I'm using the object_detection from the tensorflow/models repo. I'm training the Faster R-CNN Resnet101 Model with my own Images. In my models, I only use 2-3 classes to find particular objects on the images.
After about 60k training steps, I get quite good results in finding the searched objects. The only problem is, that my false-positive rate is quite high. When the searched object is in the image, I get a result with 99%, but on about 60% of the results >90% the searched object is not there.
Does anyone has some hints, how I can reduce the false-positive rate? Is there a way in the used model to give the training specific false-positive examples?
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By “Cold Start” I mean that often computer vision models for object detection or semantic segmentation require about 5000 images per class. So if an idea if floated within the company for e.g. we want to use object detection to count the number of wood logs when the truck is dispatched and then use the same app to count the number that is received.
So now the challenge is that you have only a few images of woods logs on a truck but to train any model you need thousands, so what do practitioners typically do for these prototypes?
Because at this stage it is not clear what model to try? It is also not very feasible to ask business to invest in collecting thousands of images of logs and label them?
That is why I am calling this “Cold Start”. How do you start?
What I have looked into is Conditional GANs, Pix-2-Pix but I am trying to understand the recommended method on how to start when you have very few images per object class.
I expect that when I drop a few images in a folder and call this library I end up getting a lot more images per class so I can then start my prototyping.
Note that asking for software libraries is specifically off-topic here.
No, there is no magic solution: if your data set doesn't have enough information in its images to train a hand-crafted model, no amount of software will change that fact. However, the first approach is to challenge that "fact": how do you know that you don't have enough images? What happened when you used what you have to train a model? You will train for more epochs before the model converges, but you should be able to achieve far better than random accuracy by training a comparable quantity of iterations.
I seriously doubt that you'll need to collect and label thousands of images: you have a very restricted paradigm, photos of log trucks taken from an vantage point you control. Training a model to count non-overlapping near-circles will take much less differentiation than, say, distinguishing motor vehicles from postal boxes.
Experiment with the basic models you have at hand -- you already have much more of the solution than you realize. If your data set is too small, go out the yard with a digital camera and get twice as many, three times, whatever you need. Flip the images left-right to get more input.
Does that get you moving?
Transfer learning solves the problem you are describing as "Cold Start". Basically you can import the weights obtained after training using a big and open dataset and just fine-tune them using the smaller dataset you already have. Data augmentation, freezing some of the layers, etc may help improving the results of a fine-tuned model.
I have a data science problem which has around 70k images already labelled across 20 different categories. Some categories have many images whereas some other have fewer images. This in turn results in an imbalanced data set and poor results (currently at 68% accuracy). After some research I found out that I need to do a sampling of the images (Image mining?) instead of selecting all the images. One such approach could be Stratified sampling. Question is how do I select images to optimise the training of the model? Any command line tool or open source code that I could use on 70k images?
You have imbalanced data so to deal with that, you can simply use a library called Imbalanced learn
This library is originally focussed for implementing SMOTE but later also implemented under sampling and over sampling techniques.
It is also compatible with scikit-learn.
Using this approach, will result in data resampling in a way that every class has nearly equal instances.
Second option:
You can simply pick equal number of images for each class and form training data. This may not boost your accuracy due to lack of proper test data but surely your model will become more robust and generalized.
I would like to use the Haar classifier to detect the presence of vehicles in a scene (trying with only cars so far). Since I have not found many trained XML files online, I decided to generate my own.
I found some image sets of vehicles that have been used for similar purposes (training computer vision algorithms) and used these to create my own XML files. It has been almost a week and some of them have finished, so I tried using them but the results were terrible. The classifiers I found online worked decently, at least it appears they are trying to detect vehicles and work fast enough for real-time application (maybe 5-10 FPS or so).
Whereas mine can take several minutes to analyze a frame using detectMultiScale() using the same parameters, and if I pass different parameters (e.g. increase min size, decrease max size, increase scaling factor) it will work faster (maybe 1 FPS) but detects absolutely nothing of note, never detects any vehicle and randomly detects some spots of asphalt as a vehicle.
