I am trying to build a classifier to detect faces in Thermal images. So I tried training using Haar, LBP and HOG classifiers. I am working with OpenCV 2.4.8 on windows.
opencv_traincascade.exe -data haarcascades -vec pos.vec -bg neg.txt -numPos 250 -numStages 24 -numNeg 900 -w 24 -h 24
I have 307 positive samples in total. The negative samples are of size 75x75. For each of the three cases the training gets stuck at a particular stage-earlier for Haar (stage-12) and later for LBP (stage-14/15). I reduced the number of negatives (upto 200) but that means the training gets stuck at a later stage. The training hasn't progressed since 2 days. No negatives are being consumed and the command window looks like this-
===== TRAINING 14-stage =====
<BEGIN
POS count : consumed 255 : 262
Also
What do POS count consumed and NEG count consumed signify?
When I reduce the minHitRate to say 0.7 why do the number of POS consumed increase?
Please let me know what I am doing wrong.
Thanks.
I had the similar problem myself. The thing is that classifier at each stage takes those negative examples which are classified as positive in the previous stages. So the thing that happens is that none of the negative samples are classified as positive and the code goes in the infinite loop trying to find one. I solved this by changing the source code so that the algorithm terminates after it cant find any negative example and just use the previous stages for the classifier.
If you dont want to change the code try adding more negative examples or reducing the number of stages.
Count consumed is the amount of possitve and negative images that are used in each stages. And you need to use more possitive and negatives images around 1000 positives and 2000 negative to get a good result
Related
I am wondering why the number of images has no influence on the number of iterations when training. Here is an example to to make my question clearer:
Suppose we have 6400 images for a training to recognize 4 classes. Based on AlexeyAB explanations, we keep batch= 64, subdivisions = 16 and write max_batches = 8000 since max_batches is determined by #classes x 2000.
Since we have 6400 images, a complete epoch requires 100 iterations. Therefore this training ends after 80 epochs.
Now, suppose that we have 12800 images. In that case, an epoch needs 200 iterations. Therefore the training ends after 40 epochs.
Since an epoch refers to one cycle through the full training dataset, I'm wondering why we don't increase the number of iterations when our dataset increases, in order to keep the number of epochs constant.
Said differently, I'm asking for a simple explanation as to why the number of epochs seems to be irrelevant to the quality of the training. I feel that it's a consequence of Yolo's construction but I am not knowledgeable enough to understand how.
Why the number of images has no influence on the number of iterations when training?
In darknet yolo, the number of iterations depends on the max_batches parameter in .cfg file. After running for max_batches, the darknet saves the final_weights.
In each epoch, all the data samples are passed through the network, so if you have many images, the training time for one epoch (and iteration) will be higher, you can test that by increasing images in your data.
The sub-division accounts for the number of mini-batches. Let's say, you have 100 images in your dataset. your batch size is 10, sub-division is 2, max_batches is 20.
So, in each iteration, 10 images are passed to the network in two mini-batches (Each having 5 samples), once you have done 20 baches (20*10 data samples), the training will be completed. (The details can be a little different, I'm using a slightly modified darknet by original author pjreddie)
The instructions are updated now. max_batches is equal to classes*2000 but not less than number of training images and not less than 6000. Please find it at this link.
I have been trying to train image databases to detect faces using Haar cascades. I have made 2 attempts:
1) I have used the following database for positive images:
http://robotics.csie.ncku.edu.tw/Databases/FaceDetect_PoseEstimate.htm#Our_Database_ (6660 images)
For negative images I have used this database:
https://github.com/sonots/tutorial-haartraining/tree/master/data/negatives (3300 images)
I have used this command to train the samples:
opencv_createsamples -info info.dat -vec samples2.vec -w 32 -h 24 -num 6660
I have used this command to train the data:
opencv_traincascade -data ./classifier3 -vec samples2.vec -bg bg.txt -numPos 6000 -numNeg 12000 -numStages 30 -precalcValBufSize 5120 -precalcIdxBufSize 5120 -numThreads 12 -acceptanceRatioBreakValue 10e-5 -w 32 -h 24 -minHitRate 0.99 -maxFalseAlarmRate 0.5 -mode ALL
The training goes on up to stage 9. Then the acceptanceRatio break value is crossed.(The required acceptanceRatio for the model has been reached to avoid over-fitting of training data. Branch training terminated.)
