1.7 KiB
Training new models
This repository also contains our training infrastructure to promote an open ecosystem and enable quicker bootstrapping for new research and development. Warning: Training is computationally expensive and takes a few weeks on our Tesla K40 GPU. Because of this, the training code assumes CUDA is installed.
A rough overview of training is:
1. Create raw image directory.
Create a directory for your raw images so that images from different
people are in different subdirectories. The names of the labels or
images do not matter, and each person can have a different amount of images.
The images should be formatted as jpg or png and have
a lowercase extension.
$ tree data/mydataset/raw
person-1
├── image-1.jpg
├── image-2.png
...
└── image-p.png
...
person-m
├── image-1.png
├── image-2.jpg
...
└── image-q.png
2. Preprocess the raw images
Change 8 to however many
separate processes you want to run:
for N in {1..8}; do ./util/align-dlib.py <path-to-raw-data> align affine <path-to-aligned-data> --size 96 &; done.
Prune out directories with less than N (I use 10) images
per class with ./util/prune-dataset.py <path-to-aligned-data> --numImagesThreshold <N> and
then split the dataset into train and val subdirectories
with ./util/create-train-val-split.py <path-to-aligned-data> <validation-ratio>.
3. Train the model
Run training/main.lua to start training the model.
Edit the dataset options in training/opts.lua or
pass them as command-line parameters.
This will output the loss and in-progress models to training/work.
4. Analyze training
Visualize the loss with training/plot-loss.py.