Model Overview

A pre-trained model for 3D segmentation of the spleen organ from CT images using DeepEdit.

DeepEdit is an algorithm that combines the power of two models in one single architecture. It allows the user to perform inference as a standard segmentation method (i.e., UNet) and interactively segment part of an image using clicks [2]. DeepEdit aims to facilitate the user experience and, at the same time, develop new active learning techniques.

The model was trained on 32 images and validated on 9 images.

Data

The training dataset is the Spleen Task from the Medical Segmentation Decathalon. Users can find more details on the datasets at http://medicaldecathlon.com/.

Target: Spleen
Modality: CT
Size: 61 3D volumes (41 Training + 20 Testing)
Source: Memorial Sloan Kettering Cancer Center
Challenge: Large-ranging foreground size

Training configuration

The training as performed with the following:

GPU: at least 12GB of GPU memory
Actual Model Input: 128 x 128 x 128
AMP: True
Optimizer: Adam
Learning Rate: 1e-4
Loss: DiceCELoss

Input

Three channels

CT image
Spleen Segment
Background Segment

Output

Two channels

Label 1: spleen
Label 0: everything else

Performance

Dice score is used for evaluating the performance of the model. This model achieves a dice score of 0.97, depending on the number of simulated clicks.

Training Dice

$\"A$

Training Loss

$\"A$

Validation Dice

$\"A$

TensorRT speedup

The spleen_deepedit_annotation 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	147.52	40.32	28.87	11.94	3.66	5.11	12.36	3.38
end2end	1292.39	1204.62	1168.09	1149.88	1.07	1.11	1.12	1.05

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

Memory Consumption

Dataset Manager: CacheDataset
Data Size: 61 3D Volumes
Cache Rate: 1.0
Single GPU - System RAM Usage: 8.2G

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.

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

Execute inference:

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

Optionally, clicks can be added to the data dictionary that is passed to the preprocessing transforms. The add keys are defined in label_names in configs/inference.json, and the corresponding values are the point coordinates. The following is an example of a data dictionary:

{\"image\": \"example.nii.gz\", \"background\": [], \"spleen\": [[I1, J1, K1], [I2, J2, K2]]}\n

where [I1,J1,K1] and [I2,J2,K2] are the point coordinates.

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] Diaz-Pinto, Andres, et al. DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images. MICCAI Workshop on Data Augmentation, Labelling, and Imperfections. MICCAI 2022.

[2] Diaz-Pinto, Andres, et al. \"MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images.\" arXiv preprint arXiv:2203.12362 (2022).

[3] Sakinis, Tomas, et al. \"Interactive segmentation of medical images through fully convolutional neural networks.\" arXiv preprint arXiv:1903.08205 (2019).

