Keras has preprocessing.image.flow_from_directory() to read the gray scale and rgb image formats. Is there some way i can read HDR images with 4 channels ('rgbe') using keras or similar library? Any ideas will be appreciated.
Thank you in advance.
The function preprocessing.image.flow_from_directory() is a very powerful one. Sadly it has only the two modes you mentioned. I would suggest you two things since there is not a similar library that could work for you:
Go from RGBE to RGB and use preprocessing.image.flow_from_directory()
Checkout this Github link. They talk about keras having preprocessing with 4 channels, I suggest you update keras.
If you want to use the E value, because you think it will have importance in your net, just build your own reader. This might help.
Related
I use Faster RCNN to classify 33 items. But most of them are misclassified among each other. All items are snack packets and sweet packets like in the link below.
https://redmart.com/product/lays-salt-and-vinegar-potato-chips
https://www.google.com/search?q=ice+breakers&source=lnms&tbm=isch&sa=X&ved=0ahUKEwj5qqXMofHfAhUQY48KHbIgCO8Q_AUIDigB&biw=1855&bih=953#imgrc=TVDtryRBYCPlnM:
https://www.google.com/search?biw=1855&bih=953&tbm=isch&sa=1&ei=S5g-XPatEMTVvATZgLiwDw&q=disney+frozen+egg&oq=disney+frozen+egg&gs_l=img.3..0.6353.6886..7047...0.0..0.43.116.3......1....1..gws-wiz-img.OSreIYZziXU#imgrc=TQVYPtSi--E7eM:
So color and shape are similar.
What could be the best way to solve this misclassification problem?
Fine tuning is a way to use features, learned on some big dataset, in our problem, which means instead of training the complete network again, we freeze out weights of the lower layer of the network and add few layers at the end of network, as per requirement. Now we train it on our data-set again. So the advantage here is that, we don't need to train all-millions of parameters, but few only. Another is that we don't need large-dataset to fine-tune.
More you can find here. This is another-useful resource, where author has explained this in more detail(with code).
Note: This is also known as transfer-learning.
My problem statement is as follows :
" Object Detection and Localization using Tensorflow and convolutional neural network "
What i did ?
I am done with the cat detection from images using tflearn library.I successfully trained a model using 25000 images of cats and its working fine with good accuracy.
Current Result :
What i wanted to do?
If my image consist of two or more than two objects in the same image for example cat and dog together so my result should be 'cat and dog' and apart from this i have to find the exact location of these two objects on the image(bounding box)
I came across many high level libraries like darknet , SSD but not able to get the concept behind it.
Please guide me about the approach to solve the problem.
Note : I am using supervised learning techniques.
Expected Result :
You have several ways to go about it.
The most straight forward way is to get some suggested bounding boxes using some bounding box suggestion algorithm like selective search and run on each on of the suggestion the classification net that you already trained. This approach is the approach taken by R-CNN.
For more advanced algorithm based on the above approach i suggest you read about Fast-R-CNN and Faster R-CNN.
Look at Object detection with R-CNN? for some basic explanation.
Darknet and SSD are based on a different approach if you want to undestand them you can read about them on
http://www.cs.unc.edu/~wliu/papers/ssd.pdf
https://pjreddie.com/media/files/papers/yolo.pdf
Image localization is a complex problem with many different implementations achieving the same result with different efficiency.
There are 2 main types of implementation
-Localize objects with regression
-Single Shot Detectors
Read this https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/content/object_localization_and_detection.html to get a better idea.
Cheers
I have done a similar project (detection + localization) on Indian Currencies using PyTorch and ResNet34. Following is the link of my kaggle notebook, hope you find it helpful. I have manually collected images from the internet and made bounding box around them and saved their annotation file (Pascal VOC) using "LabelImg" annotation tool.
https://www.kaggle.com/shweta2407/objectdetection-on-custom-dataset-resnet34
There is a set of images, each of which contains different shape entities, such as shown in the following figure. I am trying to localize and recognize these different shapes. For instance, adding a bounding box for each different shape and maybe even label it. What are the major research papers/deep learning models that have been able to solve this kind of problem?
Object detection papers such as rcnn, faster rcnn, yolo and ssd would help you solve this if you were bent on using a deep learning approach.
It’s easy to say this is a trivial problem that can be solved with tools in OpenCV and deep learning is overkill, but I can see many reasons to use deep learning tools and that does not answer your question.
We assume that your shapes has different scales and rotations. Actually your main image shown above is very large for training process and it needs a lot of training samples to generate a good accuracy at the end on test samples. In this case it is better to train a Convolutional Neural Network on a short images (like 128x128) with only one shape per each image and then use slide trick!
This project will have three main steps:
Generate test and train samples, each image should have only one shape
Train a classifier to recognize a single shape within each input image
Use slide trick! Break your original image containing many shapes to overlapping blocks of size 128x128. Pass each block to your model trained in the second step.
In this way at the end you will have label for each shape from your trained model, and also you will have location of each shape using slide trick.
For the classifier you can use exactly CNN structure of Tensorflow's MNIST tutorial.
Here is a paper with exactly same method applied to finger print images to extract local features.
A direct fingerprint minutiae extraction approach based on convolutional neural networks
I am a beginner with Tensorflow and machine learning in general.For my project I have to classify urban sound data.
I have extracted mfccs of my sample data and now I want to classify them by using a CNN in Tensorflow. I don't know how many channels I should use and why. Can anyone help me? Thanks.
I was working on a similar project and I found this paper useful with this.
http://karol.piczak.com/papers/Piczak2015-ESC-ConvNet.pdf
This is a newbie question for the tensorflow experts:
I reading lot of data from power transformer connected to an array of solar panels using arduinos, my question is can I use tensorflow to predict the power generation in future.
I am completely new to tensorflow, if can point me to something similar I can start with that or any github repo which is doing similar predictive modeling.
Edit: Kyle pointed me to the MNIST data, which I believe is a Image Dataset. Again, not sure if tensorflow is the right computation library for this problem or does it only work on Image datasets?
thanks, Rajesh
Surely you can use tensorflow to solve your problem.
TensorFlow™ is an open source software library for numerical
computation using data flow graphs.
So it works not only on Image dataset but also others. Don't worry about this.
And about prediction, first you need to train a model(such as linear regression) on you dataset, then predict. The tutorial code can be found in tensorflow homepage .
Get your hand dirty, you will find it works on your dataset.
Good luck.
You can absolutely use TensorFlow to predict time series. There are plenty of examples out there, like this one. And this is a really interesting one on using RNN to predict basketball trajectories.
In general, TF is a very flexible platform for solving problems with machine learning. You can create any kind of network you can think of in it, and train that network to act as a model for your process. Depending on what kind of costs you define and how you train it, you can build a network to classify data into categories, predict a time series forward a number of steps, and other cool stuff.
There is, sadly, no short answer for how to do this, but that's just because the possibilities are endless! Have fun!