请问网络层为什么要使用多个尺寸的label呢？

[zhudaoruyi]

    out1 = Activation('softmax',name='l1')(Reshape((400 * 400, n_label))(R_out4))
    out2 = Activation('softmax',name='l2')(Reshape((200 * 200, n_label))(R_out3))
    out3 = Activation('softmax',name='l3')(Reshape((100 * 100, n_label))(R_out2))
    out4 = Activation('softmax',name='l4')(Reshape((50 * 50, n_label))(R_out1))

请问网络层为什么要使用多个尺寸的label呢？ 另外，您在推理预测的时候，好像并没有使用到多尺寸的pred

                pred = model.predict(crop,verbose=2)
                pred = pred[0]
                pred = np.reshape(pred, (1, c.size_train[1] * c.size_train[0], c.n_label))
                pred = np.argmax(pred, axis=2)

[TachibanaYoshino]

Multiple loss cascades for intermediate supervision