Description:

Polyp Detection RT-DETR is a RT-DETR model that is designed to detect polyps in colonoscopy images.\nThis model is ready for commercial use.

License/Terms of Use:

GOVERNING TERMS: Use of this model is governed by the NVIDIA Open Model License. Additional Information: Apache License Version 2.0.\nYou are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.

Reference(s):

The model is trained on the REAL-Colon dataset [1], utilizing the RT-DETR v2 architecture [2] and the ResNet50 backbone [3]. The backbone is pretrained on the NVImageNet dataset.

\n
[1] Biffi, Carlo, et al. \"REAL-Colon: A dataset for developing real-world AI applications in colonoscopy.\" Scientific Data 11.1 (2024): 539.
\n

\n
[2] Lv, Wenyu, et al. \"Rt-detrv2: Improved baseline with bag-of-freebies for real-time detection transformer.\" arXiv preprint arXiv:2407.17140 (2024).
\n

\n
[3] He, Kaiming, et al. \"Deep residual learning for image recognition.\" Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
\n

Model Architecture:

Architecture Type: Convolution Neural Network (CNN), Transformer
\nNetwork Architecture: RT DETR V2 with ResNet50 backbone

Input:

Input Type(s): Image
\nInput Format(s): Red, Green, Blue (RGB)
\nInput Parameters: Two-Dimensional (2D)
\nOther Properties Related to Input: Image Range Needed (640 x 640 x 3), Pre-Processing Needed (value range [0, 255])

Output:

Output Type(s): A dictionary which contains two keys: \"pred_logits\" and \"pred_boxes\"
\nOutput Format: The value of key \"pred_logits\" is a tensor with shape [300, 1]. The value of the key \"pred_boxes\" is a tensor with shape [300, 4]
\nOutput Parameters: Two-Dimensional (2D)
\nOther Properties Related to Output: The values of the key \"pred_boxes\" are in range [0, 1], which represents normalized coordinates in format [center_x, center_y, width, height] relative to the input image size.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration:

Runtime Engine(s):

[Holoscan SDK 2.9.0]

Supported Hardware Microarchitecture Compatibility:

[NVIDIA Ampere]
[NVIDIA Hopper]
[NVIDIA Lovelace]
[NVIDIA Volta]

[Preferred/Supported] Operating System(s):

[Linux]

Model Version(s):

rtdetrv2_timm_r50_nvimagenet_pretrained_neg_finetune_bhwc

Training, Testing, and Evaluation Datasets:

The total size: 60 recordings
\nTotal number of datasets: 1
\nDataset partition: Training 66%, testing 14%, validation 20%

Training Dataset:

Link: https://plus.figshare.com/articles/media/REAL-colon_dataset/22202866
\nData Collection Method by dataset

[Human]

Labeling Method by dataset

[Human]

Properties: 40 recordings of real-world colonoscopies.

Testing Dataset:

Link: https://plus.figshare.com/articles/media/REAL-colon_dataset/22202866
\nData Collection Method by dataset

[Human]

Labeling Method by dataset

[Human]

Properties : 12 recordings of real-world colonoscopies.

Evaluation Dataset:

Link: https://plus.figshare.com/articles/media/REAL-colon_dataset/22202866
\nBenchmark Score
\nmAP@0.5:0.95: 0.301

Data Collection Method by dataset

[Human]

Labeling Method by dataset

[Human]

Properties: 8 recordings of real-world colonoscopies.

Inference:

Acceleration Engine: Tensor(RT)
\nTest Hardware:

V100
A100
H100
RTX 6000 Ada

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\nUsers are responsible for ensuring the model-generated segmentations are appropriately evaluated and comply with applicable safety regulations and ethical standards. Please make sure you have proper rights and permissions for all input images, particularly for personal health information.

For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.

Please report security vulnerabilities or NVIDIA AI Concerns here.

