Makefile.config for shallow0
Added pickle_to_leveldb.py in /examples to convert from cuda-convnet data batches
ignore DS_Stores
No more DS_Stores
1. Refactored command line args parsing in train_net. Added the GPU device id option.
2. Tweaked the makefile
refactored test_net command line arguments, works on LeNet
1. Added codelite project
2. Data layer re-formatting, added some comments
1. Added Eclipse nSight project
2. Ran source code formatter on the whole project
removed codelite workspace
another pass of formatting
1. Added BGR->RGB conversion before saving to leveldb.
2. ImageNet experiments are operational.
1. Improved optimisation flags in the makefile
2. Added batch prep/processing timing
leftovers from the last commit
more leftovers
1. The first (untested) implementation of the file system data provider (loads from *.jpg files, processing using OpenCV).
2. Fixed optimisation options in the Makefile.
1. Added the phase attribute to the network and layer classes (TRAIN/TEST).
2. Added visualisation to the data prefetching procedure.
3. Added build config for profiling.
1. Added boost random generator to Caffe.
2. Added RGB jittering.
1. Added parameters: snapshot history length, number of iteration to resume from, learning rate multiplier.
2. Added validation error saving and visualisation.
1. Refactoring of Caffe initialisation - GPU #0 utilisation bug fixed.
2. Added a launcher script in Python to restart training from the last checkpoint, if it crashes.
moved drivers out of the repo