HoloHub Sample App Model for SSD Detection for Endoscopy Surgical Tools

This resource contains a SSD Detection model for the identification of surgical tools, meant to be run on the video in Holoscan Endoscopy Sample Data.\nThe SSD model is from the NVIDIA DeepLearningExamples repository.

Go to the HoloHub repository to find the sample app that utilizes this model.

The data used to create the SSD model is derived from the model and video data in Holoscan Endoscopy Sample Data, which are kindly provided by Research Group Camma, IHU Strasbourg and University of Strasbourg.

Model

There are two ONNX models provided in this resource:

epoch24.onnx
epoch24_nms.onnx

as well as the original PyTorch model epoch_24.pt.

Please see the model conversion process from PyTorch epoch_24.pt to the two ONNX models, the addition of Non Maximum Suppression in epoch24_nms.onnx compared to epoch24.onnx, and description of the models in the Model Conversion to ONNX section in the application README on HoloHub.

Note: The provided model is in ONNX format. It will automatically be converted into a TensorRT model (.engine) the first time it is processed by a Holoscan application.

Inputs

For both epoch24.onnx and epoch24_nms.onnx

INPUT__0 - Input RGB image (batchsize, height, width, channels)\n
- shape=[1, 300, 300, 3]
- dtype=float32
- range=[-1, 1]
\n

Outputs

For epoch24.onnx

OUTPUT__LOC - location of bounding box candidates.\n
- shape=[batch_size, 4, 8732]
- dtype=float32
\n
OUTPUT__LABEL - scores of labels for locations of bounding box candidates.\n
- shape=[batch_size, 81, 8732]
- dtype=float32
\n

For epoch24_nms.onnx

num_detections - number of valid detections.\n
- shape=[batch_size, 1]
- dtype=int32
\n
detection_boxes - coordinates of non-max suppressed boxes, here 20 is max number of output boxes.\n
- shape=[batch_size, 20, 4]
- dtype=float32
\n
detection_scores - the scores for the boxes.\n
- shape=[batch_size, 20]
- dtype=float32
\n
detection_classes - the classes for the boxes.\n
- shape=[batch_size, 20]
- dtype=int32
\n

The NMS layer in epoch24_nms.onnx is EfficientNMS_TRT. See the Model Conversion to ONNX section in the application README for details on how we utilize it.

Video Data

This model is meant to be run on the video in Holoscan Endoscopy Sample Data.

License

Refer to the file NVIDIA Clara Holoscan SSD Evaluation License (28Feb2023) in the downloaded files.