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\nPolyp Detection RT-DETR is a RT-DETR model that is designed to detect polyps in colonoscopy images.\nThis model is ready for commercial use.\n \n### License/Terms of Use:
\nGOVERNING TERMS: Use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: [Apache License Version 2.0](https://github.com/lyuwenyu/RT-DETR/blob/main/LICENSE).\nYou are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.
\n \n## Reference(s):\nThe model is trained on the [REAL-Colon dataset](https://github.com/cosmoimd/real-colon-dataset) [1], utilizing the [RT-DETR v2](https://github.com/lyuwenyu/RT-DETR/tree/main/rtdetrv2_pytorch) architecture [2] and the [ResNet50](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/resnet.py) backbone [3]. The backbone is pretrained on the NVImageNet dataset.\n \n> [1] Biffi, Carlo, et al. \"REAL-Colon: A dataset for developing real-world AI applications in colonoscopy.\" Scientific Data 11.1 (2024): 539.\n \n> [2] Lv, Wenyu, et al. \"Rt-detrv2: Improved baseline with bag-of-freebies for real-time detection transformer.\" arXiv preprint arXiv:2407.17140 (2024).\n \n> [3] He, Kaiming, et al. \"Deep residual learning for image recognition.\" Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
\n \n## Model Architecture:
\n**Architecture Type:** Convolution Neural Network (CNN), Transformer
\n**Network Architecture:** RT DETR V2 with ResNet50 backbone
\n\n## Input:
\n**Input Type(s):** Image
\n**Input Format(s):** Red, Green, Blue (RGB)
\n**Input Parameters:** Two-Dimensional (2D)
\n**Other Properties Related to Input:** Image Range Needed (640 x 640 x 3), Pre-Processing Needed (value range [0, 255])
\n \n## Output:
\n**Output Type(s):** A dictionary which contains two keys: \"pred_logits\" and \"pred_boxes\"
\n**Output Format:** The value of key \"pred_logits\" is a tensor with shape [300, 1]. The value of the key \"pred_boxes\" is a tensor with shape [300, 4]
\n**Output Parameters:** Two-Dimensional (2D)
\n**Other Properties Related to Output:** The values of the key \"pred_boxes\" are in range [0, 1], which represents normalized coordinates in format [center_x, center_y, width, height] relative to the input image size.
\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
\n \n## Software Integration:
\n**Runtime Engine(s):**
\n* [Holoscan SDK 2.9.0]
\n \n**Supported Hardware Microarchitecture Compatibility:**
\n* [NVIDIA Ampere]
\n* [NVIDIA Hopper]
\n* [NVIDIA Lovelace]
\n* [NVIDIA Volta]
\n \n**[Preferred/Supported] Operating System(s):**
\n* [Linux]
\n\n## Model Version(s):\nrtdetrv2_timm_r50_nvimagenet_pretrained_neg_finetune_bhwc
\n \n# Training, Testing, and Evaluation Datasets:
\n \n**The total size:** 60 recordings
\n**Total number of datasets:** 1
\n**Dataset partition:** Training 66%, testing 14%, validation 20%
\n\n## Training Dataset: \n**Link:** https://plus.figshare.com/articles/media/REAL-colon_dataset/22202866
\n**Data Collection Method by dataset**
\n* [Human]
\n\n**Labeling Method by dataset**
\n* [Human]
\n\n**Properties:** 40 recordings of real-world colonoscopies.
\n\n ## Testing Dataset:\n**Link:** https://plus.figshare.com/articles/media/REAL-colon_dataset/22202866
\n**Data Collection Method by dataset**
\n* [Human]
\n\n**Labeling Method by dataset**
\n* [Human]
\n\n**Properties :** 12 recordings of real-world colonoscopies.
\n\n## Evaluation Dataset:\n**Link:** https://plus.figshare.com/articles/media/REAL-colon_dataset/22202866
\n**Benchmark Score**
\n mAP@0.5:0.95: 0.301\n\n**Data Collection Method by dataset**
\n* [Human]
\n\n**Labeling Method by dataset**
\n* [Human]
\n\n**Properties:** 8 recordings of real-world colonoscopies.
\n \n## Inference:\n**Acceleration Engine:** Tensor(RT)
\n**Test Hardware:**
\n* V100
\n* A100
\n* H100
\n* RTX 6000 Ada
\n\n## Ethical Considerations:\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \nUsers are responsible for ensuring the model-generated segmentations are appropriately evaluated and comply with applicable safety regulations and ethical standards. Please make sure you have proper rights and permissions for all input images, particularly for personal health information.
\n\nFor more detailed information on ethical considerations for this model, please see the Model Card++ [Explainability](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/holoscan/models/polyp_detection_rt_detr_model/explainability), [Bias](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/holoscan/models/polyp_detection_rt_detr_model/bias), [Safety & Security](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/holoscan/models/polyp_detection_rt_detr_model/safety-and-security), and [Privacy](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/holoscan/models/polyp_detection_rt_detr_model/privacy) Subcards.