Update README with StrainNet application and retraining

BlytheDumerer · web-flow · commit a37c33e0092e · 2026-04-14T10:57:29.000-07:00
Added sections for applying StrainNet to experimental images and retraining with experimental data.
diff --git a/README.md b/README.md
@@ -22,6 +22,8 @@ StrainNet is a deep learning based method for predicting strain from images
 - [Applying StrainNet to experimental images](#applying-strainnet-to-experimental-images)
   - [Arguments](#arguments-2)
   - [Applying StrainNet to human flexor tendons *in vivo*](#applying-strainnet-to-human-flexor-tendons-in-vivo)
+- [Retraining StrainNet further with experimental images](#retraining-strainnet-further-with-experimental-images)
+    - [TBD](#tbd)
 - [Citation](#citation)
     - [TBD](#tbd)
 - [LICENSE](#license)
@@ -116,7 +118,7 @@ python train.py --help
 
 Or examine the [`core/arguments.py`](core/arguments.py) Python script. 
 
-## Resuming training
+## Resuming training on pre-trained models
 
 You can also resume training on models for StrainNet by specifying the `--resume` flag and the path to the pre-trained model. For example:
 
@@ -190,6 +192,41 @@ To run the `eval.sh` script, simply execute the following command from the termi
 . scripts/eval.sh
 ```
 
+# Applying StrainNet to experimental images
+
+To apply StrainNet to experimental images that do not have known strains, you can use the apply2experimental.py script. This script loads the trained StrainNet model and processes the experimental images to predict the strains.
+
+To apply StrainNet to experimental images, use the following command:
+
+```
+python apply2experimental.py --model path/to/trained/model --experimental-data path/to/experimental/data
+```
+Replace path/to/trained/model with the actual path to the trained StrainNet model, and path/to/experimental/data with the actual path to the experimental data.
+
+The apply2experimental.py script will output the predicted strains to the terminal and/or save them to a file, depending on the specified command line arguments.
+
+## Arguments
+
+You can see a list of all the available arguments for the apply2experimental.py script by using the --help flag:
+
+```
+python apply2experimental.py --help
+```
+Note: The apply2experimental.py script requires the experimental images to be in a specific format that is compatible with StrainNet.
+
+## Applying StrainNet to human flexor tendons in vivo
+
+To apply the pretrained models real experimental data, you can use the flexor_tendon.sh script. This script will invoke the necessary scripts and pass the appropriate arguments to them.
+
+To run the flexor_tendon.sh script, simply execute the following command from the terminal:
+
+```
+. scripts/flexor_tendon.sh
+```
+
+# Retraining StrainNet further with experimental images
+To train StrainNet further on experimental images
+
 # Citation
 
 ```