Where did I go wrong in generating my files? I have limited time to complete this task and these classifiers can take a whole week to train so I have very few attempts remaining. For reference, my methodology is (following this tutorial):
-Take all positive and negative images; if no negative images supplied, take negative images from another data set, at least as many negatives as positives
-Generate as many samples as the number of positives
-Use same parameters as suggested, except image size (set to the size of images in a given data set), and nstages (set to 10 because 20 takes far too long)
-For the npos parameter, I use 1/10th the number of samples, using the full number of samples resulted in "assertion failed" after a few hours, apparently the number of samples cannot be the same as the npos according to this so I gave myself a safety margin.
TL;DR Haar classifier I trained myself performs much worse than one found online (in terms of time and accuracy), need advice on how to improve it and not waste another week training it.
There are two problems here. One, the accuracy of the classifier is low. The other, the classifier runs too slow.
There seems to be no problem with the reference that you used. The steps seem accurate, and I have personally tried them in that order and managed to get good results.
As #Micka mentions, nPos around 90% of the original sample count is good enough. minHitRate is a parameter that you can change. Did you observe the numbers that are displayed while training? How was the accuracy improving, and did your classifier stop training (or are you using the trained parameters before learning ends?)?
For the low speed in detection, the most likely reason is that your training data did not have simple features to learn quickly. Did you trying detection on the data that you used for training? How were the results in that case? Compiler settings or high image resolution can be a problem too, but if you tried the same inputs and settings with other classifiers, this is unlikely.
If you like tor try a different approach (and have a GPU), YOLO V2 should be much faster and more accurate for this task.
I have been using cascade classifier to train some kind of plants. Here is a sample image for what I want to detect
I sampled the little green plants for positives, and made negatives out of images with similar background and no green plants (as suggested by many sources). Used many images similar to this one for sampling.
I did not have a lot of training data so of course I did not expect some idealistic classification results.
I have set the usual parameters min_hit_rate 0.95 max_false_alarm 0.5 etc. I have tried training with 5,6,7,8,9 and 10 stages. The strange thing that happens to me is that during the training process I get hit rate of 1 during all stages, and after 5 stages I get good acceptance ratio 0.004 (similar for later stages 6,7,8...).
I tried testing my classifier on the same image which I used for the training samples and there is very illogical behavior:
the classifier detects almost everything BUT the positive samples i took from it (the same samples in the training with HIT RATION EQUAL TO 1).
the classifier is really but really slow it took over an hour for single input image (down-sampled scale factor 1.1).
I do not get it how could the same samples be classified as positives during training (through all the stages) and then NONE of it as positive on the image (there are a lot of false positives around it).
I checked everything a million times (I thought that I somehow mixed positives and negatives but I did not).
Can someone help me with this issue?
I can try and help but of course I can't train this thing for you unless you send me your images.
In my experience if you aren't getting the desired results, you are simply giving traincascade the wrong or not enough images (either or both positives or negatives).
I did not get great results until I created an annotation file using the built-in opencv_annotation tool. Have you done that? How many positives?
Did your negatives contain the background that you are attempting to detect your object in? This is key and can't be overlooked.
Also, I would use LBP, it's much faster.
If you or anyone is still stuck and have some positives created, send them to me and I'll see if I can train this thing.
And also, I have written hopefully a one-stop tutorial about this stuff after my experiences with it:
http://johnallen.github.io/opencv-object-detection-tutorial/
I'm facing a text classification problem, and I need to classify examples to 34 groups.
The problem is, the size of training data of 34 groups are not balanced. For some groups I have 2000+ examples, while for some I only have 100+ examples.
For some small groups, the classification accuracy is quite high. I guess those groups may have specific key words to recognize and classify. While for some, the accuracy is low, and the prediction always goes to large groups.
I want to know how to deal with the "low frequency example problem". Would simply copy and duplicate the small group data work? Or I need to choose the training data and expand and balance the data size? Any suggestions?
Regularization can sometimes help imbalanced class problems by reducing the effect of spurious correlation, but that depends on your data. One solution is to simply over-sample the smaller classes, or increase the weights of the data points in the smaller classes to force the classifier to pay more attention to it.
You can find more advanced techniques by searching for "class imbalance" problems. Though not as many of them have been applied / created for text classification problems, as it is very common to have huge amounts of data when working with text problems. So I'm not sure how many work well in such high dimensional space.