Model Overview

A pre-trained model for classifying nuclei cells as the following types

Other
Inflammatory
Epithelial
Spindle-Shaped

This model is trained using DenseNet121 over ConSeP dataset.

Data

The training dataset is from https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet

wget https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/consep_dataset.zip\nunzip -q consep_dataset.zip\n

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Preprocessing

After downloading this dataset,\npython script data_process.py from scripts folder can be used to preprocess and generate the final dataset for training.

python scripts/data_process.py --input /path/to/data/CoNSeP --output /path/to/data/CoNSePNuclei\n

After generating the output files, please modify the dataset_dir parameter specified in configs/train.json and configs/inference.json to reflect the output folder which contains new dataset.json.

Class values in dataset are

1 = other
2 = inflammatory
3 = healthy epithelial
4 = dysplastic/malignant epithelial
5 = fibroblast
6 = muscle
7 = endothelial

As part of pre-processing, the following steps are executed.

Crop and Extract each nuclei Image + Label (128x128) based on the centroid given in the dataset.
Combine classes 3 & 4 into the epithelial class and 5,6 & 7 into the spindle-shaped class.
Update the label index for the target nuclie based on the class value
Other cells which are part of the patch are modified to have label idex = 255

Example dataset.json in output folder:

{\n  \"training\": [\n    {\n      \"image\": \"/workspace/data/CoNSePNuclei/Train/Images/train_1_3_0001.png\",\n      \"label\": \"/workspace/data/CoNSePNuclei/Train/Labels/train_1_3_0001.png\",\n      \"nuclei_id\": 1,\n      \"mask_value\": 3,\n      \"centroid\": [\n        64,\n        64\n      ]\n    }\n  ],\n  \"validation\": [\n    {\n      \"image\": \"/workspace/data/CoNSePNuclei/Test/Images/test_1_3_0001.png\",\n      \"label\": \"/workspace/data/CoNSePNuclei/Test/Labels/test_1_3_0001.png\",\n      \"nuclei_id\": 1,\n      \"mask_value\": 3,\n      \"centroid\": [\n        64,\n        64\n      ]\n    }\n  ]\n}\n

Training configuration

The training was performed with the following:

GPU: at least 12GB of GPU memory
Actual Model Input: 4 x 128 x 128
AMP: True
Optimizer: Adam
Learning Rate: 1e-4
Loss: torch.nn.CrossEntropyLoss
Dataset Manager: CacheDataset

Memory Consumption Warning

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

Input

4 channels

3 RGB channels
1 signal channel (label mask)

Output

4 channels

0 = Other
1 = Inflammatory
2 = Epithelial
3 = Spindle-Shaped

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Performance

This model achieves the following F1 score on the validation data provided as part of the dataset:

Train F1 score = 0.926
Validation F1 score = 0.852

\nConfusion Metrics for Validation for individual classes are:\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\">

Metric	Other	Inflammatory	Epithelial	Spindle-Shaped
Precision	0.6909	0.7773	0.9078	0.8478
Recall	0.2754	0.7831	0.9533	0.8514
F1-score	0.3938	0.7802	0.9300	0.8496

\nConfusion Metrics for Training for individual classes are:\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\">

Metric	Other	Inflammatory	Epithelial	Spindle-Shaped
Precision	0.8000	0.9076	0.9560	0.9019
Recall	0.6512	0.9028	0.9690	0.8989
F1-score	0.7179	0.9052	0.9625	0.9004

Training Loss and F1

A graph showing the training Loss and F1-score over 100 epochs.

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\n $\"\"$

Validation F1

A graph showing the validation F1-score over 100 epochs.

