Comments (12)
Thank you for your timely reply.
I made a mistake in the formula , so that the result was quite different from the test result you published. And Failure rate = (Number of samples whose mean error > 0.1 ) / num of all samples. But I am puzzled about how the threshold 0.1 is determined?
thank you very much @wywu
from lab.
Hi,
Thanks! You can calculate the mean error with “inter-ocular” normalising factor by using https://ibug.doc.ic.ac.uk/media/uploads/competitions/compute_error.m and modify the index of outer-eye-corners. Failure rate is calculated as the percent of samples in the test set whose error is larger than 10%.
Best,
Wayne
from lab.
Hi,
We follow “DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild” to use threshold 0.1
However, some literatures also use 0.08 as the threshold.
Best,
Wayne
from lab.
Hi,
I did again as what you told. The result was similar with you. thank you so much!
from lab.
when I read the code in file tools/alignment_tools.cpp,I found that the prediction process used annotated points.
line 246
vector<float> label_71pt_list(71*2); for (size_t j=0; j<76; j++) { label_71pt_list[j] = label_list[i][j]; } for (size_t j=76; j<86; j++) { label_71pt_list[j] = label_list[i][j+8]; } for (size_t j=86; j<94; j++) { label_71pt_list[j] = label_list[i][j+16]; } label_71pt_list[94] = label_list[i][120]; label_71pt_list[95] = label_list[i][121]; label_71pt_list[96] = label_list[i][128]; label_71pt_list[97] = label_list[i][129]; label_71pt_list[98] = label_list[i][136]; label_71pt_list[99] = label_list[i][137]; label_71pt_list[100] = label_list[i][144]; label_71pt_list[101] = label_list[i][145]; for (size_t j=102; j<142; j++) { label_71pt_list[j] = label_list[i][j+50]; } vector<Point2f> landmark=ToPoints(label_71pt_list);
This does not seem reasonable. If I know the landmark in an image, it is unnecessary to use LAB to predict the landmark. Why ? @wywu
from lab.
Hi,
In this version of evaluation code, annotated landmarks only play the role of detection rectangle to crop the face. Generally, we could use the detection rectangle directly rather than annotated landmarks for cropping and there has shown hardly any performance difference for this cropping method in our experiments.
Best,
Wayne
from lab.
thank you for you reply.
You means that I need detect the position of face in one image, then crop the face, feed into the LAB, can get landmark. So the meanpose file is not useful for me ?@wywu
from lab.
Yes, I used the cropped the face as input, get very good landmark. but the time is 500ms. the cropped face size is less than 600*800. I had build caffe on cudnn and nvcc. Do you think why the time is larger than 60ms? @wywu
from lab.
Hi,
We have not supplied the GPU version evaluation code by now.
Best,
Wayne
from lab.
So happy for you reply. I just modify the code in the alignment_tool.cpp. caffe::Caffe::set_mode(caffe::Caffe::CPU);
to caffe::Caffe::SetDevice(0); caffe::Caffe::set_mode(caffe::Caffe::GPU);
. Is it the GPU version code? or any other code needed modified? @wywu
from lab.
Hi,
I did again as what you told. The result was similar with you. thank you so much!
I have the same question about the formula of calculate mean error and failure rate in WFLW dataset? And i noticed that you have finished this work ,can you provide me the formula ,thx
from lab.
@so-as hi,
Have you already done detection + landmark
pipeline? If so, would you plz share your work? It would be very helpful! Thanks!!
from lab.
Related Issues (20)
- 你好,请问Boundary Heatmaps的Truth Heatmap我们是怎么打标得到的,而且打标后有经过什么处理? HOT 1
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- 关于IPN和ION差距的疑问,IPN和ION的测试是同一个模型吗 HOT 2
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