training the UT model on challenge-sets
how to run imagenet training
changes to model initialization and learning parameters
fixed problem with the general-cropped dataprovider. plus updates to UT model spec
changes to UT model params, and general-cropped dataprovider
dropout version of UT model
alter data provider to prevent it from loading all batches into memory upon initialization. this makes each minibatch take
about 40% longer, but is necessary for running larger datasets (we can't reasonably load 2Tb of images into memory). a more intelligent
caching mecahnism (loading X number of batches into memory at a time) would remove this performance hit, but might not be enough
to justify the added code complexity...
changed dataprovider pre-processing to remove the need to have batches written out on disk
current status of code
more updates/files
movie statistic correlations vs imagenet performance
movie stats... second try at committing
update to most recent model config
filter stats analysis
Merge branch 'imagenet_training_test' of github.com:dicarlolab/archconvnets into imagenet_training_test
remove dataprovider original code
small changes
integrating some of darrens changes
simplyfying data.py
initial commit of new DLDataProvider