huangyangyu / seqface Goto Github PK

Hi

First of all, thank you for your work.

I am trying to run the trained model with some custom images given by me. The results did not make sense so I'd like to ask you if I'm missing something.

1- I prepared a small dataset of a few images
dataset.zip

2- Then I created pairs.txt like this:

adile1.jpg adile2.jpg 1
adile1.jpg adile3.jpg 1 
sener1.jpg sener2.jpg 1 
munir2.jpg adile1.jpg 0 
munir1.jpg sener1.jpg 0
sener2.jpg adile2.jpg 0

3- I run evaluate.py in the LFW folder. I have obtained a similarity score for each pair which are given as follows:

adile1.jpg adile2.jpg 1 -4.43
adile1.jpg adile3.jpg 1 -2.06
sener1.jpg sener2.jpg 1 -0.07
munir2.jpg adile1.jpg 0 -0.88
munir1.jpg sener1.jpg 0 -3.38
sener2.jpg adile2.jpg 0 -5.03

The results confused me a bit. Similarity between "adile1" and "adile2" (same person) is -4.43 while similarity between "sener2" and "adile2" is -5.03. If you inspect the above list, it is hard to observe a proportion in the similarity score for similar and/or different people.

How can it be?

Secondly, is there a rough threshold for the similarity score between two images of the same person? For instance, score is between -5 and 0 for the same person and it is far less than this (e.g., -100) for different people?

Thanks in advance.

Best regards

Thank you for your generous share , your model has very good robustness through large-scale data testing. I am trying to reiteration papers on resnet20 , but met some difficulties ,such as convergence difficulties， fine-tune failure. Could you share some training expirence for us , or give some more detailed proposals about how to prepare training data ,how to set params , perhaps there are some tricks ，easily neglected ，which make the training hard to convergence .
Looking forward to your reply.

I running your web demo of face verification. And there have two result which are sim old and sim new. what is that meaning?

looking forward to the resnet64 model ,when do you plan to release it ?

What's the right version of cuda and cudnn for the caffe compling?

It seems the result on new sim method is really good on your demo page, could you please share it? thank you

Hi,

I have tried to run the MTCNN detector for YTF dataset, but there are some frames with no detections. How do you deal with this? Did you discard these frames?

Thank your share the nice job.
I have download your res-27 model,and test it on LFW,but only get 99.48%,maybe something wrong when I align photos.
I use MTCNN detect all photos,and align it with this
coord5point = [ 46.29460144, 59.69630051;
81.53179932, 59.50139999;
64.02519989, 79.73660278;
49.54930115, 100.3655014 ;
78.72990417, 100.20410156];
then crop to be 128x128.
Am I right? Or problem is here?
At the end,I use evaluation code in sphereface,and can get accuracy 99.48%(with image flip),without image flip it can get 99.43%.
Can you give me some advise about how should I do?
BTW,I also try your python code norml2_sim,but can get the same result.
Thanks very much!

When running evaluate.py I get:

best: 6000 -11.621302604675293 0.989

Is the resnet-27 accuracy a typo or am I missing something?

Hello,

make all for the caffe comes with error

src/caffe/layers/dsa_loss_layer.cpp:366:1: note: in expansion of macro ‘STUB_GPU’
STUB_GPU(DSALossLayer);
^~~~~~~~
Makefile:573: recipe for target '.build_release/src/caffe/layers/dsa_loss_layer.o' failed
make: *** [.build_release/src/caffe/layers/dsa_loss_layer.o] Error 1
make: *** Waiting for unfinished jobs....

Whether the two datasets(identity and sequence) need to meet a specific ratio?

I have tested with some image pairs which belong to the same person, and uploaded one of the comparison result screenshot:
1.
https://imgur.com/a/f3YDk
the similarity between 2 person is about 51% based on the L2 distance --->similarity conversion:
https://stats.stackexchange.com/questions/158279/how-i-can-convert-distance-euclidean-to-similarity-score
2.
https://imgur.com/a/6dzRZ

@huangyangyu Hi, your "example/train_val.prototxt" has the "SmoothMarginInnerProduct" layer , but your caffe hasn't it.

https://github.com/huangyangyu/SeqFace/blob/master/code/YTF/evaluate.py#L68
Hi, could you please provite " pairs.txt" in YTF evaluation code?

Hi :)
Can you please release the model with Pytorch compatibility?

Thx in advance!

I have some confusion about backward of NoiseTolerantFRLayer, why when skip_ is True, bottom_diff multiply zero? why not just return as the situation "iter_<start_iter_"?

` void NoiseTolerantFRLayer::Backward_cpu(const vector<Blob>& top,
const vector& propagate_down,
const vector<Blob>& bottom)
{
if (propagate_down[0])
{
const Dtype* label_data = bottom[2]->cpu_data();
const Dtype* top_diff = top[0]->cpu_diff();
Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
const Dtype* weight_data = weights_.cpu_data();

      int count = bottom[0]->count();
      int num = bottom[0]->num();
      int dim = count / num;

      if (top[0] != bottom[0]) caffe_copy(count, top_diff, bottom_diff);

      if (this->phase_ != TRAIN) return;

      if (iter_ < start_iter_) return;

      // backward
      for (int i = 0; i < num; i++)
      {
          int gt = static_cast<int>(label_data[i]);
          if (gt < 0) continue;
          for (int j = 0; j < dim; j++)
          {
              bottom_diff[i * dim + j] *= skip_ ? Dtype(0.0) : weight_data[i];
          }
      }
  }

}`

Hi, thanks for your code. Great Work.
Whether the losses in your paper can be combined with ArcFace? if I want to combine the losses with ArcFace, it should be LSR-ArcFace+DSA loss? After combination, whether face recognition efficacy can be better?
It's mentioned that the accuary of LSR-L2-SphereFace+DSA is slightly lower than L2-SphereFace in table 1 in your paper. @huangyangyu