Model Card for BoardCNN
BoardCNN implements a Convolutional Neural Network (CNN) to recognize the position from images of chess boards.
The model expects a board image as input and returns the expected positions of the pieces on the board.
Model Details
Custom CNN architecture was implemented via pytorch
Developed by: Igor Alexey
Model type: Safetensors
License: GNU GPL v3
Model Sources
- Repository: [More Information Needed]
- Demo: [More Information Needed]
Uses
The model can be used to make predictions on new chess board images. The output is a 8x8 grid of chess piece symbols, representing the predicted position of pieces on the board.
Out-of-Scope Use
The pre-trained models are not made for scanning 3D boards, although it's likely the architecture should scale well for this task with a proper training set.
Limitations
Might not always give 100% correct output, especially on varying piece sets and board themes.
Getting started
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
The models are trained on 5k gnerated images of valid random board positions with reasonable piece sets from lichess.
Training Procedure
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
