对象检测算法/网络通常可以通过预测边界框的位置以及班级。因此，训练数据通常需要包含边界框数据。通过为模型提供训练数据并带有始终是图像大小的边界框，那么很可能会产生垃圾预测，包括始终勾勒出图像轮廓的框。

Object detection algorithms/networks often work by predicting the location of a bounding box as well as the class. For this reason the training data often needs to contain bounding box data. By feeding your model with training data with a bounding box that is always the size of the image then it's likely you'll get garbage predictions out including a box that always outlines the image.

这听起来像您的训练数据有问题。您不应该提供裁切后的图像，而应标注带有对象的完整图像/场景。此时，您基本上是在训练分类器。

This sounds like a problem with your training data. You shouldn't give cropped images but instead full images/scenes with your object annotated. You're basically training a classifier at this point.

尝试使用未裁剪的正确样式的图像进行训练，看看您的情况如何。

Try training with the correct style of images that are not cropped and see how you get on.