "True"\n\">

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

Execute inference with the TensorRT model:

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

References

[1] J. Hu, L. Shen and G. Sun, Squeeze-and-Excitation Networks, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132-7141. https://arxiv.org/pdf/1709.01507.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.

","resourceVersions",[297,298,299,300,301],"resourceVersionsTotal",4,"hasMoreVersions","versionMetadata",{"_270":22,"_271":272,"_49":273,"_274":-5,"_275":276,"_277":278,"_279":280,"_281":16,"_282":31,"_283":-5,"_284":31,"_285":-5,"_286":31,"_287":-5,"_288":31,"_289":-5,"_290":31,"_291":-5,"_292":31,"_293":-5,"_294":31,"_295":296},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/monaitoolkit/models/monai_endoscopic_inbody_classification/-","version","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/monaitoolkit/monai_endoscopic_inbody_classification:0.4.9","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/monaitoolkit/models/monai_endoscopic_inbody_classification/0.4.9/version-history","accessCheckResult",{"_269":189},"hasRelatedCollections","context",{"_265":266,"_267":268},1784159474924,[],[],[258,259,260,261,262,263,264],{"_45":59,"_47":69,"_49":61,"_51":62,"_53":70,"_55":56},{"_45":59,"_47":68,"_49":61,"_51":62,"_53":65,"_55":56},{"_45":59,"_47":66,"_49":61,"_51":62,"_53":67,"_55":56},{"_45":59,"_47":64,"_49":205,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":61,"_51":62,"_53":63,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":54,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56},"orgName","nvidia","teamName","monaitoolkit","allowed","versionId","description","","UPLOAD_COMPLETE","releaseType","createdDate",["D",1729221980021],"updatedDate",["D",1740779340768],"totalSizeInBytes",209157064,"totalFileCount","isSigned","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_79":309,"_81":310,"_274":-5,"_282":31,"_284":31,"_295":308,"_286":31},{"_79":22,"_81":22,"_274":-5,"_282":31,"_284":31,"_295":308,"_286":31},{"_79":306,"_81":306,"_274":-5,"_282":31,"_284":31,"_295":307,"_286":31},{"_79":304,"_81":304,"_274":-5,"_282":31,"_284":31,"_295":305,"_286":31},{"_79":302,"_81":302,"_274":-5,"_282":31,"_284":31,"_295":303,"_286":31},"0.3.4",[],"0.4.4",[],"0.4.8",[],[],"-","Latest (0.4.9)","nvidia/monaitoolkit/monai_endoscopic_inbody_classification","key","nvidia/monaitoolkit/models/monai_endoscopic_inbody_classification","resourceType","resourceId","monai_endoscopic_inbody_classification","shortDescription","A pre-trained model for the endoscopic inbody classification task.","## Model Overview\n\nA pre-trained model for the endoscopic inbody classification task and trained using the SEResNet50 structure, whose details can be found in [1]. All datasets are from private samples of [Activ Surgical](https://www.activsurgical.com/). Samples in training and validation dataset are from the same 4 videos, while test samples are from different two videos.\n\nThe [PyTorch model](https://drive.google.com/file/d/14CS-s1uv2q6WedYQGeFbZeEWIkoyNa-x/view?usp=sharing) and [torchscript model](https://drive.google.com/file/d/1fOoJ4n5DWKHrt9QXTZ2sXwr9C-YvVGCM/view?usp=sharing) are shared in google drive. 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_inbody_classification_workflow.png)\n\n### Data\n\nThe datasets used in this work were provided by [Activ Surgical](https://www.activsurgical.com/).\n\nSince datasets are private, we provide a [link](https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/inbody_outbody_samples.zip) of 20 samples (10 in-body and 10 out-body) to show what they look like.\n\n#### Preprocessing\n\nAfter downloading this dataset, python script in `scripts` folder named `data_process` can be used to generate label json files by running the command below and modifying `datapath` to path of unziped downloaded data. Generated label json files will be stored in `label` folder under the bundle path.\n\n```\npython scripts/data_process.py --datapath /path/to/data/root\n```\n\nBy default, label path parameter in `train.json` and `inference.json` of this bundle is point to the generated `label` folder under bundle path. If you move these generated label files to another place, please modify the `train_json`, `val_json` and `test_json` parameters specified in `configs/train.json` and `configs/inference.json` to where these label files are.\n\nThe input label json should be a list made up by dicts which includes `image` and `label` keys. An example format is shown below.