The deep learning model to classify chess pieces using CNN (Convolutional Neural Network) and transfer learning is a state-of-the-art solution for automatically recognizing and classifying different chess pieces.
Transfer learning helps to leverage the pre-existing knowledge of the CNN to improve the performance of the model on the specific task of chess piece classification. This approach enables the model to learn meaningful representations of the input data, and identify features that are highly indicative of a particular chess piece. The model can classify chess pieces with high accuracy, even when the pieces are partially obscured or occluded by other pieces.
Overall, this deep learning model provides a fast and accurate solution for recognizing and classifying chess pieces, which can be used in various applications such as chess game analysis, chess puzzle solving, and automated chess game-playing. The model is available for use on various platforms and can be trained on different datasets to improve its performance.
The model unfortunately was trained on an extremely noisy dataset hindering its accuracy.