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TensorRT speedup

This 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	20.47	20.57	2.49	1.48	1.00	8.22	13.83	13.90
end2end	45	49	18	18	0.92	2.50	2.50	2.72

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

MONAI Bundle Commands

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

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

Execute training:

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

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

\n\">

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

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

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

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

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

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

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

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

Execute inference:

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

Execute inference with the TensorRT model:

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

References

[1] S. Graham, Q. D. Vu, S. E. A. Raza, A. Azam, Y-W. Tsang, J. T. Kwak and N. Rajpoot. \"HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images.\" Medical Image Analysis, Sept. 2019. [doi]

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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\n\n#### Preprocessing\n\nAfter [downloading this dataset](https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/consep_dataset.zip),\npython script `data_process.py` from `scripts` folder can be used to preprocess and generate the final dataset for training.\n\n```commandline\npython scripts/data_process.py --input /path/to/data/CoNSeP --output /path/to/data/CoNSePNuclei\n```\n\nAfter generating the output files, please modify the `dataset_dir` parameter specified in `configs/train.json` and `configs/inference.json` to reflect the output folder which contains new dataset.json.\n\nClass values in dataset are\n\n- 1 = other\n- 2 = inflammatory\n- 3 = healthy epithelial\n- 4 = dysplastic/malignant epithelial\n- 5 = fibroblast\n- 6 = muscle\n- 7 = endothelial\n\nAs part of pre-processing, the following steps are executed.\n\n- Crop and Extract each nuclei Image + Label (128x128) based on the centroid given in the dataset.\n- Combine classes 3 & 4 into the epithelial class and 5,6 & 7 into the spindle-shaped class.\n- Update the label index for the target nuclie based on the class value\n- Other cells which are part of the patch are modified to have label idex = 255\n\nExample `dataset.json` in output folder:\n\n```json\n{\n \"training\": [\n {\n \"image\": \"/workspace/data/CoNSePNuclei/Train/Images/train_1_3_0001.png\",\n \"label\": \"/workspace/data/CoNSePNuclei/Train/Labels/train_1_3_0001.png\",\n \"nuclei_id\": 1,\n \"mask_value\": 3,\n \"centroid\": [\n 64,\n 64\n ]\n }\n ],\n \"validation\": [\n {\n \"image\": \"/workspace/data/CoNSePNuclei/Test/Images/test_1_3_0001.png\",\n \"label\": \"/workspace/data/CoNSePNuclei/Test/Labels/test_1_3_0001.png\",\n \"nuclei_id\": 1,\n \"mask_value\": 3,\n \"centroid\": [\n 64,\n 64\n ]\n }\n ]\n}\n```\n\n### Training configuration\n\nThe training was performed with the following:\n\n- GPU: at least 12GB of GPU memory\n- Actual Model Input: 4 x 128 x 128\n- AMP: True\n- Optimizer: Adam\n- Learning Rate: 1e-4\n- Loss: torch.nn.CrossEntropyLoss\n- Dataset Manager: CacheDataset\n\n#### Memory Consumption Warning\n\nIf you face memory issues with CacheDataset, you can either switch to a regular Dataset class or lower the caching rate `cache_rate` in the configurations within range [0, 1] to minimize the System RAM requirements.\n\n### Input\n\n4 channels\n- 3 RGB channels\n- 1 signal channel (label mask)\n\n### Output\n\n4 channels\n- 0 = Other\n- 1 = Inflammatory\n- 2 = Epithelial\n- 3 = Spindle-Shaped\n\n![](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_classification_val_in_out.jpeg)\n\n### Performance\n\nThis model achieves the following F1 score on the validation data provided as part of the dataset:\n\n- Train F1 score = 0.926\n- Validation F1 score = 0.852\n\n

\nConfusion Metrics for Validation for individual classes are:\n\n| Metric | Other | Inflammatory | Epithelial | Spindle-Shaped |\n|-----------|--------|--------------|------------|----------------|\n| Precision | 0.6909 | 0.7773 | 0.9078 | 0.8478 |\n| Recall | 0.2754 | 0.7831 | 0.9533 | 0.8514 |\n| F1-score | 0.3938 | 0.7802 | 0.9300 | 0.8496 |\n\n

