It took nearly 10 days for Talos to search possible models using the 768 item vector representing the colour histogram for each composition. The best validation accuracy listed by the search was 68.5% and the best model 66.2%. The best model achieved a training accuracy of 77.9%. 465 bad compositions were predicted to be bad, 294 bad compositions were predicted to be good, 232 good compositions were predicted to be bad and 568 good compositions were predicted to be good.
This is a very minor improvement from the variance features. The low training accuracy indicates there may not be enough epochs for such a large dimensional vector. I’m now running a second experiment where the 768 bin (256 bins per channel) histogram is reduced to a 96 bins (32 bins per channel). This is more comparable to the initial 57 element training vectors. If the problem is the size of the vector, this should allow for higher training accuracy and I hope, also better generalization in the next search.