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https://github.com/renmengye/few-shot-ssl-public
Meta Learning for Semi-Supervised Few-Shot Classification
https://github.com/renmengye/few-shot-ssl-public
Last synced: about 2 months ago
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Meta Learning for Semi-Supervised Few-Shot Classification
- Host: GitHub
- URL: https://github.com/renmengye/few-shot-ssl-public
- Owner: renmengye
- License: other
- Created: 2018-02-22T20:00:50.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2019-06-26T03:22:36.000Z (over 5 years ago)
- Last Synced: 2024-07-31T23:45:07.267Z (4 months ago)
- Language: Python
- Size: 1.46 MB
- Stars: 548
- Watchers: 17
- Forks: 99
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-few-shot-meta-learning - code - official (TF)
README
# few-shot-ssl-public
Code for paper
*Meta-Learning for Semi-Supervised Few-Shot Classification.* [[arxiv](https://arxiv.org/abs/1803.00676)]
## 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](https://drive.google.com/open?id=1INlOTyPtnCJgm0hBVvtRLu5a0itk8bjs)] (9.3 MB)
```
# Download and place "omniglot.tar.gz" in "$DATA_ROOT/omniglot".
mkdir -p $DATA_ROOT/omniglot
cd $DATA_ROOT/omniglot
mv ~/Downloads/omniglot.tar.gz .
tar -xzvf omniglot.tar.gz
rm -f omniglot.tar.gz
```
### miniImageNet
[[Google Drive](https://drive.google.com/open?id=16V_ZlkW4SsnNDtnGmaBRq2OoPmUOc5mY)] (1.1 GB)
Update: Python 2 and 3 compatible version:
[[train](https://drive.google.com/file/d/1I3itTXpXxGV68olxM5roceUMG8itH9Xj)]
[[val](https://drive.google.com/file/d/1KY5e491bkLFqJDp0-UWou3463Mo8AOco)]
[[test](https://drive.google.com/file/d/1wpmY-hmiJUUlRBkO9ZDCXAcIpHEFdOhD)]
```
# Download and place "mini-imagenet.tar.gz" in "$DATA_ROOT/mini-imagenet".
mkdir -p $DATA_ROOT/mini-imagenet
cd $DATA_ROOT/mini-imagenet
mv ~/Downloads/mini-imagenet.tar.gz .
tar -xzvf mini-imagenet.tar.gz
rm -f mini-imagenet.tar.gz
```
### tieredImageNet
[[Google Drive](https://drive.google.com/open?id=1g1aIDy2Ar_MViF2gDXFYDBTR-HYecV07)] (12.9 GB)
```
# Download and place "tiered-imagenet.tar" in "$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},
}
```