This repository is the official implementation of NeurIPS'21 paper, Meta Internal Learning by Raphael Bensadoun, Shir Gur, Tomer Galanti, Lior Wolf.
To install requirements:
pip install -r requirements.txt
- Create a folder X containing the images. (see structure in data folder)
- Determine how many iterations Y to train by scale (depends mostly on the size of the dataset, you may refer to the appendix for reference).
- Run
python train.py --image-path X --batch-size 16 --visualize --niter Y --min-size 25 --checkname X_result --SAVE-MODEL --SAVE-IMGS
Generated images, tensorboard logs and trained models are stored in MetaInternalLearning/run/X_result.
The default input format is jpg. Use '--file-suffix png' for .png files.
Places-50 -
python train.py --image-path data/places_50 --batch-size 16 --visualize --niter 4000 --min-size 28 --checkname places_50_result --SAVE-MODEL --SAVE-IMGS
LSUN-50
python train.py --image-path data/lsun_50 --batch-size 16 --visualize --niter 5000 --checkname lsun_50_result --SAVE-MODEL --SAVE-IMGS
Valley dataset can be downloaded here - http://places2.csail.mit.edu/download.html (256x256 small images) and can be divided into subsets as mentioned in the paper.
V500 -
python train.py --image-path data/V500 --batch-size 16 --visualize --niter 25000 --min-size 25 --checkname v500_result --ar 1 --SAVE-MODEL
V2500 -
python train_dataset_parallel.py --image-path data/V2500 --batch-size 16 --niter 100000 --rec-weight 50 --min-size 25 --checkname v2500_result --ar 1 --SAVE-MODEL
V5000 -
python train_dataset_parallel.py --image-path data/V5000 --batch-size 16 --niter 150000 --rec-weight 50 --min-size 25 --checkname v5000_result --ar 1 --SAVE-MODEL