I need to diagnosis captcha for a project. I did this using the object_detection provided by Tensorflow.
also, I added 500 captcha samples by turning images into XML by LabelImg and then to TFRecord.
beside I used "faster_rcnn_inception_v2_coco_2018_01_28"
The problem is that the accuracy of the machine is very low.
My questions are:
Can the problem be solved by increasing the number of training data?
Should I change my algorithm?
How effective is the use of the Yolo 3 instead of the detection object provided by Tensorflow?
Q. Can the problem be solved by increasing the number of training data?
A. It would be depend on how many data you can get more. I think that only increasing the number of training data is not good approach.
Consider using Fine-tuning existing trained model to detect object class. If you want to fine-tune the model, you need to be careful class label assignment because existing trained model like YOLO3, Faster RCNN, etc. has no label "captcha" in their training dataset.
I recommend you to refer to this website that can help you to fine-tune the model.
Q. Should I change my algorithm?
A. Do as you wish.
Q. How effective is the use of the Yolo 3 instead of the detection object provided by Tensorflow?
A. In my opinion, two different models are much the same if you don't need to consider inference time.
Related
I want to do multi-class classification with a pre-trained resnet50 with contrastive loss. I want to calculate the loss between the actual label and the label predicted by the model. But while searching, I came across some questions.
first, should I necessarily use supervised contrastive learning to use contrastive loss? The method is explained in this link. It seems that it first generates more data from the initial data and then calculates the loss.
secondly, there is also some prepared function like this, But I do not know how to use them. or do you know other prepared functions?
Thank you in advance.
I am a total beginner to ML and Neural networks. I am currently working on a project where I have a lot of pictures stored in a MongoDB database. Each one of those pictures has a number from 0 to 1. For example "picture 1" 0.71.
I want to train my model given the database. The main goal for the project is that after the model is finished and trained, given an image the model will be able to return(predict) a number from 0 to 1. After doing some research and asking a few people I figured out some libraries that would be useful for the project are: Tenserflow and Keras. Some people told me that it is impossible, but I'm not sure therefore I came to ask here.
So my questions are: Is it possible? If so, how can I implement it? Are there any specific tools you recommend? If you specify a way that I should use for my project do I need to export my MongoDB database in a certain form? Since I am a beginner maybe there are some tutorials that you think that can help?
I'm sorry if this question is a bit too general, if there are any misunderstandings please comment and I will try to answer.
Thanks in advance!
What you want to do is totally feasible, this kind of project is called regression, since you are using images data the best type of models are called convolutional neural network (CNN), you'll need some understanding if you want to build your own model. I've done a project where I had to predict a number of bacterial colonies using an image, much like your problem except that I had no boundaries on the predicted values.
What is a CNN ? Here is a link
Basically a CNN will understand the features in the images and will use those features to predict a value.
You won't need to create your own model, most people just use well-designed one in the scientific litterature.
Go for keras, it's the easiest framework out there and work like a charm. Here is how to implement VGG16 (an architecture that is probably the best for your problem) : link
You should follow this tutorial to get going on developing with keras.
Last hint: don't use the same last layer as the one on the VGG16 implementation, use a Dense Layer with one neuron and with a sigmoid/linear/leaky relu activation.
ie:
#model.add(Dense(1000, activation='softmax'))
model.add(Dense(1, activation='sigmoid'))
This means : predict 1 number (sigmoid will bound it between 0 and 1, but maybe lrelu or linear is better)
Also, I guess you could use MongoDB to read the images as arrays, but I would just put the images on a folder.
Edit : When compiling the model, use a mean squared error as in
adam = keras.optimizers.Adam(lr=1e-4)
model.compile(optimizer=adam, loss='mse')
Here you have the "hello world program" in terms of neural networks and digits classification. You can start studying it because I think you will end up with a similar architecture for your NN. What you should focus on is the output of your model, because in this example they are performing classification on 10 classes (digits from 0 to 9) but you are trying to read a real number. You could try to use a single neurone with sigmoid or linear activation at the end of your model.
I am trying to build cnn model (keras) that can classify image based on users emotions. I am having issues with data. I have really small data for training. Will augmenting data help? Does it improve accuracy? In which case one should choose to augment data and should avoid?
Will augmenting data help? Does it improve accuracy?
