Almost start from scratch every time launch an experiment. Write down a memo for some must-have components when build a new project. Also make a TODO list for my customize lib.
Never could make it the most solid experiment…
- A space saves:
- config file
- log file
- Module state & Optimizer state
A few more words:
network file should be a hard copy of defined network
config file should be only one file where saves all the customized config
log file should include all the details of what’s happening in the whole training procedure, e.g. config, peroid loss report, test results.
NOTE: should be a log module for logging and print to stdout, especially for Python 2.x
*NOTE: should be a module for state (module and optimizer) saving and reloading
-  logging: print and log
-  config: detectron fashion
-  state tool: load and save all states
TensorBoard or Visdom should be used.
-  TensorBoard fashioned logger
Process the data really kills the time…
-  dataset: from Fast R-CNN
-  data loader: from Fast R-CNN
-  pre-process image and related annotation