Update README.md

README.md

@@ -2,4 +2,32 @@
 license: mit
 ---
 
-# source code of CPICANN
+# source code of CPICANN
+
+## Instructions for replication
+
+This directory contains all the source code needed to reproduce this work.
+
+### Data preparation
+
+To directly run the train and validation script in this directory, data preparation needs to be done. The [OneDrive link](https://hkustgz-my.sharepoint.com/:f:/g/personal/bcao686_connect_hkust-gz_edu_cn/EhdJLtou8I1MoUJCu-KCoboBfi-wOp00WAlQCrONxjoYgg?e=rltgFE) contains all the training and synthetic testing data used in this work, stored in data.zip. This link also contains the pretrained model for single-phase and di-phase identification.
+
+File single-phase_checkpoint_0200.pth and file bi-phase_checkpoint_2000.pth from the link above is the pretrained model, place them under directory "pretrained".
+
+File data.zip contains the data and the annotaion file. Place directory "train" and "val" from data.zip under directory "data", place the annotation files anno_train.csv and anno_val.csv under directory "annotation".
+
+### Model Trianing
+
+#### Single-phase
+
+Run ```python train_single-phase.py``` to train the single-phase identification model from scratch. To train the model on your data, addtional parameters need to be set: ```python train_single-phase.py --data_dir_train=[your training data] --data_dir_val=[your validation data] --anno_train=[your anno file for training data] --anno_val=[your anno file for validation data]```.
+
+#### Bi-phase
+
+Run ```python train_bi-phase.py``` to train the bi-phase identification model. The bi-phase identification model is trained based on single-phase model, you can change the default setting by set the parameter ```load_path=[your pretrained single-phase model]```.
+
+### Model validation
+
+Run ```python train_single-phase.py``` and ```python val_bi-phase.py``` to run the validation code at default setting.
+
+If you wish to validate the model on your data, plase format your data using data_format.py