Comments (16)
hi, YangUSTC
Did you finial get your training result well?
Linson Wang
from pico.
Is the default training sample (GENKI) working well? I.e., were you able to get a working cascade?
If so, the dataset you use may be too "diverse" for trees of depth 5 (default). Try increasing the tree depth (line 851 in picolrn.c
). E.g., set tdepth = 7
.
from pico.
Thanks for answering my question
The default training sample(GENKI) is working well(i mean about training).As you know,the GENKI dataset has a few people face,so the detection performance is not good in FDDB dataset.
I have read your paper.In the paper,your training sample is chosen in AFLW dataset,and setting the depth of trees 6.I also use AFLW dataset and set the depth of trees 6.And i haven't changed any other parameters.
Could you tell me more details about how to choose the positive and negative samples(especially in quantity)
Buy the way,only setting tdepth=12,in the first stage,the number of tree is 1.
from pico.
Did you check whether your data-generating script outputs good images (faces) and that the bounding box coordinates are OK? Can you send me some face images with drawn bounding boxes generated by the script?
from pico.
from pico.
I have already responded to your email.Please check it.
from pico.
Yes, I can see your emails. However, there are no images attached.
from pico.
I have checked this question.That's no problem(i think).I will send you some face images with drawn bounding boxes generated by the script through the annex(especially i picked some side face).I hope you can give me some advice about the training samples.
from pico.
The images seem OK. Note that pico will have problems if there's too much variation in the dataset.
Can you send me the output of picolrn.c
when run on your data?
from pico.
myfacefinder.zip
OK.I used the entire AFLW dataset(24384 face),and used genki.py to process the image(just nrands set to 3 ).Then i got 1584 trees.I have describe the problem when i first ask you this question.
from pico.
Could you please give me your training data?
from pico.
myfacefinder.zip
I expected the text that picolrn.c
outputs to the console. Please send that.
Could you please give me your training data?
The original facefinder
cascade was produced in 2013. Thus, I do not have its training data any more.
from pico.
I get the same problem. I am willing to train a model, I have the right train data, but I do not know how to use the "picolrn.c". I have a puzzle about the function——"main"——in picolrn.c.
from pico.
What are the parameters——argv[1], argv[2]... mean?
from pico.
nobjects == 0;The program break.
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Related Issues (20)
- The details in Training HOT 5
- tree tdepth HOT 2
- the method is fast!
- Rectangular detection HOT 1
- train data problem HOT 1
- Problem in training my
- Problem in training my own detectors HOT 2
- it's not faster than OpenCV 3.1
- Background dataset HOT 2
- can't detect rotated face HOT 7
- Advice on training custom detectors? HOT 4
- Problems about accelerating the speed of inference stage HOT 2
- Scanner logic should be from largest to smallest
- Could I ask what does s = 2.0*1.5*eyedist means in caltechfaces.py?
- what should I set ldim to? HOT 4
- Understanding tsr tsc parameters HOT 1
- Can i improve the code for eye detction HOT 1
- understanding the lut parameter HOT 1
- no documentation for learning custom detectors
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