Overview

The VISTA2D is a cell segmentation training and inference pipeline for cell imaging [Blog].

A pretrained model was trained on collection of 15K public microscopy images. The data collection and training can be reproduced following the download_preprocessor/. Alternatively, the model can be retrained on your own dataset. The pretrained vista2d model achieves good performance on diverse set of cell types, microscopy image modalities, and can be further finetuned if necessary. The codebase utilizes several components from other great works including SegmentAnything and Cellpose, which must be pip installed as dependencies. Vista2D codebase follows MONAI bundle format and its specifications.

Model highlights

Robust deep learning algorithm based on transformers
Generalist model as compared to specialist models
Multiple dataset sources and file formats supported
Multiple modalities of imaging data collectively supported
Multi-GPU and multinode training support

Generalization performance

Evaluation was performed for the VISTA2D model with multiple public datasets, such as TissueNet, LIVECell, Omnipose, DeepBacs, Cellpose, and more. For more details about dataset licenses, please refer to /docs/data_license.txt. A total of ~15K annotated cell images were collected to train the generalist VISTA2D model. This ensured broad coverage of many different types of cells, which were acquired by various imaging acquisition types. The benchmark results of the experiment were performed on held-out test sets for each public dataset that were already defined by the dataset contributors. Average precision at an IoU threshold of 0.5 was used for evaluating performance. The benchmark results are reported in comparison with the best numbers found in the literature, in addition to a specialist VISTA2D model trained only on a particular dataset or a subset of data.

TensorRT speedup

The vista2d bundle supports acceleration with TensorRT. The table below displays the speedup ratios observed on an A100 80G GPU. Please note that 32-bit precision models are benchmarked with tf32 weight format.

\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_tf32(ms)	torch_amp(ms)	trt_tf32(ms)	trt_fp16(ms)	speedup amp	speedup tf32	speedup fp16	amp vs fp16
model computation	39.72	39.68	26.13	17.32	1.00	1.52	2.29	2.29
end2end	1562	1903	1494	1440	0.82	1.05	1.08	1.32

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_tf32 and torch_amp are for the PyTorch models with or without amp mode.
trt_tf32 and trt_fp16 are for the TensorRT based models converted in corresponding precision.
speedup amp, speedup tf32 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: 10.3.0+cuda12.6
Torch-TensorRT Version: 2.4.0
CPU Architecture: x86-64
OS: ubuntu 20.04
Python version:3.10.12
CUDA version: 12.6
GPU models and configuration: A100 80G

Prepare Data Lists and Datasets

The default dataset for training, validation, and inference is the Cellpose dataset. Please follow the download_preprocessor/ to prepare the dataset before executing any commands below.

Additionally, all data lists are available in the datalists.zip file located in the root directory of the bundle. Extract the contents of the .zip file to access the data lists.

Dependencies

Please refer to the required_packages_version section in configs/metadata.json to install all necessary dependencies before execution. If you’re using the MONAI container, you can simply run the commands below and ignore any \"opencv-python-headless not installed\" error message, as this package is already included in the container.

pip install fastremap==1.15.0 roifile==2024.5.24 natsort==8.4.0\npip install --no-deps cellpose\n

Important Note: if your environment already contains OpenCV, installing cellpose may lead to conflicts and produce errors such as:

AttributeError: partially initialized module 'cv2' has no attribute 'dnn' (most likely due to a circular import)\n

To resolve this, uninstall OpenCV first, and then install cellpose using the following commands:

pip uninstall -y opencv && rm /usr/local/lib/python3.*/dist-packages/cv2\n

Make sure to replace 3.* with your actual Python version (e.g., 3.10).

Alternatively, you can install cellpose without its dependencies to avoid potential conflicts:

pip install --no-deps cellpose\n

Execute training

python -m monai.bundle run_workflow \"scripts.workflow.VistaCell\" --config_file configs/hyper_parameters.yaml\n

You can override the basedir to specify a different dataset directory by using the following command:

\n\">

python -m monai.bundle run_workflow \"scripts.workflow.VistaCell\" --config_file configs/hyper_parameters.yaml --basedir <actual dataset ditectory>\n

Quick run with a few data points

python -m monai.bundle run_workflow \"scripts.workflow.VistaCell\" --config_file configs/hyper_parameters.yaml --quick True --train#trainer#max_epochs 3\n

Execute multi-GPU training

torchrun --nproc_per_node=gpu -m monai.bundle run_workflow \"scripts.workflow.VistaCell\" --config_file configs/hyper_parameters.yaml\n