I don't understand the issue here. I have only used the recommended values for the parameters. I had tried changing the minHitRate to 0.95, yet the result is the same. I can think of some potential reasons:
i) I had used the positive images directly without cropping. But I don't
think that should be an issue, as the background is completely plain.
ii) The image database contains faces in different poses. That could lead
to complications while training. Is it a good idea to train faces
under different poses using the same cascade classifier? Or should I
use different classifiers for each pose?
iii) My negative images might be too different compared to the positive
images. Is that the case? If yes, what kind of negative images should
I be looking for?
I tried testing the cascade.xml file on a few sample images, but nothing is detected at all.
2) Keeping in mind the potential reason i), I used this database already cropped, for positive images: http://conradsanderson.id.au/lfwcrop/ (around 13000 images)
But still the problem persists. This time it trains upto stage 11. In this case I used -numPos as 8000 -numNeg as 20000( increased the ratio to give the training more negative samples), -w as 24 and -h as 24.
Can anyone please guide me here?
I am trying my own haar cascade classifier I have 2139 positive images However I have 16000 negative images This is right ? And so I have a negative numPos
Because:
numPos<=(Positive samples-negative samples)/(1+(stages number-1)(1-minhitrate)))
so:
(2139-16000)/(1+(17-1)(1-0.995))=-12834
This is normal??
no, numPos has nothing to do with your negative samples. numPos is the number of positives you want to use in each stage. This must be a bit lower than your total number of positive samples because you'll lose all the false negatives ( = positive samples which are falsely not detected anymore by the classifier) in each stage.
For example if you sr numPos to 1000 and minHitRate to 0.999 you lose up to 1 positive sample (1000 - 1000*0.999) in each stage. So if you want to compute 2 stages you'll need up to 1001 samples when choosing numPos = 1000.
For 20 stages I roughly choose numPos to be 90% of my positive samples although that is too pessimistic for minHitRate 0.999 (fits 0.995 quite well afair). There is a formula in the openCV Q&A if you want to compute the best/max save value.
I used typical haar-cascade of OpenCV.
And setup stages as 5 in training process,but in xml & cascade folder only 3 stages were found.
Why I got fewer stages than expected?
Any solutions?
Take this example training command:
opencv_traincascade -data classifier -vec samples.vec -bg negatives.txt\
-numStages 20 -minHitRate 0.999 -maxFalseAlarmRate 0.5 -numPos 1000\
-numNeg 600 -w 80 -h 40 -mode ALL -precalcValBufSize 1024\
-precalcIdxBufSize 1024
This has a maxFalseAlarmRate of 0.5, when the classifier reaches this value it will finish.
For your problem, I imagine you have set the numStages to 5 but after 3 stages it has reached the maxFalseAlarmRate and completed the training.
In order to confirm/dispell this you would need to provide:
Your training command (as above)
The output from your last training stage.
You most likely have not provided traincascade enough information to learn from. This is most likely because we humans are incredibly lazy and hate to work. It would have kept going if it thought it could learn more from the data you specified.
Take more positives. Remember that you can take multiple images of your object a slightly tilted angles (10º-20º or so). And be sure to provide at least hundreds of your objects, especially if there is quite a bit of variation between your objects, like there are with faces.
If you're still stuck, see this tutorial I wrote that can hopefully help you and others: http://johnallen.github.io/opencv-object-detection-tutorial/
I'm working with OpenCV 2.4.7 on windows. I'm using TrainCascade to train a new Haar cascade for eyeglasses using the following command:
opencv_traincascade -data trainCascade20 -vec vector3.vec -bg infofile3.txt -numStages 40 -minHitRate 0.999 maxFalseAlarmRate 0.5 -numPos 170 -numNeg 1000 -w 20 -h 20 -mode ALL -precalcValBufSize 1024 -precalcIdxBufSize 1024
It's stuck (or progressing very slow) on stage 24 on the phase of getting new negatives. The negative images file "infofile3.txt" contains about 12K negative image.
Can someone please explain why it's progressing so slowly and what can I do make it progress (a lot) faster?
Thanks in advance,
Gil.
Around 24 hours sounds normal to me. Haar training can actually take up to days depending on size and number of samples. And of course on the computer as well. The longest my training took was approximately a week for hand detection.
If you are really worried, to check whether the haar training is still on-going, you can try to generate an intermediate haar cascade xml file, from the data available. If you are able to generate the xml file, it would show that it's still running(albeit slow) and not stuck.
How to improve the haar training speed, the only solution I know or used before is "paralleling"
A quick search on google about that leads to a few link, here's one of them: http://www.computer-vision-software.com/blog/2009/06/parallel-world-of-opencv/
I have used such methods, and it's pretty efficient in cutting the time taken to train the Haar Cascade. So hope this method suits you well. Do try my method of generating an immediate xml file from the current data available first though. If there is any needs, do comment, I try get back to you soon. Cheers.