Updated Python setup with compatibility information.

Author: goliasm

README.md

@@ -13,6 +13,8 @@ We adapted the FIVES retinal vessel segmentation dataset ([https://doi.org/10.10
 
 ## Python setup
 
+### Development environment
+
 The project was fully developed in **Python 3.10.11** with **Tensorflow** version **2.10.0** for neural network optimisation. We used the standard packages written in the following list:
 
 - numpy
@@ -25,8 +27,21 @@ The project was fully developed in **Python 3.10.11** with **Tensorflow** versio
 
 The file *requirements.txt* contains a list of these packages for easier installation with pip. In addition, we provide *requirements_frozen.txt*, which contains the exact versions of all packages installed in our project environment during verification of the code in this repository.
 
+### Testing system
+
 The project was developed and tested on Windows 10 22H2, AMD64 architecture with native Python installation. Specifically, the networks were trained on Intel(R) Core(TM) i7-8750H CPU @ 2.20 GHz system with NVIDIA GeForce GTX 1060 (6 GB) GPU.
 
+### Compatibility update (30. 1. 2025)
+
+The source code was updated, and some bugs were fixed to make the project compatible with **Python 3.12.8** and **Tensorflow 2.18.0**. This update necessitated changing the format of saved network weights to **.h5**. The re-saved weights are available as a secondary release. A script `resave.py`, which can be used to re-save network weights was added. Note that when loading models (or training them), the names of the weight files are hard-coded in `rbv_sin/nn/inp_trainer.py` and `rbv_sin/nn/seg_trainer.py`. These names must be changed to something ending with `.weights.h5` to train with **Tensorflow 2.18.0**. 
+
+Compatibility with the versions used in development was preserved, and this update was tested with the following:
+
+- Windows Native (GPU), Python 3.10.11, Tensorflow 2.10.0
+- Windows Native (CPU), Python 3.12.8, Tensorflow 2.18.0
+- Windows WSL Ubuntu 20.04 (CPU), Python 3.10.16, Tensorflow 2.10.0
+- Windows WSL Ubuntu 20.04 (CPU), Python 3.12.8, Tensorflow 2.18.0
+
 ## Data preprocessing
 
 Trying out our method on images using the testing scripts doesn't require specific data preprocessing, however, if one wants to train using our code or evaluate on an entire set of images at once then it is necessary to prepare the data in the expected format. This can be done with the script **data_process.py**. The script has the following arguments: