Chess Pieces Object Detection Dataset - 416x416_aug

# Overview

This is a dataset of Chess board photos and various pieces. All photos were captured from a constant angle, a tripod to the left of the board. The bounding boxes of all pieces are annotated as follows: `white-king`, `white-queen`, `white-bishop`, `white-knight`, `white-rook`, `white-pawn`, `black-king`, `black-queen`, `black-bishop`, `black-knight`, `black-rook`, `black-pawn`. There are 2894 labels across 292 images.


**Follow [this tutorial](https://blog.roboflow.ai/training-a-yolov3-object-detection-model-with-a-custom-dataset/) to see an example of training an object detection model using this dataset or jump straight to the [Colab notebook](https://colab.research.google.com/drive/1ByRi9d6_Yzu0nrEKArmLMLuMaZjYfygO#scrollTo=WgHANbxqWJPa).**

# Use Cases

At Roboflow, we built a chess piece object detection model using this dataset.


You can see a video demo of that [here](https://www.youtube.com/watch?v=XLispu-Yb_0). (We did struggle with pieces that were occluded, i.e. the state of the board at the very beginning of a game has many pieces obscured - let us know how your results fare!)

# Using this Dataset

We're releasing the data free on a public license.

# About Roboflow

[Roboflow](https://roboflow.ai) makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility.

#### [](https://roboflow.ai)

{'# Overview': '本数据集收录了棋盘及棋子图像。所有图像均从固定角度拍摄，棋盘左侧放置了三脚架。所有棋子的边界框均按照以下标注进行标记：白王（white-king）、白后（white-queen）、白象（white-bishop）、白骑士（white-knight）、白车（white-rook）、白兵（white-pawn）、黑王（black-king）、黑后（black-queen）、黑象（black-bishop）、黑骑士（black-knight）、黑车（black-rook）、黑兵。该数据集包含292张图像，共计2894个标签。', '': '', 'Follow [this tutorial](https://blog.roboflow.ai/training-a-yolov3-object-detection-model-with-a-custom-dataset/) to see an example of training an object detection model using this dataset or jump straight to the [Colab notebook](https://colab.research.google.com/drive/1ByRi9d6_Yzu0nrEKArmLMLuMaZjYfygO#scrollTo=WgHANbxqWJPa)': '请遵循[本教程](https://blog.roboflow.ai/training-a-yolov3-object-detection-model-with-a-custom-dataset/)以了解如何使用此数据集训练目标检测模型，或直接跳转到[Colab笔记本](https://colab.research.google.com/drive/1ByRi9d6_Yzu0nrEKArmLMLuMaZjYfygO#scrollTo=WgHANbxqWJPa)。', '# Use Cases': '在Roboflow中，我们利用此数据集构建了一个棋子目标检测模型。[](https://www.youtube.com/watch?v=XLispu-Yb_0)。您可以在[此处](https://www.youtube.com/watch?v=XLispu-Yb_0)观看该模型的视频演示。（我们在处理被遮挡的棋子方面遇到了一些挑战，例如游戏开始时棋盘上许多棋子被遮挡的情况——请告知您的检测结果！)', 'You can see a video demo of that [here](https://www.youtube.com/watch?v=XLispu-Yb_0). (We did struggle with pieces that were occluded, i.e. the state of the board at the very beginning of a game has many pieces obscured - let us know how your results fare!)': '您可以在[此处](https://www.youtube.com/watch?v=XLispu-Yb_0)观看该模型的视频演示。（我们在处理被遮挡的棋子方面遇到了一些挑战，即游戏开始时棋盘上许多棋子被遮挡的情况——请告知您的检测结果！）', '# Using this Dataset': '我们免费向公众发布此数据集。', '# About Roboflow': '[Roboflow](https://roboflow.ai)致力于实现计算机视觉数据集的管理、预处理、增强和版本控制的一体化。', "Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility.": '开发者在使用Roboflow的工作流程时，可减少50%的冗余代码，节省训练时间并提高模型的可重复性。', '#### [](https://roboflow.ai)': ''}