Model for HoloHub Sample App for MONAI Endoscopic Tool Segmentation

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

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.

Inputs

INPUT__0 - Input RGB image (batchsize, height, width, channels)\n
- shape=[1, 480, 736, 3]
- dtype=float32
\n

Outputs

OUTPUT__0 - Segmentation output with two channels.\n
- shape=[1, 2, 480, 736]
- dtype=float32
\n

References

[1] Tan, M. and Le, Q. V. Efficientnet: Rethinking model scaling for convolutional neural networks. ICML, 2019a. https://arxiv.org/pdf/1905.11946.pdf

[2] 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

License

Refer to the MONAI Endoscopic Tool Segmentation Model License as well as the data license for the data used in training this model.

Copyright (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.\n

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For details on this model, please see [MONAI GitHub repository](https://github.com/Project-MONAI/model-zoo/tree/dev/models/endoscopic_tool_segmentation). 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.\nGo to the [HoloHub repository](https://github.com/nvidia-holoscan/holohub) to find the sample app that utilizes this model.\n\n## Model\n\nGiven 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.\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\n\n- `INPUT__0` - Input RGB image (batchsize, height, width, channels)\n - `shape=[1, 480, 736, 3]`\n - `dtype=float32`\n\n### Outputs\n\n- `OUTPUT__0` - Segmentation output with two channels.\n - `shape=[1, 2, 480, 736]`\n - `dtype=float32`\n\n### References\n[1] Tan, M. and Le, Q. V. Efficientnet: Rethinking model scaling for convolutional neural networks. ICML, 2019a. https://arxiv.org/pdf/1905.11946.pdf\n\n[2] 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. 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