Chess Pieces Object Detection Dataset - raw
收藏public.roboflow.com2021-04-01 更新2025-03-22 收录
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https://public.roboflow.com/object-detection/chess-full/23
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# 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)
{'# Using this Dataset': '我们在此向公众免费发布数据,并采用公共许可证进行管理。', '# Use Cases': '在Roboflow公司,我们利用此数据集构建了一个棋子目标检测模型。

您可以在[此处](https://www.youtube.com/watch?v=XLispu-Yb_0)观看该模型的演示视频。(我们在处理被遮挡的棋子时遇到了一些挑战,例如游戏开始阶段棋盘上许多棋子被遮挡的情况 - 如有相关反馈,请告知我们您的检测结果!)', '# About Roboflow': '[Roboflow](https://roboflow.ai)致力于简化计算机视觉数据集的管理、预处理、增强和版本控制过程。
开发者在使用Roboflow的工作流程时,可减少50%的样板代码,节省训练时间,并提高模型的可复现性。
#### [](https://roboflow.ai)', '# Overview': '本数据集包含一系列国际象棋棋盘照片及其各种棋子。所有照片均以恒定角度拍摄,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方式进行标注:
- 白色国王(white-king)、白色王后(white-queen)、白色象(white-bishop)、白色骑士(white-knight)、白色车(white-rook)、白色兵(white-pawn)、
- 黑色国王(black-king)、黑色王后(black-queen)、黑色象(black-bishop)、黑色骑士(black-knight)、黑色车(black-rook)、黑色兵(black-pawn)。
该数据集包含292张图像,共计2894个标签。

**请参考[本教程](https://blog.roboflow.ai/training-a-yolov3-object-detection-model-with-a-custom-dataset/)了解如何使用本数据集训练目标检测模型,或直接跳转至[Colab笔记本](https://colab.research.google.com/drive/1ByRi9d6_Yzu0nrEKArmLMLuMaZjYfygO#scrollTo=WgHANbxqWJPa)进行操作。'}
提供机构:
Roboflow



