This GitHub repository presents a cutting-edge computer vision project aimed at automating the identification and classification of chess pieces on a chessboard. Leveraging the state-of-the-art Vision Transformer (ViT) model, this innovative solution offers a novel approach to understanding and processing chess imagery.
This public dataset of chessboard images encompasses various board configurations and lighting conditions, ensuring robustness and accuracy in the classification process. The Vision Transformer model, renowned for its exceptional capabilities in handling large-scale image data, has been fine-tuned specifically for this chess piece recognition task, yielding remarkable results.
Key Features:
Utilization of Vision Transformer (ViT) model for image classification. A comprehensive dataset of diverse chessboard images. Extensive fine-tuning for precise and reliable piece recognition. Efficient and scalable solution for real-time applications. Step-by-step instructions to reproduce the training process. Pre-trained models for quick deployment in your projects. Whether you are an enthusiast in computer vision, an AI researcher, or a chess enthusiast seeking automation in chessboard analysis, this repository serves as a valuable resource to explore and implement a cutting-edge chess piece classification system powered by Vision Transformer technology. Join us in advancing the realm of computer vision and revolutionizing how chess pieces are identified and interpreted.