SHPNeXT

SHPNeXT: Enhanced Tongue Image Segmentation Across Multi-scale and Variable Resolutions for Traditional Chinese Medicine

1.Preparation

The project requires Python 3.7+, CUDA 10.2+ and PyTorch 1.8+. Step 1. Create a conda environment and activate it.

shell conda create --name openmmlab python=3.8 -y conda activate openmmlab

Step 2. Install PyTorch following official instructions, e.g.

On GPU platforms:

shell conda install pytorch torchvision -c pytorch

On CPU platforms:

shell conda install pytorch torchvision cpuonly -c pytorch

Step 3. The method code proposed in this article are SHPNeXt_config.py, SHPNeXt_backbone.py, poolformer.py, hire-mlp.py and ham_head_nuclearnmf_decoder.py. Put the SHPNeXt_backbone.py, poolformer.py and hire-mlp.py into mmseg/models/backbones, put the ham_head_nuclearnmf_decoder.py into mmseg/models/decode_heads, and put the SHPNeXt_config.py into dir of config.

Step 4. Prepare the datasets. The two open source data sets BioHit and LRCM used in this article can download from the link Dataset. Due to privacy restrictions, we are unable to provide the HUCM data set.

2.Installation

Note: Our job based on the Project of mmsegmentation.We recommend the users install the relative packges.

Step 0. Install MMCV using MIM.

shell pip install -U openmim mim install mmengine mim install "mmcv>=2.0.0"

Step 1. Install MMSegmentation.

Case a: If you develop and run mmseg directly, install it from source:

```shell git clone -b main https://github.com/open-mmlab/mmsegmentation.git cd mmsegmentation pip install -v -e .

'-v' means verbose, or more output

'-e' means installing a project in editable mode,

thus any local modifications made to the code will take effect without reinstallation.

```

Case b: If you use mmsegmentation as a dependency or third-party package, install it with pip:

shell pip install "mmsegmentation>=1.0.0" Follow the mmsegmentation tutorial. Register and configure settings and you're ready to run.

4.Train

We provide tools/train.py to launch training jobs on a single GPU. You can train the all the models presented in the paper by use the following command.

shell python tools/train.py ${CONFIG_FILE} [optional arguments]

This tool accepts several optional arguments, including:

• --work-dir ${WORK_DIR}: Override the working directory.
• --amp: Use auto mixed precision training.
• --resume: Resume from the latest checkpoint in the work_dir automatically.
• --cfg-options ${OVERRIDE_CONFIGS}: Override some settings in the used config, and the key-value pair in xxx=yyy format will be merged into the config file.

For example, if you want train the SHPNeXt, run shell python tools/train.py configs/SHPNeXt_config.py --work-dir /path/your_choice

5.Test

You can directly use our trained model（Download the weights: SHPNext_BioHit, SHPNext_LRCM） files for quick testing, or use your trained model for testing. Use the following command:

shell python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]

This tool accepts several optional arguments, including:

• --work-dir: If specified, results will be saved in this directory. If not specified, the results will be automatically saved to work_dirs/{CONFIG_NAME}.
• --show: Show prediction results at runtime, available when --show-dir is not specified.
• --show-dir: Directory where painted images will be saved. If specified, the visualized segmentation mask will be saved to the work_dir/timestamp/show_dir.
• --wait-time: The interval of show (s), which takes effect when --show is activated. Default to 2.
• --cfg-options: If specified, the key-value pair in xxx=yyy format will be merged into the config file.
• --tta: Test time augmentation option.

For example, if you want test the SHPNeXt using our trained model, run shell python tools/train.py configs/SHPNeXt_config.py SHPNext_BioHit.pth

6.Contact

If you have any questions, please feel free to contact: pengchongxiaoKZ@163.com