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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It allows the user to perform inference as a standard segmentation method (i.e., UNet) and interactively segment part of an image using clicks [2]. DeepEdit aims to facilitate the user experience and, at the same time, develop new active learning techniques.\n\nThe model was trained on 32 images and validated on 9 images.\n\n### Data\n\nThe training dataset is the Spleen Task from the Medical Segmentation Decathalon. Users can find more details on the datasets at http://medicaldecathlon.com/.\n\n- Target: Spleen\n- Modality: CT\n- Size: 61 3D volumes (41 Training + 20 Testing)\n- Source: Memorial Sloan Kettering Cancer Center\n- Challenge: Large-ranging foreground size\n\n### Training configuration\n\nThe training as performed with the following:\n- GPU: at least 12GB of GPU memory\n- Actual Model Input: 128 x 128 x 128\n- AMP: True\n- Optimizer: Adam\n- Learning Rate: 1e-4\n- Loss: DiceCELoss\n\n#### Input\n\nThree channels\n- CT image\n- Spleen Segment\n- Background Segment\n\n#### Output\n\nTwo channels\n- Label 1: spleen\n- Label 0: everything else\n\n### Performance\n\nDice score is used for evaluating the performance of the model. This model achieves a dice score of 0.97, depending on the number of simulated clicks.\n\n##### Training Dice\n\n![A graph showing the train dice over 90 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_spleen_deepedit_annotation_train_dice_v2.png)\n\n##### Training Loss\n\n![A graph showing the training loss over 90 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_spleen_deepedit_annotation_train_loss_v2.png)\n\n##### Validation Dice\n\n![A graph showing the validation dice over 90 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_spleen_deepedit_annotation_val_dice_v2.png)\n\n##### TensorRT speedup\n\nThe `spleen_deepedit_annotation` 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 | 147.52 | 40.32 | 28.87 | 11.94 | 3.66 | 5.11 | 12.36 | 3.38 |\n| end2end |1292.39 | 1204.62 | 1168.09 | 1149.88 | 1.07 | 1.11 | 1.12 | 1.05 |\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#### Memory Consumption\n\n- Dataset Manager: CacheDataset\n- Data Size: 61 3D Volumes\n- Cache Rate: 1.0\n- Single GPU - System RAM Usage: 8.2G\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### 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##### Execute inference:\n\n```\npython -m monai.bundle run --config_file configs/inference.json\n```\n\nOptionally, clicks can be added to the data dictionary that is passed to the preprocessing transforms. The add keys are defined in `label_names` in `configs/inference.json`, and the corresponding values are the point coordinates. The following is an example of a data dictionary:\n\n```\n{\"image\": \"example.nii.gz\", \"background\": [], \"spleen\": [[I1, J1, K1], [I2, J2, K2]]}\n```\n\nwhere **[I1,J1,K1]** and **[I2,J2,K2]** are the point coordinates.\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] Diaz-Pinto, Andres, et al. DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images. MICCAI Workshop on Data Augmentation, Labelling, and Imperfections. MICCAI 2022.\n\n[2] Diaz-Pinto, Andres, et al. \"MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images.\" arXiv preprint arXiv:2203.12362 (2022).\n\n[3] Sakinis, Tomas, et al. \"Interactive segmentation of medical images through fully convolutional neural networks.\" arXiv preprint arXiv:1903.08205 (2019).\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",1731434216868],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/monaitoolkit/models/monai_spleen_deepedit_annotation","rawImageUrl","https://developer.download.nvidia.com/assets/Clara/Images/monai_toolkit_logo.png","canGuestDownload","latestVersionSizeInBytes",248252092,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[337,338,339,340,341,342,343,344,345,346,347,348],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_305":378,"_350":379,"_351":380,"_353":354},{"_305":376,"_350":376,"_351":377,"_353":354},{"_305":374,"_350":374,"_351":375,"_353":354},{"_305":372,"_350":372,"_351":373,"_353":354},{"_305":368,"_350":369,"_351":370,"_353":354},{"_305":366,"_350":366,"_351":367,"_353":354},{"_305":364,"_350":364,"_351":365,"_353":354},{"_305":362,"_350":362,"_351":363,"_353":354},{"_305":360,"_350":360,"_351":361,"_353":354},{"_305":358,"_350":358,"_351":359,"_353":354},{"_305":356,"_350":356,"_351":357,"_353":354},{"_305":349,"_350":349,"_351":352,"_353":354},"Spleen","value","filter",{"_355":354,"_350":349},"category","label","field","Pytorch",{"_355":354,"_350":356},"NSPECT-7WQX-5CZ7",{"_355":354,"_350":358},"MONAI",{"_355":354,"_350":360},"Medical Imaging",{"_355":354,"_350":362},"Medical",{"_355":354,"_350":364},"Imaging",{"_355":354,"_350":366},"Healthcare","indus_healthcare",{"_355":371,"_350":369},"industry","Deep