CORe50是专为（C）物体（O）物体（Re）认知而设计的，它是50个生活用品的集合，属于10个类别：插头适配器，手机，剪刀，灯泡，易拉罐，眼镜，球，标记物，杯子和遥控器。分类可以在对象级别（50个类别）或类别级别（10个类别）执行。第一项任务（默认任务）更具挑战性，因为在某些姿势下很难区分相同类别的对象。

该数据集已在11个不同的会话中收集（8个室内和3个室外）以不同的背景和光线为特征。对于每个会话和每个对象，使用Kinect 2.0传感器记录了15秒的视频（20 fps），该视频提供300 RGB-D帧。

操作员可以手持物体，而摄像机的视点就是操作员的眼睛。要求操作员伸出手臂并平稳地移动/旋转摄像机前面的物体。物体在抓斗距离处的主观视角非常适合许多机器人应用。整个过程中，抓手（左手或右手）都会发生变化，并且相关的对象遮挡通常由手本身产生。

Fig.1 Example images of the 50 objects in CORe50. Each column denotes one of the 10 categories.

The presence of temporal coherent sessions (i.e., videos where the objects gently move in front of the camera) is another key feature since temporal smoothness can be used to simplify object detection, improve classification accuracy and to address semi-supervised (or unsupervised) scenarios.

In Fig. 1 you can see some image examples of the 50 objects in CORe50 where each column denotes one of the 10 categories and each row a different object. The full dataset consists of 164,866 128×128 RGB-D images: 11 sessions × 50 objects × (around 300) frames per session. Three of the eleven sessions (#3, #7 and #10) have been selected for test and the remaining 8 sessions are used for training. We tried to balance as much as possible the difficulty of training and test session with respect to: indoor/outdoor, holding hand (left or right) and complexity of the background. For more information about the dataset take a look a the section "CORe50" in the paper.