Execute validation

python -m monai.bundle run_workflow \"scripts.workflow.VistaCell\" --config_file configs/hyper_parameters.yaml --pretrained_ckpt_name model.pt --mode eval\n

(can append --quick True for quick demoing)

Execute multi-GPU validation

torchrun --nproc_per_node=gpu -m monai.bundle run_workflow \"scripts.workflow.VistaCell\" --config_file configs/hyper_parameters.yaml --mode eval\n

Execute inference

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

Please note that the data used in this config file is: \"/cellpose_dataset/test/001_img.png\", if the dataset path is different or you want to do inference on another file, please modify in configs/inference.json accordingly.

Execute inference with the TensorRT model:

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

Execute multi-GPU inference

torchrun --nproc_per_node=gpu -m monai.bundle run_workflow \"scripts.workflow.VistaCell\" --config_file configs/hyper_parameters.yaml --mode infer --pretrained_ckpt_name model.pt\n

(can append --quick True for quick demoing)

Finetune starting from a trained checkpoint

(we use a smaller learning rate, small number of epochs, and initialize from a checkpoint)

python -m monai.bundle run_workflow \"scripts.workflow.VistaCell\" --config_file configs/hyper_parameters.yaml --learning_rate=0.001 --train#trainer#max_epochs 20 --pretrained_ckpt_path /path/to/saved/model.pt\n

Configuration options

To disable the segmentation writing:

--postprocessing []\n

Load a checkpoint for validation or inference (relative path within results directory):

--pretrained_ckpt_name \"model.pt\"\n

Load a checkpoint for validation or inference (absolute path):

--pretrained_ckpt_path \"/path/to/another/location/model.pt\"\n

--mode eval or --mode inferwill use the corresponding configurations from the validate or infer\nof the configs/hyper_parameters.yaml.

By default the generated model.pt corresponds to the checkpoint at the best validation score,\nmodel_final.pt is the checkpoint after the latest training epoch.

Development

For development purposes it's possible to run the script directly (without monai bundle calls)

python scripts/workflow.py --config_file configs/hyper_parameters.yaml ...\ntorchrun --nproc_per_node=gpu -m  scripts/workflow.py --config_file configs/hyper_parameters.yaml  ..\n

MLFlow support

Enable MLFlow logging by specifying \"mlflow_tracking_uri\" (can be local or remote URL).

python -m monai.bundle run_workflow \"scripts.workflow.VistaCell\" --config_file configs/hyper_parameters.yaml --mlflow_tracking_uri=http://127.0.0.1:8080\n

Optionally use \"--mlflow_run_name=..\" to specify MLFlow experiment name, and \"--mlflow_log_system_metrics=True/False\" to enable logging of CPU/GPU resources (requires pip install psutil pynvml)

Unit tests

Test single GPU training:

python unit_tests/test_vista2d.py\n

Test multi-GPU training (may need to uncomment the \"--standalone\" in the unit_tests/utils.py file):

python unit_tests/test_vista2d_mgpu.py\n

Compute Requirements

Min GPU memory requirements 16Gb.

Contributing

Vista2D codebase follows MONAI bundle format and its specifications.\nMake sure to run pre-commit before committing code changes to git

pip install pre-commit\npython3 -m pre_commit run --all-files\n

Community

Join the conversation on Twitter @ProjectMONAI or join\nour Slack channel.

Ask and answer questions on MONAI VISTA's GitHub discussions tab.

License

The codebase is under Apache 2.0 Licence. The model weight is released under CC-BY-NC-SA-4.0. For various public data licenses please see data_license.txt.

Acknowledgement

segment-anything
Cellpose

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\n\n### Model highlights\n\n- Robust deep learning algorithm based on transformers\n- Generalist model as compared to specialist models\n- Multiple dataset sources and file formats supported\n- Multiple modalities of imaging data collectively supported\n- Multi-GPU and multinode training support\n\n### Generalization performance\n\nEvaluation was performed for the VISTA2D model with multiple public datasets, such as TissueNet, LIVECell, Omnipose, DeepBacs, Cellpose, and more. For more details about dataset licenses, please refer to `/docs/data_license.txt`. A total of ~15K annotated cell images were collected to train the generalist VISTA2D model. This ensured broad coverage of many different types of cells, which were acquired by various imaging acquisition types. The benchmark results of the experiment were performed on held-out test sets for each public dataset that were already defined by the dataset contributors. Average precision at an IoU threshold of 0.5 was used for evaluating performance. The benchmark results are reported in comparison with the best numbers found in the literature, in addition to a specialist VISTA2D model trained only on a particular dataset or a subset of data.\n\n

