Abstract: With the growing needs for few shot learning, Mini Imagenet has gained its position in FSL area. However, I found at least 3 versions of Mini Imagenet Dataset (in different file formats and directory structures), and there are also tons of different pre-processing programs for this dataset, which requires your (tremendous) extra effort to rearrange the dataset in the right way. Well, I did all that for you. This is a complete pre-processing program for Mini Imagenet, leading you from downloading the raw data to getting the complete pre-processed dataset.

keywords: Mini ImageNet, Data Preprocessing, Few Shot Learning

No, read it, seriously. Everything is important.

I borrowed the baseline pre-processing program from here, and made some subtle changes. Credits to Boris Oreshkin et. al..

Link to Mini_Imagenet Dataset tar.gz file: google drive

Our next step is downloading the raw dataset from google drive. After unpacking this file, you will get 3 pkl files, which stand for a series of pickled files. They can be opened via python pickle module.

Move/save the 3 pkl files to the mini_imagenet directory

Before you run the program, prepare the directory structures like this:

.
|
 ---- datasets
|        |
|         ---- create_dataset_miniImagenet.py
|         ---- test.csv
|         ---- train.csv
|         ---- val.csv
|
 ---- mini_imagenet
|        |
|         ---- mini-imagenet-cache-train.pkl
|         ---- mini-imagenet-cache-test.pkl
|         ---- mini-imagenet-cache-val.pkl
|         ---- unpack.py
|
 ---- temp

Besides saving the three pkl files to mini_imagenet directory, create a new empty directory temp. This directory will be used to save unpacked images from the three pkl files. Of course you can put it anywhere you like, and you can modify the paths in unpack.py to fit your environment.

Before you run the program, check if all the packages/modules in requirements.txt files have been installed properly. If not, you may install with pip install package.

1. Open the unpack.py file and make changes to paths to fit your devices.
2. Run python3 unpack.py in python3 environment
3. Go to temp folder and run zip images.zip ./*
4. Run mv images.zip ../datasets
5. You can remove the temp directory now to save storage space
6. Go to datasets folder and run the python program python3 create_dataset_miniImagenet.py --data-dir /path/to/current/dir/ --output-dir /path/to/save/the/final/preprocessed/files/
7. It might take some time, like... forever
8. Done