I'm searching for 2-class convolutional neural network problem published as paper using smallest-dataset.
Generally CNN requires millions of images to train ,but I found it successfully works with hundreds to thousands of 2-class images through augmentation. I need to prove it by citing other best practices.
The similar case I found is "Mitosis detection in breast cancer histology images with deep neural networks" it trained on 190 positive samples and other images on the background. and it had quite successful result.
Is there another successful 2-class problem CNN research with small dataset?
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Deep learning is famous for classifying images into different categories. However, I am interested to use any other machine learning model which is capable of classifying the images. The images are about 2000 and are in png format. Does anybody know any machine learning model which can be applied in python to classify images other than Deep learning models.
You can take a look to SVMs (scikit-learn). I can advise you to extract features from images first, with SIFT or SURF for example.
EDIT: SIFT and SURF are using the principle of convolution, but it exists plenty of other feature descriptors.
I am doing thesis on baby cry detection, I build the model with CNN and KNN, the train accuracy of CNN is 99% and Test accuracy is 98% and for KNN, train accuracy is 98% and Test accuracy is 98%.
Please suggest me which algorithms I should choose and why?
In KNN, output completely relies on nearest neighbours, which may or may not be good choice. Also it is sensitive to distance metrics. More you can find here. And great discussion on its distance metrics can be helpful for you.
On the other hand, CNN extract the features from the input data. Which are very helpful for making analysis. And recent success in the CNN specially wavenet for the audio application, i will prefer to go with CNN.
Edit: Considering your data-size, CNN is not good option here.
I just followed this great tutorial about how to quickly retrain ImageNet and make image classifiers using Tensorflow. I made the classifier, and it works well. From what I understand, Tensorflow partitions the provided dataset into training, test and validation by itself - or at least it does with this script. I've worked with sklearn in the past, and you can always find the accuracy of the model.
My question is, how can I find the accuracy percentage of the trained model in Tensorflow, specifically for image classifiers?
Thanks very much.
I have a dataset of the order of MxN. I want to perform a binary classifcation on this dataset using neural networks. I was looking into Recurrent Neural Networks. Although, LSTM's can be used for AutoEncoders, I am not sure if they can be used for classification (I am trying to do a binary classification). I am very new to neural networks and deep learning models and i am not really sure if there is a way of achieving binary classification with neural networks. I tried Bernouli RBM on my dataset. I am not sure how to use this model to perform classification. I also found out Pipeline(). Again, I am not sure how to achieve my goal.
Any help would be greatly appreciated.
Ok, something doesn't stack up. If you have unlabelled data and you want to classify it you must take a look at K-Means (http://scikit-learn.org/stable/modules/clustering.html#k-means).
Regarding LSTMs classification: You run your input through the RNN layers and take the last output and feed it into some Conv / Fully-connected layers to take care of classification as you know it.
I am trying to work with MSRA-CFW dataset for face recognition. I have tried two algorithms for this dataset:
Eigenfaces and
FisherFaces
Eigenfaces, as expected, performed poorly (because of its sensitivity to variations in light and head orientation). However even FisherFaces did not give satisfactory results.
Which algorithm should I use for this dataset? Or maybe even for any other dataset. Would convolutional neural networks work in this setting ? Also LBP features are provided with this dataset. Would these be better than convolutional neural networks ?
Thanks
There is a recent face recognition library that I tested and which is based on a paper from Google. It has the best recognition rate from an open source ~92%)
https://cmusatyalab.github.io/openface/
Internally it uses convolutional neural networks model trained on 500K face.
The only problem is that it doesn't recognize an unknown person, and it sees it as the nearest face from the database.