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","Polyp Detection RT DETR Model",["D",1749728201191],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/clara-holoscan/models/polyp_detection_rt_detr_model","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","| Field | Response |\n|-----------------------------------------------------------------------------------------------------|------------------|\n| Participation considerations from adversely impacted groups [(protected classes)](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None |\n| Measures taken to mitigate against unwanted bias: | None |","explainability","| Field | Response |\n|--------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|\n| Intended Domain: | Polyp Detection in Colonoscopy Images |\n| Model Type: | Object Detection |\n| Intended Users: | Intended for users that use models for polyp detection tasks. |\n| Output: | A dictionary which contains two keys: \"pred_logits\" and \"pred_boxes\". |\n| Describe how the model works: | The model processes a colonoscopy image to output predicted boxes around detected polyps and their associated logits. |\n| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable |\n| Technical Limitations & Mitigation: | Limitation: This model's detection performance may not be 100% accurate, particularly in instances with variable lighting conditions, motion blur from camera movement and different video resolutions. Mitigation: The model can be finetuned to adapt to local imaging conditions for better performance. |\n| Verified to have met prescribed NVIDIA quality standards: | Yes |\n| Performance Metrics: | Mean Average Precision |\n| Potential Known Risks: | The model may result in missed polyps (false negatives) or incorrect detections (false positive) in clinical scenarios. This model is only intended to support diagnostic purposes and should serve only as a supplementary screening aid to assist healthcare professionals in identifying areas of interest during colonoscopy procedures. |\n| Licensing: | GOVERNING TERMS: Use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: [Apache License Version 2.0](https://github.com/lyuwenyu/RT-DETR/blob/main/LICENSE). |","privacy","| Field | Response |\n|------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------|\n| Generatable or reverse engineerable personal data? | None |\n| Personal data used to create this model? | None |\n| How often is dataset reviewed? | Before Release |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | Not Applicable |\n| Applicable Privacy Policy | [https://www.nvidia.com/en-us/about-nvidia/privacy-policy/](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) |","safetyAndSecurity","| Field | Response |\n|-----------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------|\n| Model Application(s): | Object Detection for Polyp in Colonoscopy Images |\n| Model Application Field(s): | Medical Devices |\n| Describe the life critical impact (if present). | The model is only intended to support diagnostic purposes. |\n| Use Case Restrictions: | Abide by [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: [Apache License Version 2.0](https://github.com/lyuwenyu/RT-DETR/blob/main/LICENSE). |\n|Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. 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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.",4,[584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608],[555,556,557,558,559],{"_393":468,"_204":468,"_164":165,"_367":579},{"_393":442,"_204":442,"_164":165,"_367":574},{"_393":569,"_204":569,"_164":165,"_367":570},{"_393":564,"_204":564,"_164":165,"_367":565},{"_393":560,"_204":560,"_164":165,"_367":561},"nvidia.partners",[562],{"_77":563,"_399":560,"_400":30,"_164":436},"edge","app.hushly.com",[566],{"_77":567,"_399":564,"_400":30,"_164":568},"*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",[571],{"_77":572,"_399":569,"_400":30,"_164":573},"_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.",[667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695],[647,648,649,650],{"_393":662,"_204":662,"_164":165,"_367":663},{"_393":657,"_204":657,"_164":165,"_367":658},{"_393":455,"_204":455,"_164":165,"_367":654},{"_393":569,"_204":569,"_164":165,"_367":651},[652],{"_77":653,"_399":569,"_400":85,"_164":573},"_gh_sess",[655],{"_77":656,"_399":455,"_400":85,"_164":462},"YSC","bizible.com",[659],{"_77":660,"_399":657,"_400":30,"_164":661},"_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",[664],{"_77":665,"_399":662,"_400":30,"_164":666},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_77":737,"_399":514,"_400":30,"_164":738},{"_77":722,"_399":736,"_400":85,"_164":723},{"_77":705,"_399":517,"_400":30,"_164":630},{"_77":705,"_399":618,"_400":30,"_164":630},{"_77":734,"_399":514,"_400":30,"_164":735},{"_77":722,"_399":520,"_400":85,"_164":723},{"_77":732,"_399":514,"_400":30,"_164":733},{"_77":722,"_399":517,"_400":85,"_164":723},{"_77":730,"_399":617,"_400":30,"_164":731},{"_77":722,"_399":639,"_400":85,"_164":723},{"_77":702,"_399":517,"_400":30,"_164":704},{"_77":728,"_399":514,"_400":85,"_164":729},{"_77":726,"_399":514,"_400":30,"_164":727},{"_77":724,"_399":514,"_400":85,"_164":725},{"_77":722,"_399":613,"_400":85,"_164":723},{"_77":720,"_399":514,"_400":30,"_164":721},{"_77":718,"_399":514,"_400":30,"_164":719},{"_77":716,"_399":514,"_400":85,"_164":717},{"_77":714,"_399":622,"_400":30,"_164":715},{"_77":702,"_399":613,"_400":30,"_164":704},{"_77":712,"_399":514,"_400":30,"_164":713},{"_77":710,"_399":514,"_400":85,"_164":711},{"_77":708,"_399":514,"_400":30,"_164":709},{"_77":706,"_399":514,"_400":30,"_164":707},{"_77":705,"_399":514,"_400":30,"_164":630},{"_77":702,"_399":703,"_400":30,"_164":704},{"_77":700,"_399":514,"_400":30,"_164":701},{"_77":698,"_399":514,"_400":85,"_164":699},{"_77":696,"_399":514,"_400":30,"_164":697},"_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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