Best available data sets and software to compare accuracy between homemade and professional ANNs / feedfoward neural networks

Question

I have a couple slightly modified / non-traditional setups for feedforward neural networks which I'd like to compare for accuracy against the ones used professionally today. Are there specific data sets, or types of data sets, which can be used as a benchmark for this? I.e. "the style of ANN typically used for such-and-such a task is 98% accurate against this data set." It would be great to have a variety of these, a couple for statistical analysis, a couple for image and voice recognition, etc.

Basically, is there a way to compare an ANN I've put together against ANNs used professionally, across a variety of tasks? I could pay for data or software, but would prefer free of course.

Answer 1

CMU has some benchmarks for neural networks: Neural Networks Benchmarks

The Fast Artificial Neural Networks library (FANN) has some benchmarks that are widely used: FANN. Download the source code (version 2.2.0) and look at the directory datasets, the format is very simple. There is always a training set (x.train) and a test set (x.test). At the beginning of the file is the number of instances, the number of inputs and the number of outputs. The next lines are the input of the first instance and the output of the first instance and so on. You can find example programs with FANN in the directory examples. I think they even had detailed comparisons to other libraries in previous versions.

I think most of FANN's benchmarks if not all are from Proben1. Google for it, there is a paper from Lutz Prechelt with detailed descriptions and comparisons.