\nConfusion Metrics for Training for individual classes are:\n\n| Metric | Other | Inflammatory | Epithelial | Spindle-Shaped |\n|-----------|--------|--------------|------------|----------------|\n| Precision | 0.8000 | 0.9076 | 0.9560 | 0.9019 |\n| Recall | 0.6512 | 0.9028 | 0.9690 | 0.8989 |\n| F1-score | 0.7179 | 0.9052 | 0.9625 | 0.9004 |\n\n##### Training Loss and F1\n\nA graph showing the training Loss and F1-score over 100 epochs.\n\n![](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_classification_train_loss_v3.png)
\n![](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_classification_train_f1_v3.png)
\n\n##### Validation F1\n\nA graph showing the validation F1-score over 100 epochs.\n\n![](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_classification_val_f1_v3.png)
\n\n##### TensorRT speedup\n\nThis 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 | 20.47 | 20.57 | 2.49 | 1.48 | 1.00 | 8.22 | 13.83 | 13.90 |\n| end2end | 45 | 49 | 18 | 18 | 0.92 | 2.50 | 2.50 | 2.72 |\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### MONAI Bundle Commands\n\nIn addition to the Pythonic APIs, a few command line interfaces (CLI) are provided to interact with the bundle. The CLI supports flexible use cases, such as overriding configs at runtime and predefining arguments in a file.\n\nFor more details usage instructions, visit the [MONAI Bundle Configuration Page](https://docs.monai.io/en/latest/config_syntax.html).\n\n##### Execute training:\n\n```\npython -m monai.bundle run --config_file configs/train.json\n```\n\nPlease note that if the default dataset path is not modified with the actual path in the bundle config files, you can also override it by using `--dataset_dir`:\n\n```\npython -m monai.bundle run --config_file configs/train.json --dataset_dir \n```\n\n##### Override the `train` config to execute multi-GPU training:\n\n```\ntorchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file \"['configs/train.json','configs/multi_gpu_train.json']\"\n```\n\nPlease note that the distributed training-related options depend on the actual running environment; thus, users may need to remove `--standalone`, modify `--nnodes`, or do some other necessary changes according to the machine used. For more details, please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html).\n\n##### Override the `train` config to execute evaluation with the trained model:\n\n```\npython -m monai.bundle run --config_file \"['configs/train.json','configs/evaluate.json']\"\n```\n\n##### Override the `train` config and `evaluate` config to execute multi-GPU evaluation:\n\n```\ntorchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file \"['configs/train.json','configs/evaluate.json','configs/multi_gpu_evaluate.json']\"\n```\n\n##### Execute inference:\n\n```\npython -m monai.bundle run --config_file configs/inference.json\n```\n\n##### 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] S. Graham, Q. D. Vu, S. E. A. Raza, A. Azam, Y-W. Tsang, J. T. Kwak and N. Rajpoot. \"HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images.\" Medical Image Analysis, Sept. 2019. [[doi](https://doi.org/10.1016/j.media.2019.101563)]\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",1731434192321],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/monaitoolkit/models/monai_pathology_nuclei_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",[376,377,378,379,380,381,382,383,384,385,386],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_345":414,"_388":414,"_389":415,"_391":392},{"_345":412,"_388":412,"_389":413,"_391":392},{"_345":410,"_388":410,"_389":411,"_391":392},{"_345":406,"_388":407,"_389":408,"_391":392},{"_345":404,"_388":404,"_389":405,"_391":392},{"_345":402,"_388":402,"_389":403,"_391":392},{"_345":400,"_388":400,"_389":401,"_391":392},{"_345":398,"_388":398,"_389":399,"_391":392},{"_345":396,"_388":396,"_389":397,"_391":392},{"_345":394,"_388":394,"_389":395,"_391":392},{"_345":387,"_388":387,"_389":390,"_391":392},"Pytorch","value","filter",{"_393":392,"_388":387},"category","label","field","Nuclei",{"_393":392,"_388":394},"NSPECT-309B-GO9E",{"_393":392,"_388":396},"MONAI",{"_393":392,"_388":398},"Medical Imaging",{"_393":392,"_388":400},"Medical",{"_393":392,"_388":402},"Imaging",{"_393":392,"_388":404},"Healthcare","indus_healthcare",{"_393":409,"_388":407},"industry","Deep