I have a trained classifier for identifying if the image is of football or tennis. Is it possible to give a feedback to the existing positive examples rather than by adding a new class? Can the classifier be improved by adding images to the zip file and updating the classifier or is there a better approach to achieve this?
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So, I am using word2vec in Java, and trying to train it somehow so that it gives me vector representation for words and sentences.
Can I use this for feeding input into a neural network, to get a response on the basis of the word2vec data? I am planning to make a chatbot with the help of this.
Adding on to #galloguille's comments, you can use pre-trained word2vec's word-vectors to initialize your RNN. RNN can learn from sequence of words to predict next word(s). A good example with code for this, you can find here - https://github.com/larspars/word-rnn.
There is good collection of current state of the art on chatbots here - https://stanfy.com/blog/the-rise-of-chat-bots-useful-links-articles-libraries-and-platforms/
From my understanding, most effective chatbots don't use RNN directly (at present) to reply to a question, but try to predict intent (from a fixed set of intents) of the question in the first step. Based on each intent, they calculate some actionable insights and a logical reply to the question.
After several month working with caffe, I've been able to train my own models successfully. For example further than my own models, I've been able to train ImageNet with 1000 classes.
In my project now, I'm trying to extract the region of my interest class. After that I've compiled and run the demo of Fast R-CNN and it works ok, but the sample models contains only 20 classes and I'd like to have more classes, for example all of them.
I've already downloaded the bounding boxes of ImageNet, with the real images.
Now, I've gone blank, I can't figure out the next steps and there's not a documentation of how to do it. The only thing I've found is how to train the INRIA person model, and they provide dataset + annotations + python script.
My questions are:
Is there maybe any tutorial or guide that I've missed?
Is there already a model trained with 1000 classes able to classify images and extract the bounding boxes?
Thank you very much in advance.
Regards.
Rafael.
Dr Ross Girshik has done a lot of work on object detection. You can learn a lot from his detailed git on fast RCNN: you should be able to find a caffe branch there, with a demo. I did not use it myself, but it seems very comprehensible.
Another direction you might find interesting is LSDA: using weak supervision to train object detection for many classes.
BTW, have you looked into faster-rcnn?
GOOD MORNING COLLEAGUES!
I am very into train a new model from my own data set of faces!
I have found no information about this topic, then I hope my information could help people and I can get some answers as well.
I will try to explain the steps I have needed to do to train my own model and later on some questions...
I have download the Latent code from: http://cs.brown.edu/~pff/latent-release4/
I have download the PASCAL VOC 2008 code (devkit) from: http://host.robots.ox.ac.uk/pascal/VOC/voc2008/index.html
I have emulate the structure of files/folders of the VOC PASCAL but in my own data set:
Annotations. I have created a .xml where I have defined a object, face, (in each image I only have one face). I didn't define difficulties or poses...
JPEGImages where I have stored all the images
ImageSets where I have defined three files:
test.txt, where I wrote the file name of my positive samples
train.txt, where I wrote the file name of my negative samples
trainval.txt, where I wrote the file name of my positive samples (exactly the same file than test.txt).
I have change some things in globals.m and VOCinit.m (to tell the algorithm the path and the location of some files...)
Then I run the training with the command: pascal('face', 1);
Following these steps I have achieved that the training run completely and doesn't fail and I get my own model BUT I have some doubts...
Can you see anything weird in my explanation? Could it work?
Must the files test.txt/trainval.txt be equal? Why... What does it mean?
Do I have to choose the number of parts I want in the model INSIDE the function?
Please, you imagine I have two kind of samples (frontal faces and side faces) and I want to detect both... How can I address this issue? I thought I have to train a model with two components... but How can I tell to the training code which are frontal or side samples?? In the annotations with the label pose?? (I don't think so...) Are there other way to handle this purpose?
Thank you for your time!!
I hope you can solve my doubts :)
I think test.txt should contain samples (images) that will be used to estimate how good the system is after learning the faces. However, trainval.txt is used during the learning stage (training) to fine-tune the parameters of the model; it is an essential part of supervised learning.
Also, it is very hard to have one single SVM to classify faces that are both frontal and sideways. Here is my suggestion:
Train one SVM to detect if the input image is a frontal face or a sideways face. Call this something like SVM-0.
Train another SVM for frontal faces. This SVM will classify all your individuals. Note, however, that SVM is usually a binary classifier, so make sure you choose the right SVM, one that as a multiclass architecture. Call this SVM-F.
Tran a final SVM for sideways faces. Again, use a multiclass SVM. Call it SVM-S.
Present the input image to SVM-0 and if it detects it is a frontal face, present the input again to SVM-F; otherwise, give the input to SVM-S.
In my experience, you should expect very low performance in SVM-S. It is a hard problem to solve. But frontal faces is not a big deal, unless you are working with faces that vary in pose, illumination, and expression (PIE). Face recognition is affected greatly with PIE variations in the images.
I recommend you this website, it contains very good information and tutorials for starters, with or without experience.
I'm implementing an character recognition system with Hidden Markov Model(HMM). I have used skeleton to extract features of image. And I thought to use HMM for training images.
My question is how I can give those features to HMM? I got to know that I have to save those features into a file and then that file should feed to the HMM.
Can someone please help me? I am stuck here for two months. Still, I couldn't find the solution for this.
Appreciate your help a lot.
I was passing by and just saw this question.
Maybe you looked somewhere because your question is almost a month ago.
You give the features to HMM by clustering your data, you can use k-means, or you can use windows with lengths. If you use k-means, you will obtain the centers, you can use the centers to obtain the features, after this you need to crossfold validation to see if it really learns the features you labeled. Also K-means gives you the states and the initial transition probabilities.
Hope this helps you
I'm building image classifier that uses DBN for feature learning and logistic regression to fine-tune resulting network. Normally, the most convenient way to implement such an architecture in SciKit Learn is to use Pipeline class. But in my case I have ~10K unlabeled images and only ~300 labeled ones. Surely, I want to use all images to train DBN and fit logistic regression with only labeled examples.
I can think of implementing my own Pipeline class that will handle this case, but first I'd like to know if there's already something existing. Is it?
The current scikit-learn Pipeline API is not well suited for supervised learning with unsupervised pre-training. Implementing your own wrapper class is probably the best way to go forward for that case.