This resource contains a MONAI endoscopic surgical tool segmentation model. For details on this model, please see MONAI GitHub repository. We convert the PyTorch model from MONAI to ONNX, and deploy the ONNX model in a HoloHub application, where at the first app runtime it will be converted to a TensorRT engine. Go to the HoloHub repository to find the sample app that utilizes this model.
Given an RGB image of
480 x 736, this model provides semantic segmentation of surgical tools. Output channel 0 is tools and 1 is everything else.
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, 480, 736, 3]
OUTPUT__0- Segmentation output with two channels.
shape=[1, 2, 480, 736]
 Tan, M. and Le, Q. V. Efficientnet: Rethinking model scaling for convolutional neural networks. ICML, 2019a. https://arxiv.org/pdf/1905.11946.pdf
 O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015. https://arxiv.org/pdf/1505.04597.pdf
Copyright (c) MONAI Consortium Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.