Description:

To build a model capable of generating FEN (Forsyth-Edwards Notation) descriptions based on schematic images of a chessboard, we need to outline a comprehensive project plan that includes several key components:

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Content:

Images: 100,000 images of randomly generated chess positions with 5-15 pieces (2 kings and 3-13 pawns/pieces).

Generated using 28 styles of chess boards and 32 styles of chess pieces, totaling 896 board/piece style combinations.

All images are 400×400 pixels.

Training set: 80,000 images

Test set: 20,000 images

Piece Distribution:

Pawn: 30%

Bishop: 20%

Knight: 20%

Rook: 20%

Queen: 10%

2 Kings are always present on the board.

Annotations: Each image is annotated with the corresponding FEN description to facilitate supervised learning.

Variability: The dataset includes a wide variety of board and piece styles to ensure robust model training across different visual representations.

Applications: This dataset is ideal for training computer vision models to accurately recognize and classify various chess positions. By using advanced image processing and machine learning techniques, these models can analyze board configurations, identify pieces

Tools: Images were generated using a custom-built tool, ensuring consistency and quality in the dataset.

This dataset is sourced from Kaggle.