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. The 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.
There are two ONNX models provided in this resource:
as well as the original PyTorch model
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.
INPUT__0- Input RGB image (batchsize, height, width, channels)
shape=[1, 300, 300, 3]
OUTPUT__LOC- location of bounding box candidates.
shape=[batch_size, 4, 8732]
shape=[batch_size, 81, 8732]
shape=[batch_size, 20, 4]
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.
This model is meant to be run on the video in Holoscan Endoscopy Sample Data.
Refer to the file NVIDIA Clara Holoscan SSD Evaluation License (28Feb2023) in the downloaded files.