readme update

README.md

@@ -2,54 +2,89 @@
 
 Czytnik Polskich Banknotów is an Open Source mobile app, which goal is to help visually handicapped people live the life we all do. By detecting our local currency's denomination (PLN - Polish zloty) using neural networks, it allows a user to know what banknote they're holding. The application provides support on a daily basis, such as going shopping, using ATMs, buying tickets or visiting restaurants.
 
-At [Play Store](https://play.google.com/store/apps/details?id=pg.eti.project.polishbanknotes)
+## Download
+
+Aviable at [Play Store](https://play.google.com/store/apps/details?id=pg.eti.project.polishbanknotes)
+
+Not currently aviable at App Store.
 
 ## About technologies
+
+Short description of technologies used in this project.
+
 ### Mobile app
+
 We're developing two versions of this application - one for Android and one for iOS. Both of them are written natively using corresponding technologies - Kotlin for Android and Swift for iOS. Currently, supported system versions are:
+
 * iOS 11.0+ (iPhone 5s+)
 * Android 7+
 
 ### Neural network
-The neural network we're working on is based on TensorFlow+Keras Image classification. In future, we are planning using Object Classification for detecting coins.
-
-### Datasets
-We're using datasets created by previous teams working on this subject (information about those are given in *Acknowledgments* section). About numbers, we currently have around 3500-4800 images per denomination, mostly in good quality with 0 or 1 banknote at it.
-
-[comment]: <> (An in-depth paragraph about your project and overview of use.)
 
-## Getting Started
+The neural network we're working on is based on TensorFlow+Keras Image classification. We used yolo architecture to train our best model - created by Team Android.
 
-### Installing
+### Datasets
 
-[comment]: <> (* How/where to download your program * Any modifications needed to be made to files/folders)
+We're using datasets created by previous teams working on this subject (information about those are given in *Acknowledgments* section). About numbers, we currently have around 3500-4800 images per denomination, mostly in good quality with 0 or 1 banknote at it.
 
 ## Help
 
-[comment]: <> (Any advise for common problems or issues.)
+For help, please contact authors of this project or create an issue on GitHub.
 
 ## Authors
 
-Team iOS
+### Team iOS
+
 * [Jakub Dajczak](https://github.com/qaziok)
 * [Miłosz Chojnacki](https://github.com/Buzeqq)
 * [Anton Delinac](https://github.com/anton-0)
 * [Stanisław Smykowski](https://github.com/StanislawSm)
 * [Mateusz Sowiński](https://github.com/wichurax)
 
-Team Android
+### Team Android
+
 * [Krzysztof Jędraszek](https://github.com/kjedrasz2137)
 * [Jakub Szymański](https://github.com/Corax0x01)
 * [Filip Żemajtys](https://github.com/fzemi)
 * [Jan Kornacki](https://github.com/jankejc)
 * [Mikołaj Wirkijowski](https://github.com/mikolajwirkijowski97)
 
-## Version History
+## Acknowledgments
 
-## License
+### Supervisors
 
-This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details
+[Jan Cychnerski](https://github.com/jachoo)
 
-## Acknowledgments
+### Previous teams working on this subject
+
+#### [Polish Banknote Recognition Application](https://github.com/bartekkkkk/PG2020)
+
+- Jacek Ardanowski
+- Bartłomiej Gawrych
+- Adam Grabowski
+- Kamil Jabłoński
+- Bartosz Kuncer
+
+#### [Vison Project](https://github.com/theATM/Vison-Project-Repository)
+
+- Aleksander Madajczak
+- Kamil Pokornicki
+- Karol Dziki
+
+### Datasets
+
+We were using datasets created by previous teams working on this subject.
+
+[Polish banknotes / Polskie banknoty](https://www.kaggle.com/datasets/bartomiejgawrych/polish-banknotes-polskie-banknoty)
+
+[VisonData PLN banknotes 2021](https://www.kaggle.com/datasets/99636797e98397fa6161113867b50bed663be07900885852cda713b1cc76e52d)
+
+### Training resources
+
+Thanks to kindness of Ph.D. Jacek Rumiński from Biomedical Engineering Department, we were able to use DGX Station, which is a powerful machine learning server, to train our neural network.
+
+This work has been partially supported by Statutory Funds of Electronics, Telecommunications and Informatics Faculty, Gdańsk University of Technology.
+
+## License
 
-[comment]: <> (Inspiration, code snippets, etc.)
+This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details