","resourceVersions",[227,228],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_210":28,"_211":-5,"_52":116,"_113":-5,"_117":212,"_119":-5,"_213":115,"_124":62,"_121":34,"_214":-5,"_120":34,"_215":-5,"_216":34,"_217":218,"_219":34,"_220":218,"_221":34,"_222":218,"_223":34,"_224":218,"_225":34,"_122":226},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/clara-holoscan/resources/ssd_surgical_tool_detection_model/-","-","imageReference","cliDownloadCommand","ngc registry resource download-version nvidia/clara-holoscan/ssd_surgical_tool_detection_model:v0.1","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/clara-holoscan/resources/ssd_surgical_tool_detection_model/-/version-history","accessCheckResult",{"_209":90},"hasRelatedCollections","context",{"_205":206,"_207":208},1787622052960,[],[],[198,199,200,201,202,203,204],{"_48":63,"_50":73,"_52":65,"_54":66,"_56":74,"_58":59},{"_48":63,"_50":72,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":65,"_54":66,"_56":71,"_58":59},{"_48":63,"_50":68,"_52":65,"_54":66,"_56":69,"_58":59},{"_48":63,"_50":64,"_52":65,"_54":66,"_56":67,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":62,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},"orgName","nvidia","teamName","clara-holoscan","allowed","versionId","description",["D",1679288072921],"totalSizeInBytes","malwareScanStatus","releaseNotes","hasReleaseNotes","setup","","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance",[],{"_85":182,"_112":230,"_113":-5,"_121":34,"_120":34,"_122":229,"_216":34},{"_85":28,"_112":28,"_113":-5,"_121":34,"_120":34,"_122":229,"_216":34},[],"Latest (v0.1)","nvidia/clara-holoscan/ssd_surgical_tool_detection_model","key","nvidia/clara-holoscan/resources/ssd_surgical_tool_detection_model","resourceId","ssd_surgical_tool_detection_model","shortDescription","A SSD Detection model for Endoscopy Surgical Tools","# HoloHub Sample App Model for SSD Detection for Endoscopy Surgical Tools\n\nThis resource contains a SSD Detection model for the identification of surgical tools, meant to be run on the video in [Holoscan Endoscopy Sample Data](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara-holoscan/resources/holoscan_endoscopy_sample_data).\nThe SSD model is from the [NVIDIA DeepLearningExamples](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Detection/SSD) repository. \n\nGo to the [HoloHub](https://github.com/nvidia-holoscan/holohub) repository to find the sample app that utilizes this model.\n\nThe data used to create the SSD model is derived from the model and video data in [Holoscan Endoscopy Sample Data](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara-holoscan/resources/holoscan_endoscopy_sample_data), which are kindly provided by [Research Group Camma](http://camma.u-strasbg.fr/), [IHU Strasbourg](https://www.ihu-strasbourg.eu/) and [University of Strasbourg](https://en.unistra.fr/).\n\n## Model\n\nThere are two ONNX models provided in this resource:\n - `epoch24.onnx`\n - `epoch24_nms.onnx`\n\nas well as the original PyTorch model `epoch_24.pt`. \n\nPlease see the model conversion process from PyTorch `epoch_24.pt` to the two ONNX models, the addition of Non Maximum Suppression in `epoch24_nms.onnx` compared to `epoch24.onnx`, and description of the models in the [Model Conversion to ONNX section in the application README](https://github.com/nvidia-holoscan/holohub/tree/main/applications/ssd_detection_endoscopy_tools#model-conversion-to-onnx) on HoloHub.\n\n_Note: The provided model is in ONNX format. It will automatically be converted into a TensorRT model (.engine) the first time it is processed by a Holoscan application._\n\n### Inputs\nFor both `epoch24.onnx` and `epoch24_nms.onnx`\n- `INPUT__0` - Input RGB image (batchsize, height, width, channels)\n - `shape=[1, 300, 300, 3]`\n - `dtype=float32`\n - `range=[-1, 1]`\n\n### Outputs\n\nFor `epoch24.onnx` \n- `OUTPUT__LOC` - location of bounding box candidates.\n - `shape=[batch_size, 4, 8732]`\n - `dtype=float32`\n- OUTPUT__LABEL - scores of labels for locations of bounding box candidates.\n - `shape=[batch_size, 81, 8732]`\n - `dtype=float32`\n \n\nFor `epoch24_nms.onnx`\n- num_detections - number of valid detections.\n - `shape=[batch_size, 1]`\n - `dtype=int32`\n- detection_boxes - coordinates of non-max suppressed boxes, here 20 is max number of output boxes.\n - `shape=[batch_size, 20, 4]`\n - `dtype=float32`\n- detection_scores - the scores for the boxes.\n - `shape=[batch_size, 20]`\n - `dtype=float32`\n- detection_classes - the classes for the boxes.\n - `shape=[batch_size, 20]`\n - `dtype=int32`\n\nThe NMS layer in `epoch24_nms.onnx` is [EfficientNMS_TRT](https://github.com/NVIDIA/TensorRT/tree/main/plugin/efficientNMSPlugin). See the [Model Conversion to ONNX section in the application README](https://github.com/nvidia-holoscan/holohub/tree/main/applications/ssd_detection_endoscopy_tools#model-conversion-to-onnx) for details on how we utilize it.\n## Video Data\n\nThis model is meant to be run on the video in [Holoscan Endoscopy Sample Data](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara-holoscan/resources/holoscan_endoscopy_sample_data).\n\n## License\n\nRefer to the file NVIDIA Clara Holoscan SSD Evaluation License (28Feb2023) in the downloaded files.","displayName","SSD Surgical Tool Detection Model",["D",1681842264233],"publisher","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/clara-holoscan/resources/ssd_surgical_tool_detection_model","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_151":1245,"_153":1246},"ngcApps",[1198],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1179":1180,"_1181":1182,"_1183":1184,"_1185":1186,"_1187":279,"_109":305,"_1188":1189,"_1190":59,"_1191":90,"_1192":90,"_1193":1194,"_1195":90,"_1196":1197},"amplitude",{"_1176":1177,"_817":-7,"_1178":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1170":1171,"_1172":1173,"_1174":1175},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787622052863,"1.12.0","defaultSearchSuggestions",{"_1057":218,"_1058":1059,"_1060":1061,"_1062":1063},"announcements",[],"consentState",{"_342":1047,"_1048":90,"_1049":34,"_1050":34,"_1051":1052},"consentConfig",{"_324":325,"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341,"_342":343},"showContactAdmin",1787622052864,[],[],[319,320,321,322,323],{"_48":63,"_50":73,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":62,"_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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