I am trying to create my own haar cascade classifier for hand gesture recognition. After generating the sample images[positive and negative] and generating the .vec file, when i try to execute the opencv_trainascaded exe file, i get the following error :
"Train dataset for temp stage can not be filled. Branch training terminated."
Can anyone help me in this regard??
Thanks in advance
Try opencv_haartraining function.
You might want to try aligning the positive and negative samples to the same size - see the linked reference.
Code wise, it looks like the error is only thrown when attempting to get the negative and positive images, so you might also want to make sure you are telling the classifier exe the correct number of positive and negative training samples.
http://nmarkou.blogspot.com/2012/01/using-train-cascades.html
check for the no of negative samples vs no. entries you have for them in .txt file.....
Related
i tried to normalize my data sets column with this code , but the results on the column in (daddr)was not in 0 , 1 range enter image description here
and also the results in loss apear like following enter image description here
this is the code i used enter image description here
please tell me what is the missing thing to solve the (loss ) problem , how i could do the MinMax Normalization on all data sets column , is the problem overfitting or what ?
Normalizing the data is not always necessary. It depends with the model you use. Most of the time Normalizing is necessary when working with sigmoid or tanh function in your model. Do you really need to normalize the data ? Try without Normalizing.
I'm training a LinearSVC model and I want to get the training error of it. Is it possible to get it w/o evaluating it manually?
Thanks
sklearn is using liblinear for this task.
You can take a quick glance into the sources here:
self.coef_, self.intercept_, self.n_iter_ = _fit_liblinear(
X, y, self.C, self.fit_intercept, self.intercept_scaling,
self.class_weight, self.penalty, self.dual, self.verbose,
self.max_iter, self.tol, self.random_state, self.multi_class,
self.loss, sample_weight=sample_weight)
which shows that only coefficients, intercepts and number of iterations are processed by sklearn's python-API. Whatever else is available in liblinear's output is not grabbed. You can't directly read out the training-error without changing the internal code.
There might be a possible hack turning on verbose-mode, redirect the output and parse additional info available there. But this assumes the info you look for is available there and it's also hacky and i won't recommend it.
Just use the score-method. It won't be too costly compared to fitting.
I understand that predict_generator outputs probabilities. To get the class, I just then find the index for the greatest probability and that will be the most probable class. However I find that after doing this, I get a different output than if I were to call predict_classes. I do not understand why. Can someone explain this please?
Generator in Keras uses glob to list folders which are alphabetically sorted, you can get classes being used during training using
# save classes to JSON
class_json = json.dumps(train_generator.class_indices)
with open("class.json", "w") as class_file:
class_file.write(class_json)
The samples are shuffled with in the batch generator(here) so that when a batch is requested by the fit_generator or evaluate_generator random samples are given.
Another possibility if this is being done on images is not to use rescale=1./255 in ImageDataGenerator as mentioned in https://github.com/fchollet/keras/issues/3477
Hope that help!
I would like to use the yolo architecture for object detection. Before training the network with my custom data, I followed these steps to train it on the Pascal VOC data: https://pjreddie.com/darknet/yolo/
The instructions are very clear.
But after the final step
./darknet detector train cfg/voc.data cfg/yolo-voc.cfg darknet19_448.conv.23
darknet immediately stops training and announces that weights have been written to the backups/ directory.
At first I thought that the pretraining was simply too good and that the stopping criteria would be reached at once.
So I've used the ./darknet detect command with these weights on one of the test images data/dog. Nothing is found.
If I don't use any pretrained weights, the network does train.
I've edited cfg/yolo-voc.cfg to use
# Testing
#batch=1
#subdivisions=1
# Training
batch=32
subdivisions=8
Now the training process has been runnning for many hours and is keeping my gpu warm.
Is this the intended way to train darknet ?
How can I use pretrained weights correctly, without training just breaking off ?
Is there any setting to create checkpoints, or get an idea of the progress ?
Adding -clear 1 at the end of your training command will clear the stats of how many images this model has seen in previous training. Then you can fine-tune your model on new data(set).
You can find more info about the usage in the function signature
void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
at https://github.com/pjreddie/darknet/blob/b13f67bfdd87434e141af532cdb5dc1b8369aa3b/examples/detector.c
I doubt it that increasing the max number of iterations is a good idea, as the learning rates are usually associated with current # of iteration. We usually increase the max # of iterations, when we want to resume a previous training task that ended because of reaching the max # of iterations, but we believe that with more iterations, it will give better results.
FYI, when you have a small dataset, training on it from scratch or from a classification network may not be a great idea. You may still want to re-use the weights from a detection network trained on large dataset like Coco or ImageNet.
This is an old question so I hope you have your answer by now, but here is mine just in case it helps.
After working with darknet for about a month, I've run into most of the roadblocks that people have asked/posted about on forums. In your case, I'm pretty certain it's because the weights have been trained for the max number of batches already, and when the pre-trained weights were read in darknet assumed training was done.
Relevant personal experience: when I used one of the pretrained weights files, it started from iteration 40101 and ran until 40200 before cutting off.
I would stick to training from scratch if you have custom data, but if you want to try the pre-trained weights again, you might find that changing max batches in the cfg file helps.
Also if using AlexeyAB/darknet they might have a problem with -clear option,
in detector.c:
if (clear) *nets[k].seen = 0
should really be:
if (clear) {*nets[k].seen = 0;*nets[k].cur_iteration = 0;}
otherwise the training loop will exit immediately.
Modify OpenCV number in your darknet/Makefile to 0
OpenCV=0
I try to model a CNN with deeplearing4j using SVHN dataset (http://ufldl.stanford.edu/housenumbers/), in particular I'm using
Format 2: Cropped Digits
This is matlab's files and each one contains a struct with a tensor (4-D) and an array with label. I would open this one into my deeplearing4j code, so I wondered and I find this class MatlabRecordReader.java into deeplearning4j/DataVec (https://github.com/deeplearning4j/DataVec/blob/master/datavec-api/src/main/java/org/datavec/api/records/reader/impl/misc/MatlabRecordReader.java) but I can't understand how use it. Anybody has experience whit this?
Thanks in advance
Here is a reference for "datavec":
http://deeplearning4j.org/DataVec
So if you look at:
http://nd4j.org/tensor
All of deeplearning4j's neural nets are written using nd4j (matlab for java) so this should be pretty easy to map.
You'll see it more or less maps to matlab.
What might be easier is if you could just write out the values as a csv
and reshape them to be the proper value instead. If you use c ordering it should work fine.
If you do that you can just use the csvrecord reader.
That matlab record reader hasn't been used by a lot of people and I think may only work with matrices (it's been a while)
I would try the csv one first.