\n\n### TensorRT speedup\nThe `vista2d` bundle supports acceleration with TensorRT. The table below displays the speedup ratios observed on an A100 80G GPU. Please note that 32-bit precision models are benchmarked with tf32 weight format.\n\n| method | torch_tf32(ms) | torch_amp(ms) | trt_tf32(ms) | trt_fp16(ms) | speedup amp | speedup tf32 | speedup fp16 | amp vs fp16|\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| model computation | 39.72 | 39.68 | 26.13 | 17.32 | 1.00 | 1.52 | 2.29 | 2.29 |\n| end2end | 1562 | 1903 | 1494 | 1440 | 0.82 | 1.05 | 1.08 | 1.32|\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_tf32` and `torch_amp` are for the PyTorch models with or without `amp` mode.\n- `trt_tf32` and `trt_fp16` are for the TensorRT based models converted in corresponding precision.\n- `speedup amp`, `speedup tf32` 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: 10.3.0+cuda12.6\n - Torch-TensorRT Version: 2.4.0\n - CPU Architecture: x86-64\n - OS: ubuntu 20.04\n - Python version:3.10.12\n - CUDA version: 12.6\n - GPU models and configuration: A100 80G\n\n### Prepare Data Lists and Datasets\n\nThe default dataset for training, validation, and inference is the [Cellpose](https://www.cellpose.org/) dataset. Please follow the `download_preprocessor/` to prepare the dataset before executing any commands below.\n\nAdditionally, all data lists are available in the `datalists.zip` file located in the root directory of the bundle. Extract the contents of the `.zip` file to access the data lists.\n\n### Dependencies\nPlease refer to the `required_packages_version` section in `configs/metadata.json` to install all necessary dependencies before execution. If you’re using the MONAI container, you can simply run the commands below and ignore any \"opencv-python-headless not installed\" error message, as this package is already included in the container.\n\n```\npip install fastremap==1.15.0 roifile==2024.5.24 natsort==8.4.0\npip install --no-deps cellpose\n```\n\nImportant Note: if your environment already contains OpenCV, installing `cellpose` may lead to conflicts and produce errors such as:\n\n```\nAttributeError: partially initialized module 'cv2' has no attribute 'dnn' (most likely due to a circular import)\n```\n\nTo resolve this, uninstall `OpenCV` first, and then install `cellpose` using the following commands:\n\n```Bash\npip uninstall -y opencv && rm /usr/local/lib/python3.*/dist-packages/cv2\n```\nMake sure to replace 3.* with your actual Python version (e.g., 3.10).\n\nAlternatively, you can install `cellpose` without its dependencies to avoid potential conflicts:\n\n```\npip install --no-deps cellpose\n```\n\n### Execute training\n```bash\npython -m monai.bundle run_workflow \"scripts.workflow.VistaCell\" --config_file configs/hyper_parameters.yaml\n```\n\nYou can override the `basedir` to specify a different dataset directory by using the following command:\n\n```bash\npython -m monai.bundle run_workflow \"scripts.workflow.VistaCell\" --config_file configs/hyper_parameters.yaml --basedir \n```\n\n#### Quick run with a few data points\n```bash\npython -m monai.bundle run_workflow \"scripts.workflow.VistaCell\" --config_file configs/hyper_parameters.yaml --quick True --train#trainer#max_epochs 3\n```\n\n### Execute multi-GPU training\n```bash\ntorchrun --nproc_per_node=gpu -m monai.bundle run_workflow \"scripts.workflow.VistaCell\" --config_file configs/hyper_parameters.yaml\n```\n\n### Execute validation\n```bash\npython -m monai.bundle run_workflow \"scripts.workflow.VistaCell\" --config_file configs/hyper_parameters.yaml --pretrained_ckpt_name model.pt --mode eval\n```\n(can append `--quick True` for quick demoing)\n\n### Execute multi-GPU validation\n```bash\ntorchrun --nproc_per_node=gpu -m monai.bundle run_workflow \"scripts.workflow.VistaCell\" --config_file configs/hyper_parameters.yaml --mode eval\n```\n\n### Execute inference\n```bash\npython -m monai.bundle run --config_file configs/inference.json\n```\n\nPlease note that the data used in this config file is: \"/cellpose_dataset/test/001_img.png\", if the dataset path is different or you want to do inference on another file, please modify in `configs/inference.json` accordingly.