\n\n```\n[\n {\n \"image\":\"/path/to/image/image_name0.jpg\",\n \"label\": 0\n },\n {\n \"image\":\"/path/to/image/image_name1.jpg\",\n \"label\": 0\n },\n {\n \"image\":\"/path/to/image/image_name2.jpg\",\n \"label\": 1\n },\n ....\n {\n \"image\":\"/path/to/image/image_namek.jpg\",\n \"label\": 0\n },\n]\n```\n\n### Training configuration\n\nThe training as performed with the following:\n- GPU: At least 12GB of GPU memory\n- Actual Model Input: 256 x 256 x 3\n- Optimizer: Adam\n- Learning Rate: 1e-3\n\n#### Input\n\nA three channel video frame\n\n#### Output\n\nTwo Channels\n- Label 0: in body\n- Label 1: out body\n\n### Performance\n\nAccuracy was used for evaluating the performance of the model. This model achieves an accuracy score of 0.99\n\n##### Training Loss\n\n![A graph showing the training loss over 25 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_endoscopic_inbody_classification_train_loss_v2.png)\n\n##### Validation Accuracy\n\n![A graph showing the validation accuracy over 25 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_endoscopic_inbody_classification_val_accuracy_v2.png)\n\n##### TensorRT speedup\n\nThe `endoscopic_inbody_classification` bundle supports acceleration with TensorRT through the ONNX-TensorRT method. 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 | 6.50 | 9.23 | 2.78 | 2.31 | 0.70 | 2.34 | 2.81 | 4.00 |\n| end2end | 23.54 | 23.78 | 7.37 | 7.14 | 0.99 | 3.19 | 3.30 | 3.33 |\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\nCurrently, the only available method to accelerate this model is through ONNX-TensorRT. However, the Torch-TensorRT method is under development and will be available in the near future.\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 \\\n --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\nIn addition, if using the 20 samples example dataset, the preprocessing script will divide the samples to 16 training samples, 2 validation samples and 2 test samples. However, pytorch multi-gpu training requires number of samples in dataloader larger than gpu numbers. Therefore, please use no more than 2 gpus to run this bundle if using the 20 samples example dataset.\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##### Execute inference:\n\n```\npython -m monai.bundle run --config_file configs/inference.json\n```\n\nThe classification result of every images in `test.json` will be printed to the screen.\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```bash\npython -m monai.bundle trt_export --net_id network_def \\\n--filepath models/model_trt.ts --ckpt_file models/model.pt \\\n--meta_file configs/metadata.json --config_file configs/inference.json \\\n--precision --use_onnx \"True\" --use_trace \"True\"\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] J. Hu, L. Shen and G. Sun, Squeeze-and-Excitation Networks, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132-7141. https://arxiv.org/pdf/1709.01507.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",1731434167902],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/monaitoolkit/models/monai_endoscopic_inbody_classification","rawImageUrl","https://developer.download.nvidia.com/assets/Clara/Images/monai_toolkit_logo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[351,352,353,354,355,356,357,358,359,360],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_320":386,"_362":386,"_363":387,"_365":366},{"_320":384,"_362":384,"_363":385,"_365":366},{"_320":382,"_362":382,"_363":383,"_365":366},{"_320":378,"_362":379,"_363":380,"_365":366},{"_320":376,"_362":376,"_363":377,"_365":366},{"_320":374,"_362":374,"_363":375,"_365":366},{"_320":372,"_362":372,"_363":373,"_365":366},{"_320":370,"_362":370,"_363":371,"_365":366},{"_320":368,"_362":368,"_363":369,"_365":366},{"_320":361,"_362":361,"_363":364,"_365":366},"Pytorch","value","filter",{"_367":366,"_362":361},"category","label","field","NSPECT-OS8Z-HTLQ",{"_367":366,"_362":368},"MONAI",{"_367":366,"_362":370},"Medical Imaging",{"_367":366,"_362":372},"Medical",{"_367":366,"_362":374},"Imaging",{"_367":366,"_362":376},"Healthcare","indus_healthcare",{"_367":381,"_362":379},"industry","Deep