Model Releases
ChessQueries: Toward Better Chess Board Recognition
arXiv:2608.30762v1 Announce Type: new Abstract: Chess board recognition is the task of mapping the image of a chess board to the information of which piece is on which square. So far this task has two
arXiv:2608.30762v1 Announce Type: new Abstract: Chess board recognition is the task of mapping the image of a chess board to the information of which piece is on which square. So far this task has two established benchmarks: ChessCog is synthetic, and ChessReD comes from smartphone pictures of a single chess board setup. We introduce ChessQueries, a new method combining a ViT encoder with a DETR-style decoder, which outperforms existing methods. On the ChessReD benchmark, we improve the state of the art from 15.3% to 99.2%, and demonstrate strong capabilities on out-of-distribution datasets. Our method saturates the task on the two datasets, with an average 0.01 wrong squares per board (vs. SotA: 3.4 / 0.15 respectively). We also share a new, harder public dataset, parsed from broadcasted top-level chess tournaments. Code, model weights and the SLCC data will be released.
Related
- COCOTree: A Dataset and Benchmark for Open Tree-Structured Visual Decomposition
- OSMa-Bench++: Toward Open-Ended Benchmarking of Semantic Mapping for Manipulation with Prompt-Generated Synthetic Scenes
- Training Crossroads for Recurrent Vision Transformers: Recurrence, Neural ODEs, and Deep Supervision
Source: arXiv cs.CV | 2026-09-01