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few-shot-ssl-public

Code for paper Meta-Learning for Semi-Supervised Few-Shot Classification. [arxiv]

Dependencies

  • cv2
  • numpy
  • pandas
  • python 2.7 / 3.5+
  • tensorflow 1.3+
  • tqdm

Our code is tested on Ubuntu 14.04 and 16.04.

Setup

First, designate a folder to be your data root:

export DATA_ROOT={DATA_ROOT}

Then, set up the datasets following the instructions in the subsections.

Omniglot

[Google Drive] (9.3 MB)

# Download and place "omniglot.tar.gz" in "$DATA_ROOT/omniglot".
mkdir -p $DATA_ROOT/omniglot
cd $DATA_ROOT/omniglot
mv ~/Downloads/omniglot.tar.gz .
tar -xzvf omniglot.tar.gz
rm -f omniglot.tar.gz

miniImageNet

[Google Drive] (1.1 GB)

Update: Python 2 and 3 compatible version: [train] [val] [test]

# Download and place "mini-imagenet.tar.gz" in "$DATA_ROOT/mini-imagenet".
mkdir -p $DATA_ROOT/mini-imagenet
cd $DATA_ROOT/mini-imagenet
mv ~/Downloads/mini-imagenet.tar.gz .
tar -xzvf mini-imagenet.tar.gz
rm -f mini-imagenet.tar.gz

tieredImageNet

[Google Drive] (12.9 GB)

# Download and place "tiered-imagenet.tar" in "$DATA_ROOT/tiered-imagenet".
mkdir -p $DATA_ROOT/tiered-imagenet
cd $DATA_ROOT/tiered-imagenet
mv ~/Downloads/tiered-imagenet.tar .
tar -xvf tiered-imagenet.tar
rm -f tiered-imagenet.tar

Note: Please make sure that the following hardware requirements are met before running tieredImageNet experiments.

  • Disk: 30 GB
  • RAM: 32 GB

Core Experiments

Please run the following scripts to reproduce the core experiments.

# Clone the repository.
git clone https://github.com/renmengye/few-shot-ssl-public.git
cd few-shot-ssl-public

# To train a model.
python run_exp.py --data_root $DATA_ROOT             \
                  --dataset {DATASET}                \
                  --label_ratio {LABEL_RATIO}        \
                  --model {MODEL}                    \
                  --results {SAVE_CKPT_FOLDER}       \
                  [--disable_distractor]

# To test a model.
python run_exp.py --data_root $DATA_ROOT             \
                  --dataset {DATASET}                \
                  --label_ratio {LABEL_RATIO}        \
                  --model {MODEL}                    \
                  --results {SAVE_CKPT_FOLDER}       \
                  --eval --pretrain {MODEL_ID}       \
                  [--num_unlabel {NUM_UNLABEL}]      \
                  [--num_test {NUM_TEST}]            \
                  [--disable_distractor]             \
                  [--use_test]
  • Possible {MODEL} options are basic, kmeans-refine, kmeans-refine-radius, and kmeans-refine-mask.
  • Possible {DATASET} options are omniglot, mini-imagenet, tiered-imagenet.
  • Use {LABEL_RATIO} 0.1 for omniglot and tiered-imagenet, and 0.4 for mini-imagenet.
  • Replace {MODEL_ID} with the model ID obtained from the training program.
  • Replace {SAVE_CKPT_FOLDER} with the folder where you save your checkpoints.
  • Add additional flags --num_unlabel 20 --num_test 20 for testing mini-imagenet and tiered-imagenet models, so that each episode contains 20 unlabeled images per class and 20 query images per class.
  • Add an additional flag --disable_distractor to remove all distractor classes in the unlabeled images.
  • Add an additional flag --use_test to evaluate on the test set instead of the validation set.
  • More commandline details see run_exp.py.

Simple Baselines for Few-Shot Classification

Please run the following script to reproduce a suite of baseline results.

python run_baseline_exp.py --data_root $DATA_ROOT    \
                           --dataset {DATASET}
  • Possible DATASET options are omniglot, mini-imagenet, tiered-imagenet.

Run over Multiple Random Splits

Please run the following script to reproduce results over 10 random label/unlabel splits, and test the model with different number of unlabeled items per episode. The default seeds are 0, 1001, ..., 9009.

python run_multi_exp.py --data_root $DATA_ROOT       \
                        --dataset {DATASET}          \
                        --label_ratio {LABEL_RATIO}  \
                        --model {MODEL}              \
                        [--disable_distractor]       \
                        [--use_test]
  • Possible MODEL options are basic, kmeans-refine, kmeans-refine-radius, and kmeans-refine-mask.
  • Possible DATASET options are omniglot, mini_imagenet, tiered_imagenet.
  • Use {LABEL_RATIO} 0.1 for omniglot and tiered-imagenet, and 0.4 for mini-imagenet.
  • Add an additional flag --disable_distractor to remove all distractor classes in the unlabeled images.
  • Add an additional flag --use_test to evaluate on the test set instead of the validation set.

Citation

If you use our code, please consider cite the following:

  • Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle and Richard S. Zemel. Meta-Learning for Semi-Supervised Few-Shot Classification. In Proceedings of 6th International Conference on Learning Representations (ICLR), 2018.
@inproceedings{ren18fewshotssl,
  author   = {Mengye Ren and 
              Eleni Triantafillou and 
              Sachin Ravi and 
              Jake Snell and 
              Kevin Swersky and 
              Joshua B. Tenenbaum and 
              Hugo Larochelle and 
              Richard S. Zemel},
  title    = {Meta-Learning for Semi-Supervised Few-Shot Classification},
  booktitle= {Proceedings of 6th International Conference on Learning Representations {ICLR}},
  year     = {2018},
}