We explore the power of the Transformer architecture in the object detection task. Given an image, the model (DETR) has to predict bounding boxes associated with a class. This novel and elegant approach, is using the best features of both CNNs and Transformers by omitting a lot of previously hand crafted features and achieving accuracies comparable to state of the art vision systems. We apply this approach on a chess dataset, with the goal of extracting the state of the game by detecting all pieces and the chessboard corners. Finally we compare DETR with YOLO performances.

Donload the zip in our drive to obtain the datasets, images and loaded models

https://drive.google.com/drive/u/3/folders/1aTZi5nNKx0bhO8nB74nx3trsIEwFLFLG

DETR:

https://github.com/roboflow/notebooks/blob/main/notebooks/train-huggingface-detr-on-custom-dataset.ipynb

YOLO:

https://github.com/roboflow/notebooks/blob/main/notebooks/train-yolo-nas-on-custom-dataset.ipynb

Real-Life Chess Vision:

https://github.com/shainisan/real-life-chess-vision

DETR last head transformer:

https://github.com/facebookresearch/detr