Structure-aware and Texture-aware End-to-end Network

This code can be used to reproduce the experiments performed in our paper 'SaTaNet: Structure-aware and Texture-aware End-to-end Network for Cephalometric Landmark Localization on A New Large Dataset'.

Requirements

• Python 3 (code has been tested on Python 3.7)
• CUDA and cuDNN (tested with Cuda 11.3)
• Our experiments used a NVIDIA 3090 which has 32GBs of memory.
• Python packages listed in the requirements.txt including PyTorch 1.10.0

Set up

To set up environment, please run the following command:

python3 -m pip install -r requirements.txt

Run train

To train the model, please run the following command:

CUDA_VISIBLE_DEVICES=XXX python train.py --cfg experiments/cephalometric.yaml --training_images /path/to/training/data/ \
 --annotations /path/to/annotation/data/

Run test

To test the model, please run the following command:

python temperature_scaling.py --cfg experiments/cephalometric.yaml --fine_tuning_images /data1/wangs/datasets/ISBI2015/CHHeatmaps_process/data/RawImage/Test1Data/ \
 --annotations /path/to/annotation/data/ --pretrained_model /path/to/cephalometric_model.pth