Learning",{"_355":354,"_350":372},"CT",{"_355":354,"_350":374},"Bundle",{"_355":354,"_350":376},"Annotation","uscs_annotation",{"_355":381,"_350":379},"application",{"_195":1358,"_197":1359},"ngcApps",[1311],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1292":1293,"_1294":1295,"_1296":1297,"_1298":1299,"_1300":393,"_227":419,"_1301":1302,"_1303":56,"_1304":174,"_1305":174,"_1306":1307,"_1308":174,"_1309":1310},"amplitude",{"_1289":1290,"_931":-7,"_1291":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1283":1284,"_1285":1286,"_1287":1288},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787724709805,"1.12.0","defaultSearchSuggestions",{"_1171":257,"_1172":1173,"_1174":1175,"_1176":16},"announcements",[],"consentState",{"_457":1161,"_1162":31,"_1163":31,"_1164":174,"_1165":1166},"consentConfig",{"_439":440,"_441":442,"_443":444,"_445":446,"_447":448,"_449":450,"_451":452,"_453":454,"_455":456,"_457":458},"showContactAdmin",1787724709806,[],[],[433,434,435,436,437,438],{"_45":60,"_47":70,"_49":190,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":68,"_49":190,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":67,"_49":190,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":190,"_51":-5,"_53":-5,"_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},"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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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,[596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617],[481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497],{"_498":591,"_305":591,"_256":257,"_471":592},{"_498":587,"_305":587,"_256":257,"_471":588},{"_498":582,"_305":582,"_256":257,"_471":583},{"_498":578,"_305":578,"_256":257,"_471":579},{"_498":573,"_305":573,"_256":257,"_471":574},{"_498":568,"_305":568,"_256":257,"_471":569},{"_498":560,"_305":560,"_256":257,"_471":561},{"_498":552,"_305":552,"_256":257,"_471":553},{"_498":547,"_305":547,"_256":257,"_471":548},{"_498":542,"_305":542,"_256":257,"_471":543},{"_498":537,"_305":537,"_256":257,"_471":538},{"_498":532,"_305":532,"_256":257,"_471":533},{"_498":527,"_305":527,"_256":257,"_471":528},{"_498":520,"_305":520,"_256":257,"_471":521},{"_498":515,"_305":515,"_256":257,"_471":516},{"_498":508,"_305":508,"_256":257,"_471":509},{"_498":499,"_305":499,"_256":257,"_471":500},"hostName","amazon-adsystem.com",[501,502],{"_81":507,"_504":499,"_505":31,"_256":506},{"_81":503,"_504":499,"_505":31,"_256":506},"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",[510,511],{"_81":514,"_504":508,"_505":31,"_256":513},{"_81":512,"_504":508,"_505":31,"_256":513},"IDE","This domain is owned by Doubleclick (Google). 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712],[659,660,661,662,663],{"_498":573,"_305":573,"_256":257,"_471":683},{"_498":547,"_305":547,"_256":257,"_471":678},{"_498":673,"_305":673,"_256":257,"_471":674},{"_498":668,"_305":668,"_256":257,"_471":669},{"_498":664,"_305":664,"_256":257,"_471":665},"nvidia.partners",[666],{"_81":667,"_504":664,"_505":31,"_256":541},"edge","app.hushly.com",[670],{"_81":671,"_504":668,"_505":31,"_256":672},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[675],{"_81":676,"_504":673,"_505":31,"_256":677},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799],[751,752,753,754],{"_498":766,"_305":766,"_256":257,"_471":767},{"_498":761,"_305":761,"_256":257,"_471":762},{"_498":560,"_305":560,"_256":257,"_471":758},{"_498":673,"_305":673,"_256":257,"_471":755},[756],{"_81":757,"_504":673,"_505":174,"_256":677},"_gh_sess",[759],{"_81":760,"_504":560,"_505":174,"_256":567},"YSC","bizible.com",[763],{"_81":764,"_504":761,"_505":31,"_256":765},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[768],{"_81":769,"_504":766,"_505":31,"_256":770},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_81":841,"_504":619,"_505":31,"_256":842},{"_81":826,"_504":840,"_505":174,"_256":827},{"_81":809,"_504":622,"_505":31,"_256":734},{"_81":809,"_504":722,"_505":31,"_256":734},{"_81":838,"_504":619,"_505":31,"_256":839},{"_81":826,"_504":625,"_505":174,"_256":827},{"_81":836,"_504":619,"_505":31,"_256":837},{"_81":826,"_504":622,"_505":174,"_256":827},{"_81":834,"_504":721,"_505":31,"_256":835},{"_81":826,"_504":743,"_505":174,"_256":827},{"_81":806,"_504":622,"_505":31,"_256":808},{"_81":832,"_504":619,"_505":174,"_256":833},{"_81":830,"_504":619,"_505":31,"_256":831},{"_81":828,"_504":619,"_505":174,"_256":829},{"_81":826,"_504":717,"_505":174,"_256":827},{"_81":824,"_504":619,"_505":31,"_256":825},{"_81":822,"_504":619,"_505":31,"_256":823},{"_81":820,"_504":619,"_505":174,"_256":821},{"_81":818,"_504":726,"_505":31,"_256":819},{"_81":806,"_504":717,"_505":31,"_256":808},{"_81":816,"_504":619,"_505":31,"_256":817},{"_81":814,"_504":619,"_505":174,"_256":815},{"_81":812,"_504":619,"_505":31,"_256":813},{"_81":810,"_504":619,"_505":31,"_256":811},{"_81":809,"_504":619,"_505":31,"_256":734},{"_81":806,"_504":807,"_505":31,"_256":808},{"_81":804,"_504":619,"_505":31,"_256":805},{"_81":802,"_504":619,"_505":174,"_256":803},{"_81":800,"_504":619,"_505":31,"_256":801},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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Model Overview

Data

Training configuration

Input

Output

Performance

Training Dice

Training Loss

Validation Dice

TensorRT speedup

Memory Consumption

Memory Consumption Warning

MONAI Bundle Commands

Execute training:

Override the train config to execute multi-GPU training:

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

Execute inference:

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

Execute inference with the TensorRT model:

References

License

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

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

Name	Size	Updated	Actions



LICENSE	11.09 KB	February 28, 2025 UTC	curl wget Download