If you are training neural networks or experimenting with natural image statistics, or even just making art, then you may want a database of natural images.
I generated an image patch database that contains 500,000 28×28 or 64×64 sized monochrome patches that were randomly sampled from 5000 representative natural images, including a mix of landscape, city, and indoor photos. I am offering them here for download from Dropbox. There are two files:
image_patches_28x28_500k_nofaces.dat (334MB compressed)
image_patches_64x64_500k_nofaces.dat (1.66GB compressed)
The first file contains 28×28 pixel patches and the second one contains 64×64 patches. The patches were sampled from a corpus of personal photographs at many different locations and uniformly in log scale. A concerted effort was made to avoid images with faces, so that these could be used as a non-face class for face detector training. However there are occasional faces that have slipped through but the frequency is less than one in one thousand. Continue reading
In my last blog post I talked about trying out my code for training neural nets on a simple one-layer network which consists of a single weight layer and a softmax output. In this post I share results for training a fully connected two-layer network.
In this network, the input goes from 28×28 image pixels down to 50 hidden units. Then there is a rectified linear activation function. The second layer goes from the 50 hidden units down to 10 units, and finally there is the softmax output stage for classification.
When I train this network on the MNIST handwriting dataset I get a test error rate of 2.89% which is pretty good and actually lower than other results quoted on the MNIST web site. It is interesting to inspect the patterns of the weights for the first layer below (here I organized the weights for the 50 hidden units as a 10×5 matrix):