The Chess Recognition Dataset (ChessReD) comprises a diverse collection of images of chess formations captured using smartphone cameras; a sensor choice made to ensure real-world applicability. The dataset is accompanied by detailed annotations providing information about the chess pieces formation in the images. Therefore, the number of annotations for each image depends on the number of chess pieces depicted in it. There are 12 category ids in total (i.e., 6 piece types per colour) and the chessboard coordinates are in the form of algebraic notation strings (e.g., "a8").

Dataset specifications

The dataset consists of 100 chess games, each with an arbitrary number of moves and therefore images, amounting to a total of 10,800 images being collected. It was split into training, validation, and test sets following a 60/20/20 split, which led to a total of 6,479 training images, 2,192 validation images, and 2,129 test images. Since two consecutive images of a chess game differ only by one move, the split was performed on game-level to ensure that quite similar images would not end up in different sets. The split was also stratified over the three distinct smartphone cameras (Apple iPhone 12, Huawei P40 pro, Samsung Galaxy S8) that were used to capture the images. The three smartphone cameras introduced variations to the dataset based on the distinct characteristics of their sensors.