Comments (14)
Hi,
I don't think anything reasonable can come out from training with 28 images, I don't think that can generalise enough to work on test images.
I've trained with ~3300 images too (all ibug images), that was the training set for the 4dface model.
The accuracy for some images are good, but some are not good.
That's a bit hard to comment on without numbers - the model I trained had an average error similar to state of the art models.
I can't download the SDM paper right now but IIRC b0/bk must be the bias (or also called affine term). The code fully supports that, just add a row to the end of your matrix. I believe the rcr-train
uses a bias term actually but I'm not 100% sure from the top of my head.
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Hi,
Many thanks for your answers!
I use the code landmark_detection.cpp to train the model with 3283 images, but the accuracy is really bad.
Maybe, I should use rcr-train. I will try this and tell you the latest output.
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Yes, definitely use rcr-train! The other app is just a simple demo.
If you drop me an email, I can send you the parameters I used to train the
4dface landmark model.
On 8 Jul 2016 02:28, "ardeal" [email protected] wrote:
Hi,
Many thanks for your answers!
I use the code landmark_detection.cpp to train the model with 3283 images,
but the accuracy is really bad.Maybe, I should use rcr-train. I will try this and tell you the latest
output.—
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Hi,
Thank you so much for your quick response!
My email address: [email protected]
I have one more questions:
In your code, you push back all training image to a vector as Mat datatype. as we know, there are more than 3000 images. we need a very big memory for those pictures. how did you manage to do that? how much memory in your computer?
from superviseddescent.
I've sent you the files and parameters. Feel free to share this information, the only reason it's not on GitHub (yet) is I don't have time for it. If you have any questions, you can also ask them here publicly.
Regarding memory: I remember I made a change to store a reference-to-Mat
, so each image will only be loaded once, and not for each perturbation, I think this should be live on GitHub. I believe the 3000 images required around 5GB of RAM but I could be mistaken, it's been a while - and our servers have >=64GB RAM ;-) Actually you can even calculate it by hand, they're 4-chan uchar
images, you can assume an average resolution of 800x600 or so (Helen images might be larger?).
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Hi,
I have the same problem as ardeal's. I use the rcr-train to train the model with ~3300 images(with 68 landmarks) which extract from video( http://ibug.doc.ic.ac.uk/resources/300-VW/), but the accuracy is not good. I trained the model according to the link(https://github.com/patrikhuber/superviseddescent/wiki/RCR-landmark-detection-training ). Could you give me some advice ?My email address: [email protected]
from superviseddescent.
the accuracy is not good
That's not very specific. Did you just try in front of your webcam, or really test on a well-known database and evaluated the ROC / average error?
One issue might be that there are no or few Chinese people in your training set, and then testing on Chinese people will naturally not give the best results.
I've uploaded the files I've used to train the 4dface model. You can have a look at them and the parameters.
You need to remove the .txt
extension.
- mean_ibug_lfpw_68_bbox_aligned.txt
- ibug_bboxes_mat2txt.m.txt
- rcr-train-from-bbox.cpp.txt
- rcr-test-from-bbox.cpp.txt
The parameters I used for one of the good models:
Set:
ibug-lfpw + ibug + afw + helen-tr
ground-truth bboxes
lms 68
perturbs 13
regressors 6
Regressor params (first to last):
MatrixNorm 5.0 false
MatrixNorm 5.0 false
MatrixNorm 5.0 false
MatrixNorm 5.0 false
MatrixNorm 5.0 false
MatrixNorm 5.0 false
HOG feature params for each regressor level:
VlHogVariantUoctti, 5, 11, 4, 1.5f
VlHogVariantUoctti, 5, 11, 4, 1.1f
VlHogVariantUoctti, 5, 10, 4, 0.7f
VlHogVariantUoctti, 5, 8, 4, 0.4f
VlHogVariantUoctti, 5, 6, 4, 0.25f
VlHogVariantUoctti, 5, 6, 4, 0.2f
perturb t_mu 0
t_sigma 0.08
s_mu 1
s_sigma 0.05
VerbosePartialPivLUSolver
InterEyeDistanceNormalisation
You can set them in the code. You can also modify some of them without incurring a large drop in performance (for example using only 4 regressors instead of 6 yields a model that is not too much worse).
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Hi,
Thank you so much for your quick response and help !
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Renamed title so people can find the files and parameters in the future, or we can link to this issue.
Closing, feel free to reopen if it's regarding the same issue, or open a new issue otherwise.
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Hi,
Thanks for your amazing source codes.
I have a question about ".mat" file which is used for facebox creation. Which algorithm is used for creating of "bb_detector" from "bb_ground_truth"?
Best regards.
from superviseddescent.
Hi @Sun1718, not sure what .mat file you're talking about, but if it's a problem related to this library, please open a new issue, it doesn't seem too related to this one.
from superviseddescent.
Thanks for your quick reply,
My question is about "ibug_bboxes_mat2txt.m.txt" file. ( As you mentioned above, you used this file for train the 4dface model).
In this file, in the line:
filename = '..\Bounding Boxes\bounding_boxes_helen_testset.mat';
you used "bounding_boxes_helen_testset.mat" file. In each cell of this file, there are three parameters: imageName , bb_detector and bb_ground_truth.
I don't know, how you calculate "bb_detector" parameter? calculate "bb_detector" from "bb_ground_truth"? how?
Thanks.
from superviseddescent.
I think the file bounding_boxes_helen_testset.mat
is from the ibug website download page, is it not? If so, you should ask any questions pertaining to those zip files to the authors of them, the people from the ibug group.
You can also have a look at the code in this repo, how I read those files and what I do with its content - this should give you a good idea about what it is.
I think your question may even be answered on the page where you can download those zip files on the ibug website. I suggest reading that.
from superviseddescent.
Oh, yes. you're right.
I didn't know that bounding_boxes_helen_testset.mat is for ibug website.
Thanks for your help.
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Related Issues (20)
- Parallelise CalculateHogDescriptor HOT 2
- eigen function runs very slow HOT 3
- save trained model with non-C++11 syntax HOT 10
- question: CPU time needed for hog feature in SDM code HOT 2
- Confidence measure of landmark detection? HOT 2
- Question: about pose estimation HOT 5
- Questions: how to optimize the speed of landmark detection code HOT 4
- Run rcr-train on my own data HOT 16
- Error Building in Visual Studio RC2015 on Windows 10 HOT 3
- Crashing after a face is detected with rcr_track.cpp HOT 5
- Way to detect whether the detected landmarks are actual landmarks on a face HOT 3
- landmark detection.cpp trained result looks like mean shape. HOT 2
- Pose estimation for an input image HOT 4
- 68 landmark points pre-trained model HOT 3
- How can i get mean_ibug_lfpw_68.txt in new dataset? HOT 1
- Inter-eye Distance Normalization HOT 3
- Pre trained model for 2d landmarking tracking HOT 1
- Training with 10K images HOT 1
- Bug suspicion HOT 3
- Learning the bias B_k HOT 5
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