\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### Execute multi-GPU inference\n```bash\ntorchrun --nproc_per_node=gpu -m monai.bundle run_workflow \"scripts.workflow.VistaCell\" --config_file configs/hyper_parameters.yaml --mode infer --pretrained_ckpt_name model.pt\n```\n(can append `--quick True` for quick demoing)\n\n\n\n#### Finetune starting from a trained checkpoint\n(we use a smaller learning rate, small number of epochs, and initialize from a checkpoint)\n```bash\npython -m monai.bundle run_workflow \"scripts.workflow.VistaCell\" --config_file configs/hyper_parameters.yaml --learning_rate=0.001 --train#trainer#max_epochs 20 --pretrained_ckpt_path /path/to/saved/model.pt\n```\n\n\n#### Configuration options\n\nTo disable the segmentation writing:\n```\n--postprocessing []\n```\n\nLoad a checkpoint for validation or inference (relative path within results directory):\n```\n--pretrained_ckpt_name \"model.pt\"\n```\n\nLoad a checkpoint for validation or inference (absolute path):\n```\n--pretrained_ckpt_path \"/path/to/another/location/model.pt\"\n```\n\n`--mode eval` or `--mode infer`will use the corresponding configurations from the `validate` or `infer`\nof the `configs/hyper_parameters.yaml`.\n\nBy default the generated `model.pt` corresponds to the checkpoint at the best validation score,\n`model_final.pt` is the checkpoint after the latest training epoch.\n\n\n### Development\n\nFor development purposes it's possible to run the script directly (without monai bundle calls)\n\n```bash\npython scripts/workflow.py --config_file configs/hyper_parameters.yaml ...\ntorchrun --nproc_per_node=gpu -m scripts/workflow.py --config_file configs/hyper_parameters.yaml ..\n```\n\n### MLFlow support\n\nEnable MLFlow logging by specifying \"mlflow_tracking_uri\" (can be local or remote URL).\n\n```bash\npython -m monai.bundle run_workflow \"scripts.workflow.VistaCell\" --config_file configs/hyper_parameters.yaml --mlflow_tracking_uri=http://127.0.0.1:8080\n```\n\nOptionally use \"--mlflow_run_name=..\" to specify MLFlow experiment name, and \"--mlflow_log_system_metrics=True/False\" to enable logging of CPU/GPU resources (requires pip install psutil pynvml)\n\n\n\n### Unit tests\n\nTest single GPU training:\n```\npython unit_tests/test_vista2d.py\n```\n\nTest multi-GPU training (may need to uncomment the `\"--standalone\"` in the `unit_tests/utils.py` file):\n```\npython unit_tests/test_vista2d_mgpu.py\n```\n\n## Compute Requirements\nMin GPU memory requirements 16Gb.\n\n\n## Contributing\nVista2D codebase follows MONAI bundle format and its [specifications](https://docs.monai.io/en/stable/mb_specification.html).\nMake sure to run pre-commit before committing code changes to git\n```bash\npip install pre-commit\npython3 -m pre_commit run --all-files\n```\n\n\n## Community\n\nJoin the conversation on Twitter [@ProjectMONAI](https://twitter.com/ProjectMONAI) or join\nour [Slack channel](https://projectmonai.slack.com/archives/C031QRE0M1C).\n\nAsk and answer questions on [MONAI VISTA's GitHub discussions tab](https://github.com/Project-MONAI/VISTA/discussions).\n\n## License\n\nThe codebase is under Apache 2.0 Licence. The model weight is released under CC-BY-NC-SA-4.0. For various public data licenses please see `data_license.txt`.\n\n## Acknowledgement\n- [segment-anything](https://github.com/facebookresearch/segment-anything)\n- [Cellpose](https://www.cellpose.org/)","displayName",["D",1731434365198],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/monaitoolkit/models/monai_vista2d","rawImageUrl","https://developer.download.nvidia.com/assets/Clara/Images/monai_toolkit_logo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[243,244,245,246,247,248,249,250,251,252],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_213":278,"_254":278,"_255":279,"_257":258},{"_213":276,"_254":276,"_255":277,"_257":258},{"_213":272,"_254":273,"_255":274,"_257":258},{"_213":270,"_254":270,"_255":271,"_257":258},{"_213":268,"_254":268,"_255":269,"_257":258},{"_213":266,"_254":266,"_255":267,"_257":258},{"_213":264,"_254":264,"_255":265,"_257":258},{"_213":262,"_254":262,"_255":263,"_257":258},{"_213":260,"_254":260,"_255":261,"_257":258},{"_213":253,"_254":253,"_255":256,"_257":258},"Segmentation","value","filter",{"_259":258,"_254":253},"category","label","field","Pytorch",{"_259":258,"_254":260},"NSPECT-T882-R24M",{"_259":258,"_254":262},"MONAI",{"_259":258,"_254":264},"Medical