About

Meta Album is a meta-dataset created for few-shot learning, meta-learning, continual learning and so on. Meta Album consists of 40 datasets from 10 unique domains. Datasets are arranged in sets (10 datasets, one dataset from each domain). It is a continuously growing meta-dataset. See our datasets in Datasets Section.

We repurposed datasets that were generously made available by original creators, see credits page. All datasets are free for use for academic purposes, provided that proper credits are given. For your convenience, you may cite our paper, which references all original creators.

License

Meta-Album is released under a CC BY-NC 4.0 license permitting non-commercial use for research purposes, provided that you cite us. Additionally, redistributed datasets have their own license, see the credits page. All resources made available through this website provided “as is”. The curators of Meta-Album (and their home institutions and their sponsors) who have worked on its preparation, this website, the code provided to read, process data, and run baseline methods, make no warranties concerning the licensed material, including fitness for any purpose, non-infringement absence of defects or errors, accuracy, and they decline any liability for losses or other possible consequences that may arise by using such material.
This briefly summarizes the terms of the license CC BY-NC 4.0 and the disclaimer (that the license includes).

Recommended use

The recommended use of Meta-Album is to conduct fundamental research on machine learning algorithms and conduct benchmarks, particularly in: few-shot learning, meta-learning, continual learning, transfer learning, and image classification.

Code

We provide code in our GitHub Repository for

1. Data processing
2. Data formatting
3. Quality control
4. Meta-Album use cases
5. Challenge winners code (NeurIPS 2021 MetaDL competition)

Visit our GitHub repository for more details.

Datasets

We list in the tables below the data statistics of the original datasets. We generally chose image classification datasets with at least 20 classes having more than 40 examples per class. Each dataset was transformed into 128x128 pixel images. The data is available in 4 versions:

1. Original data (from the original creators’ website)
2. Meta Album extended = All classes having at least 40 examples per class, images 128x128 pixel
3. Meta Album mini = same as Meta Album extended, but we randomly sampled only 40 examples for each class (hence the datasets are class-balanced)
4. Meta Album micro = same as Meta Album mini, but only 20 randomly selected classes.

Download Instructions

Our datasets are hosted on OpenML platform. The following piece of code will help you download the datasets.
Install OpenML for python

Gallery

Some sample images from Meta Album datasets