Pretrained models are available here: https://uofi.box.com/s/zap2p03tnst9dfisad4u0sfupc0y1fxt
Datasets in PyTorch format are available here: https://uofi.box.com/s/ixncr3d85guosajywhf7yridszzg5zsq
The PyTorch-friendly Places365 dataset can be downloaded from https://places2.csail.mit.edu/download.html
Place models in checkpoints/ and unzipped datasets in data/

Note that the numbers in the paper are averaged over multiple runs for each ordering of datasets. The pretrained models are for a specific dataset addition ordering: (c) CUBS Birds, (s) Stanford Cars, (f) Flowers.

These numbers were obtained by evaluating the models on a Titan X (Pascal).
Note that numbers on other GPUs might be slightly different (~0.1%) owing to cudnn algorithm selection.
https://discuss.pytorch.org/t/slightly-different-results-on-k-40-v-s-titan-x/10064

Python 2.7 or 3.xx
torch==0.2.0.post3
torchvision==0.1.9
torchnet (pip install git+https://github.com/pytorch/tnt.git@master)
tqdm (pip install tqdm)

Check out the scripts in src/scripts.
Run all code from the src/ directory, e.g. ./scripts/run_all.sh

cd src  # Run everything from src/
# Pruning-based models.
python main.py --mode eval --dataset cubs_cropped \
  --loadname ../checkpoints/csf_0.75,0.75,-1_vgg16_0.5-nobias-nobn_1.pt
# LwF models.
python lwf.py --mode eval --dataset cubs_cropped \
  --loadname ../checkpoints/csf_lwf.pt