retinaface使用FGD精度下降

[408550969]

你好，我用retinaface训练自己的数据集，发现使用fgd后精度比不用低。teacher模型已验证精度，是正常的。我的teacher模型backbone是resnet50，student的backbone是mobilenetv3-small。
我将ssh后的特征层拿来计算fgd的loss，具体代码如下：
out,features = net(images)
loss_l, loss_c = criterion(out, priors, targets)
loss = cfg['loc_weight'] * loss_l + loss_c
with torch.no_grad():
teacher_out,teacher_features = teacher_net(images)
for i in range(3):
ComputeFeatureLoss = FeatureLoss(features[i].shape[1],teacher_features[i].shape[1])
ComputeFeatureLoss = ComputeFeatureLoss.cuda()
distilloss=ComputeFeatureLoss(features[i],teacher_features[i],targets)
loss= loss + distilloss
retinaface的targets是归一化的值，不是像素值，所以我去掉了img_metas。FeatureLoss的参数采用的默认值，请问造成精度较低的原因可能有哪些？