This is a vision AI application through CNN (Convolutional Neural Network) that understands FEN positions of the given chess board by looking at it. One of the methods to solve solving this problem is to use a CNN that is fed by the chess board blocks divided into 64 squares represents alternative positions or pieces. In order to obtain the findings, we first encode the labels into classes, and then decoding the outcomes to recreate the images with expected FEN codes.
A common notation for defining a certain chess board position is the Forsyth-Edwards Notation (FEN). The goal of FEN is to offer all the information required to restart a game from a specific point. By the time of now, researchers who study machine learning have shown that it is possible to correctly guess a chess player's identity based on the sequence of positions from their chess games. The method utilized in the study has the potential to be widely used for identifying people based on a variety of activities.
CNN is a deep learning method that can take in an input picture, assign importance to various characteristics and objects in the image, and distinguish between them. In comparison to other classification methods, a convolutional network requires substantially less preprocessing. Convolutional networks can also learn these filters and attributes with enough training, unlike basic approaches where filters must be manually engineered.
pip install requirements.txtstreamlit run app.py --server.port 8501
This is a sample image of the application predicting the FEN of a chess board
P.S.: This application is has not been tested on platforms other than MacOS