Awesome
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 arebasic
,kmeans-refine
,kmeans-refine-radius
, andkmeans-refine-mask
. - Possible
{DATASET}
options areomniglot
,mini-imagenet
,tiered-imagenet
. - Use
{LABEL_RATIO}
0.1 foromniglot
andtiered-imagenet
, and 0.4 formini-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 testingmini-imagenet
andtiered-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 areomniglot
,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 arebasic
,kmeans-refine
,kmeans-refine-radius
, andkmeans-refine-mask
. - Possible
DATASET
options areomniglot
,mini_imagenet
,tiered_imagenet
. - Use
{LABEL_RATIO}
0.1 foromniglot
andtiered-imagenet
, and 0.4 formini-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},
}