Learning",{"_367":366,"_362":382},"Classification",{"_367":366,"_362":384},"Bundle",{"_367":366,"_362":386},{"_210":1562,"_212":1563},"ngcApps",[1515],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1496":1497,"_1498":1499,"_1500":1501,"_1502":1503,"_1504":399,"_242":423,"_1505":1506,"_1507":56,"_1508":189,"_1509":189,"_1510":1511,"_1512":189,"_1513":1514},"amplitude",{"_1493":1494,"_1095":-7,"_1495":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1487":1488,"_1489":1490,"_1491":1492},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1370":272,"_1371":1372,"_1373":1374,"_1375":1376},"announcements",[],"consentState",{"_462":1360,"_1361":189,"_1362":31,"_1363":31,"_1364":1365},"consentConfig",{"_444":445,"_446":447,"_448":449,"_450":451,"_452":453,"_454":455,"_456":457,"_458":459,"_460":461,"_462":463},"showContactAdmin",1784159474848,[],[],[437,438,439,440,441,442],{"_45":59,"_47":69,"_49":205,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":205,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":66,"_49":205,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":205,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":205,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":443,"_55":56},5,"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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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.",[612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646],[485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501],{"_502":608,"_320":608,"_271":272,"_476":609},{"_502":603,"_320":603,"_271":272,"_476":604},{"_502":599,"_320":599,"_271":272,"_476":600},{"_502":594,"_320":594,"_271":272,"_476":595},{"_502":589,"_320":589,"_271":272,"_476":590},{"_502":577,"_320":577,"_271":272,"_476":578},{"_502":565,"_320":565,"_271":272,"_476":566},{"_502":560,"_320":560,"_271":272,"_476":561},{"_502":549,"_320":549,"_271":272,"_476":550},{"_502":544,"_320":544,"_271":272,"_476":545},{"_502":539,"_320":539,"_271":272,"_476":540},{"_502":534,"_320":534,"_271":272,"_476":535},{"_502":529,"_320":529,"_271":272,"_476":530},{"_502":524,"_320":524,"_271":272,"_476":525},{"_502":519,"_320":519,"_271":272,"_476":520},{"_502":512,"_320":512,"_271":272,"_476":513},{"_502":503,"_320":503,"_271":272,"_476":504},"hostName","amazon-adsystem.com",[505,506],{"_81":511,"_508":503,"_509":31,"_271":510},{"_81":507,"_508":503,"_509":31,"_271":510},"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",[514,515],{"_81":518,"_508":512,"_509":31,"_271":517},{"_81":516,"_508":512,"_509":31,"_271":517},"IDE","This domain is owned by Doubleclick (Google). 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It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[541],{"_81":542,"_508":539,"_509":31,"_271":543},"sp","Testing","google.com",[546],{"_81":547,"_508":544,"_509":31,"_271":548},"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",[551,552,553,554],{"_81":559,"_508":549,"_509":31,"_271":556},{"_81":558,"_508":549,"_509":31,"_271":556},{"_81":557,"_508":549,"_509":31,"_271":556},{"_81":555,"_508":549,"_509":31,"_271":556},"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",[562],{"_81":563,"_508":560,"_509":31,"_271":564},"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",[567,568,569,570],{"_81":576,"_508":565,"_509":31,"_271":572},{"_81":574,"_508":565,"_509":31,"_271":575},{"_81":573,"_508":565,"_509":31,"_271":572},{"_81":571,"_508":565,"_509":31,"_271":572},"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",[579,580,581,582],{"_81":588,"_508":577,"_509":31,"_271":586},{"_81":587,"_508":577,"_509":31,"_271":586},{"_81":585,"_508":577,"_509":31,"_271":586},{"_81":583,"_508":577,"_509":31,"_271":584},"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",[591],{"_81":592,"_508":589,"_509":31,"_271":593},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[596],{"_81":597,"_508":594,"_509":31,"_271":598},"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",[601],{"_81":602,"_508":599,"_509":31,"_271":543},"R","6sc.co",[605],{"_81":606,"_508":603,"_509":31,"_271":607},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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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.",[734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754],[699,700,701,702,703],{"_502":594,"_320":594,"_271":272,"_476":725},{"_502":560,"_320":560,"_271":272,"_476":720},{"_502":713,"_320":713,"_271":272,"_476":714},{"_502":709,"_320":709,"_271":272,"_476":710},{"_502":704,"_320":704,"_271":272,"_476":705},"nvcrm.my.site.com",[706],{"_81":707,"_508":704,"_509":31,"_271":708},"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",[711],{"_81":712,"_508":709,"_509":31,"_271":543},"edge","github.com",[715,716],{"_81":719,"_508":713,"_509":31,"_271":718},{"_81":717,"_508":713,"_509":31,"_271":718},"logged_in","This domain is owned by Github. 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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 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LICENSE	11.09 KB	February 28, 2025 UTC	curl wget Download