Learning",{"_393":392,"_388":410},"Classification",{"_393":392,"_388":412},"Bundle",{"_393":392,"_388":414},{"_241":1591,"_243":1592},"ngcApps",[1544],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1525":1526,"_1527":1528,"_1529":1530,"_1531":1532,"_1533":427,"_272":451,"_1534":1535,"_1536":56,"_1537":220,"_1538":220,"_1539":1540,"_1541":220,"_1542":1543},"amplitude",{"_1522":1523,"_1124":-7,"_1524":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1516":1517,"_1518":1519,"_1520":1521},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1399":300,"_1400":1401,"_1402":1403,"_1404":1405},"announcements",[],"consentState",{"_489":1389,"_1390":220,"_1391":31,"_1392":31,"_1393":1394},"consentConfig",{"_471":472,"_473":474,"_475":476,"_477":478,"_479":480,"_481":482,"_483":484,"_485":486,"_487":488,"_489":490},"showContactAdmin",1784045570961,[],[],[465,466,467,468,469,470],{"_45":60,"_47":71,"_49":236,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":236,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":67,"_49":236,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":236,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":236,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":236,"_51":-5,"_53":-5,"_55":56},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1381":1382,"_1383":1384,"_1385":1386,"_1387":1388},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[491,492,493,494,495],{"_496":1033,"_83":1034,"_299":1035,"_500":220,"_501":18,"_503":1036,"_505":1037},{"_496":823,"_83":824,"_299":825,"_500":31,"_501":54,"_503":826,"_505":827},{"_496":722,"_83":723,"_299":724,"_500":31,"_501":725,"_503":726,"_505":727},{"_496":507,"_83":508,"_299":509,"_500":31,"_501":510,"_503":511,"_505":512},{"_496":497,"_83":498,"_299":499,"_500":31,"_501":502,"_503":504,"_505":506},"optanonGroupId","C0005","Social Media Cookies","These cookies are set by a range of social media services that we have added to the site to enable you to share our content with your friends and networks. They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674],[513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529],{"_530":636,"_345":636,"_299":300,"_503":637},{"_530":631,"_345":631,"_299":300,"_503":632},{"_530":627,"_345":627,"_299":300,"_503":628},{"_530":622,"_345":622,"_299":300,"_503":623},{"_530":617,"_345":617,"_299":300,"_503":618},{"_530":605,"_345":605,"_299":300,"_503":606},{"_530":593,"_345":593,"_299":300,"_503":594},{"_530":588,"_345":588,"_299":300,"_503":589},{"_530":577,"_345":577,"_299":300,"_503":578},{"_530":572,"_345":572,"_299":300,"_503":573},{"_530":567,"_345":567,"_299":300,"_503":568},{"_530":562,"_345":562,"_299":300,"_503":563},{"_530":557,"_345":557,"_299":300,"_503":558},{"_530":552,"_345":552,"_299":300,"_503":553},{"_530":547,"_345":547,"_299":300,"_503":548},{"_530":540,"_345":540,"_299":300,"_503":541},{"_530":531,"_345":531,"_299":300,"_503":532},"hostName","amazon-adsystem.com",[533,534],{"_83":539,"_536":531,"_537":31,"_299":538},{"_83":535,"_536":531,"_537":31,"_299":538},"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",[542,543],{"_83":546,"_536":540,"_537":31,"_299":545},{"_83":544,"_536":540,"_537":31,"_299":545},"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,[763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783],[728,729,730,731,732],{"_530":622,"_345":622,"_299":300,"_503":754},{"_530":588,"_345":588,"_299":300,"_503":749},{"_530":742,"_345":742,"_299":300,"_503":743},{"_530":738,"_345":738,"_299":300,"_503":739},{"_530":733,"_345":733,"_299":300,"_503":734},"nvcrm.my.site.com",[735],{"_83":736,"_536":733,"_537":31,"_299":737},"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",[740],{"_83":741,"_536":738,"_537":31,"_299":571},"edge","github.com",[744,745],{"_83":748,"_536":742,"_537":31,"_299":747},{"_83":746,"_536":742,"_537":31,"_299":747},"logged_in","This domain is owned by Github. 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The data is aggregated and analyzed; therefore, it remains anonymous upon use.\n","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","_biz_nA","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website 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Model Overview

Data

Preprocessing

Training configuration

Memory Consumption Warning

Input

Output

Performance

Training Loss and F1

Validation F1

TensorRT speedup

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:

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

Execute inference:

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:

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

Name	Size	Updated	Actions




LICENSE	11.09 KB	February 28, 2025 UTC	curl wget Download