That's hard to say in advance. But almost certainly, when you already have a model which is better than random. And when you choose the right augmentation method.
See my masters thesis Analysis and Optimization of Convolutional Neural Network Architectures, page 80 for many different augmentation methods.
In which case one should choose to augment data and should avoid?
When you don't have enough data -> augment
Avoid augmentations where you can't tell the emotion after the augmentation. So in case of character recognition, rotation is a bad idea (e.g. due to 6 vs 9 or u vs n or \rightarrow vs \nearrow)
Yes, data augmentation really helps, and sometimes it's really necessary. (But take a look at Martin Thoma's answer, there are more details there and some important "take-cares").
You should use it when:
You have too little data
You notice your model is overfitting too easily (may be a model too powerful too)
Overfitting is something that happens when your model is capable of memorizing the data. Then it gets splendid accuracy for training data, but terrible accuracy for test data.
Increasing the size of training data will make it more difficult for your model to memorize. Small changes here and there will make your model stop paying attention to details that don't mean anything (but are capable of creating distinctions between images) and start paying attention to details that indeed cause the desired effect.
I want to use pre-trained model for the face identification. I try to use Siamese architecture which requires a few number of images. Could you give me any trained model which I can change for the Siamese architecture? How can I change the network model which I can put two images to find their similarities (I do not want to create image based on the tutorial here)? I only want to use the system for real time application. Do you have any recommendations?
I suppose you can use this model, described in Xiang Wu, Ran He, Zhenan Sun, Tieniu Tan A Light CNN for Deep Face Representation with Noisy Labels (arXiv 2015) as a a strating point for your experiments.
As for the Siamese network, what you are trying to earn is a mapping from a face image into some high dimensional vector space, in which distances between points reflects (dis)similarity between faces.
To do so, you only need one network that gets a face as an input and produce a high-dim vector as an output.
However, to train this single network using the Siamese approach, you are going to duplicate it: creating two instances of the same net (you need to explicitly link the weights of the two copies). During training you are going to provide pairs of faces to the nets: one to each copy, then the single loss layer on top of the two copies can compare the high-dimensional vectors representing the two faces and compute a loss according to a "same/not same" label associated with this pair.
Hence, you only need the duplication for the training. In test time ('deploy') you are going to have a single net providing you with a semantically meaningful high dimensional representation of faces.
For a more advance Siamese architecture and loss see this thread.
On the other hand, you might want to consider the approach described in Oren Tadmor, Yonatan Wexler, Tal Rosenwein, Shai Shalev-Shwartz, Amnon Shashua Learning a Metric Embedding for Face Recognition using the Multibatch Method (arXiv 2016). This approach is more efficient and easy to implement than pair-wise losses over image pairs.
I am trying to detect the faces using the Matlab built-in viola jones face detection. Is there anyway that I can combine two classification models like "FrontalFaceCART" and "ProfileFace" into one in order to get a better result?
Thank you.
You can't combine models. That's a non-sense in any classification task since every classifier is different (works differently, i.e. different algorithm behind it, and maybe is also trained differently).
According to the classification model(s) help (which can be found here), your two classifiers work as follows:
FrontalFaceCART is a model composed of weak classifiers, based on classification and regression tree analysis
ProfileFace is composed of weak classifiers, based on a decision stump
More infos can be found in the link provided but you can easily see that their inner behaviour is rather different, so you can't mix them or combine them.
It's like (in Machine Learning) mixing a Support Vector Machine with a K-Nearest Neighbour: the first one uses separating hyperplanes whereas the latter is simply based on distance(s).
You can, however, train several models in parallel (e.g. independently) and choose the model that better suits you (e.g. smaller error rate/higher accuracy): so you basically create as many different classifiers as you like, give them the same training set, evaluate each accuracy (and/or other parameters) and choose the best model.
One option is to make a hierarchical classifier. So in a first step you use the frontal face classifier (assuming that most pictures are frontal faces). If the classifier fails, you try with the profile classifier.
I did that with a dataset of faces and it improved my overall classification accuracy. Furthermore, if you have some a priori information, you can use it. In my case the faces were usually in the middle up part of the picture.
To further improve your performance, without using the two classifiers in MATLAB you are using, you would need to change your technique (and probably your programming language). This is the best method so far: Facenet.