是，您需要有另一个类，它是您不想检测的对象.

Yes, you need to have another Class which is the object you don't want to detect.

如果您没有这个其他类，其中包括所有不被检测到的内容.该模型会将其与与感兴趣的卡片几乎相同的现有类进行比较.

If you don't have this Other Class which includes everything that is not to be detected. The model will compare it to the existing class which is almost identical to the cards of interest.

其中一些因素是:

1. 形状
的相似性
2. 颜色的相似性
3. 符号
的相似之处

1. Similarity of Shape
2. Similarity of Color
3. Similarity of Symbols

这就是为什么即使它不是感兴趣的卡片(Skip、Reverse 和 Draw 4)，它也会以某种方式对这三个类具有很高的归属感".

This is why even though it is not the card of interest (Skip, Reverse, and Draw 4), it would somehow have high "belongingness" to these three classes.

让另一个班级转储所有这些可以显着减少对三个感兴趣的班级的归属感"，并尽可能在训练期间提供大量数据.

Having another Class to dump all of these can significantly lessen the "belongingness" to the three classes of interest and as much as possible provide A LOT of Data during Training.

如果你不想再上课.

您可以过拟合跳过、反转和抽 4 张卡片(接近 100%)，然后将检测阈值提高到(70-90%).

You could overfit Skip, Reverse, and Draw 4 cards (close to 100%), then increase your threshold value of detection to (70-90%).

希望对你有帮助.