Teng Li

> @ddxu it should be solved by: [519b453](https://github.com/AITTSMD/MTCNN-Tensorflow/commit/519b453975a8dbdc7b0eecdb4f8735b7b6c80d46) @ilyanelken do we need to regularize the parameters in fully connected layers?

> 你好，用一个数据集比如FDDB，然后用pnet测试数据集的图片，将测试的结果与groundtruth比较，比如测试的Bbox与gt的IOU大于0.5，那么这个人脸就被检测正确。检测正确的人脸数除以总的人脸数就是召回率～ @AITTSMD 请问代码在哪呀？

Hi @david-di the error below occurred. Here is the code:

The implemented architecture of L_ResnetE_IR is as mentioned in the paper? As you @HsuTzuJen said there are some differences. Specially the size of implemented model is big.

The paper says stride of the second conv2 in each bottleneck is 2, but those in implementation are 1 except for those in the first unit of each block. Is...

So cool @HsuTzuJen. You got exact architecture of L_ResNet100E_IR? I am still confused about the structure of Resnet part. Plus, please don't forget to mention me as soon as you...

Thanks @HsuTzuJe. I have read the paper but the Arcface implementation here seems not to follow the original structure. For instance, "There are 14 units in block 3 in L_Resnet_E_IR.py,...

You are sure L_ResNet_E_IR was implemented in TF the same way as in MxNet?

@HsuTzuJen How is your own implementation going? Is there any changes to this one? :)

@HsuTzuJen I just checked the MxNet code. There is no bottleneck in L_ResNet_E_IR. So the filter number in each unit of a block is same. No need to 4* base_depth.