Imaging",{"_259":258,"_254":266},"Medical",{"_259":258,"_254":268},"Imaging",{"_259":258,"_254":270},"Healthcare","indus_healthcare",{"_259":275,"_254":273},"industry","Deep Learning",{"_259":258,"_254":276},"Bundle",{"_259":258,"_254":278},{"_123":1452,"_125":1453},"ngcApps",[1405],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1386":1387,"_1388":1389,"_1390":1391,"_1392":1393,"_1394":291,"_154":315,"_1395":1396,"_1397":59,"_1398":102,"_1399":102,"_1400":1401,"_1402":102,"_1403":1404},"amplitude",{"_1383":1384,"_986":-7,"_1385":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1377":1378,"_1379":1380,"_1381":1382},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1261":185,"_1262":1263,"_1264":1265,"_1266":1267},"announcements",[],"consentState",{"_352":1251,"_1252":102,"_1253":34,"_1254":34,"_1255":1256},"consentConfig",{"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351,"_352":353},"showContactAdmin",1783980873665,[],[],[329,330,331,332,333],{"_48":63,"_50":74,"_52":118,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":118,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":118,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":118,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":118,"_54":-5,"_56":-5,"_58":59},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1243":1244,"_1245":1246,"_1247":1248,"_1249":1250},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[354,355,356,357,358],{"_359":895,"_78":896,"_184":897,"_363":102,"_364":16,"_366":898,"_368":899},{"_359":685,"_78":686,"_184":687,"_363":34,"_364":62,"_366":688,"_368":689},{"_359":584,"_78":585,"_184":586,"_363":34,"_364":587,"_366":588,"_368":589},{"_359":370,"_78":371,"_184":372,"_363":34,"_364":57,"_366":373,"_368":374},{"_359":360,"_78":361,"_184":362,"_363":34,"_364":365,"_366":367,"_368":369},"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.",[502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536],[375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391],{"_392":498,"_213":498,"_184":185,"_366":499},{"_392":493,"_213":493,"_184":185,"_366":494},{"_392":489,"_213":489,"_184":185,"_366":490},{"_392":484,"_213":484,"_184":185,"_366":485},{"_392":479,"_213":479,"_184":185,"_366":480},{"_392":467,"_213":467,"_184":185,"_366":468},{"_392":455,"_213":455,"_184":185,"_366":456},{"_392":450,"_213":450,"_184":185,"_366":451},{"_392":439,"_213":439,"_184":185,"_366":440},{"_392":434,"_213":434,"_184":185,"_366":435},{"_392":429,"_213":429,"_184":185,"_366":430},{"_392":424,"_213":424,"_184":185,"_366":425},{"_392":419,"_213":419,"_184":185,"_366":420},{"_392":414,"_213":414,"_184":185,"_366":415},{"_392":409,"_213":409,"_184":185,"_366":410},{"_392":402,"_213":402,"_184":185,"_366":403},{"_392":393,"_213":393,"_184":185,"_366":394},"hostName","amazon-adsystem.com",[395,396],{"_78":401,"_398":393,"_399":34,"_184":400},{"_78":397,"_398":393,"_399":34,"_184":400},"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",[404,405],{"_78":408,"_398":402,"_399":34,"_184":407},{"_78":406,"_398":402,"_399":34,"_184":407},"IDE","This domain is owned by Doubleclick (Google). 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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.",4,[625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645],[590,591,592,593,594],{"_392":484,"_213":484,"_184":185,"_366":616},{"_392":450,"_213":450,"_184":185,"_366":611},{"_392":604,"_213":604,"_184":185,"_366":605},{"_392":600,"_213":600,"_184":185,"_366":601},{"_392":595,"_213":595,"_184":185,"_366":596},"nvcrm.my.site.com",[597],{"_78":598,"_398":595,"_399":34,"_184":599},"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",[602],{"_78":603,"_398":600,"_399":34,"_184":433},"edge","github.com",[606,607],{"_78":610,"_398":604,"_399":34,"_184":609},{"_78":608,"_398":604,"_399":34,"_184":609},"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. 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