Convolution Neural Network for image detection/classification

Question

So here is there setup, I have a set of images (labeled train and test) and I want to train a conv net that tells me whether or not a specific object is within this image.

To do this, I followed the tensorflow tutorial on MNIST, and I train a simple conv net reduced to the area of interest (the object) which are training on image of size 128x128. The architecture is as follows : successively 3 layers consisting of 2 conv layers and 1 max pool down-sampling layers, and one fully connected softmax layers (with two class 0 and 1 whether the object is present or not)

I impleted it using tensorflow, and this works quite well, but since I have enough computing power I was wondering how I could improve the complexity of the classification: - adding more layers ? - adding more channel at each layer ? (currently 32,64,128 and 1024 for the fully connected) - anything else ?

But the most important part is that now I want to detect this same object on larger images (roughle 600x600 whereas the size of the object should be around 100x100).

I was wondering how I could use the previously training "small" network used for small images, in order to pretrained a larger network on the large images ? One option could be to classify the image using a slicing window of size 128x128 and scan the whole image but I would like to try if possible to train a whole network on it.

Any suggestion on how to proceed ? Or an article / ressource tackling this kind of problem ? (I am really new to deep learning so sorry if this is stupid question...)

Thanks !

Answer 1

I suggest that you continue reading on the field overall. Your search keys include CNN, image classification, neural net, AlexNet, GoogleNet, and ResNet. This will return many articles, on-line classes and lectures, and other materials to help you learn about classification with neural nets.

Don't just add layers or filters: the complexity of the topology (net design) must be fitted to the task; a net that's too complex will over-fit the training data. The one you've been using is probably LeNet; the three I cite above are for the ImageNet image classification contest.

Answer 2

Since you are working on images, I would suggest you to use a pretrained image classification network (like VGG, Alexnet etc.)and fine tune this network with your 128x128 image data. In my experience until we have very large data set fine tuned network will give more accuracy and also save training time. After building a good image classifier on your data set you can use any popular algorithm to generate region of proposal from the image. Now take all regions of proposal and pass them to classification network one by one and check weather this network is classifying given region of proposal as positive or negative. If it classifying as positively then most probably your object is present in that region. Otherwise it's not. If there are a lot of region of proposal in which object is present according to classifier then you can use non maximal suppression algorithms to reduce number of positive proposals.