End-to-End Chess Recognition

Chess recognition refers to the task of identifying the chess pieces configuration from a chessboard image. Contrary to the predominant approach that aims to solve this task through the pipeline of chessboard detection, square localization, and piece classification, we rely on the power of deep learning models and introduce two novel methodologies to circumvent this pipeline and directly predict the chessboard configuration from the entire image. In doing so, we avoid the inherent error accumulation of the sequential approaches and the need for intermediate annotations. Furthermore, we introduce a new dataset, Chess Recognition Dataset (ChessReD), specifically designed for chess recognition that consists of 10,800 images and their corresponding annotations. In contrast to existing synthetic datasets with limited angles, this dataset comprises a diverse collection of real images of chess formations captured from various angles using smartphone cameras; a sensor choice made to ensure real-world applicability. We use this dataset to both train our model and evaluate and compare its performance to that of the current state-of-the-art. Our approach in chess recognition on this new benchmark dataset outperforms related approaches, achieving a board recognition accuracy of 15.26% ($\approx$7x better than the current state-of-the-art).

Post-training Model Quantization Using GANs for Synthetic Data Generation

Quantization is a widely adopted technique for deep neural networks to reduce the memory and computational resources required. However, when quantized, most models would need a suitable calibration process to keep their performance intact, which requires data from the target domain, such as a fraction of the dataset used in model training and model validation (i.e. calibration dataset). In this study, we investigate the use of synthetic data as a substitute for the calibration with real data for the quantization method. We propose a data generation method based on Generative Adversarial Networks that are trained prior to the model quantization step. We compare the performance of models quantized using data generated by StyleGAN2-ADA and our pre-trained DiStyleGAN, with quantization using real data and an alternative data generation method based on fractal images. Overall, the results of our experiments demonstrate the potential of leveraging synthetic data for calibration during the quantization process. In our experiments, the percentage of accuracy degradation of the selected models was less than 0.6%, with our best performance achieved on MobileNetV2 (0.05%). The code is available at: https://github.com/ThanosM97/gsoc2022-openvino