Offical Code for "Establishment of an artificial intelligence-assisted nasal endoscopic diagnostic system and its application in the identification and tracking of new nasal organisms"
This repository is based on PyTorch 1.12.0, CUDA 11.３, and Python 3.９.７. All experiments in our paper were conducted on NVIDIA GeForce RTX 3090 GPU with an identical experimental setting.
We provide training code, testing code, and pretrained model will release as soon as possible.
organize the dataset and create the dict .json file, like this format: {"class 1": {"train": [file1, file2...], "val":[...], "test":[...]}, "class 2": {...}}.
To train a model,
python train.py --root_path 'your image files path' -dev 'cuda:0' -b 32 -l 0.001 -name 'baseline_XXXX' -e 300
under reviewer
Some modules in our code were inspired by Monai and segmentation_models.pytorch. We appreciate the effort of these authors to provide open-source code for the community. Hope our work can also contribute to related research.