Tools for tieredImageNet Dataset

This repo provides python source codes for creating tieredImageNet dataset from ImageNet and the utils for generating batches during training. This repo is related to our work on few-shot learning: Meta-Transfer Learning.

Summary

• About tieredImageNet
• Requirements
• Usage
• Performance
• Acknowledgement

About tieredImageNet

The tieredImageNet dataset is a larger subset of ILSVRC-12 with 608 classes (779,165 images) grouped into 34 higher-level nodes in the ImageNet human-curated hierarchy. This set of nodes is partitioned into 20, 6, and 8 disjoint sets of training, validation, and testing nodes, and the corresponding classes form the respective meta-sets. As argued in Ren et al. (2018), this split near the root of the ImageNet hierarchy results in a more challenging, yet realistic regime with test classes that are less similar to training classes.

Requirements

• Python 2.7 or 3.x
• numpy
• tqdm
• opencv-python
• Pillow

Usage

First, you need to download the image source files from ImageNet website. If you already have it, you may use it directly.

Filename: ILSVRC2012_img_train.tar
Size: 138 GB
MD5: 1d675b47d978889d74fa0da5fadfb00e

Then clone the repo:

git clone https://github.com:y2l/tiered-imagenet-tools.git
cd tiered-imagenet-tools

To generate tieredImageNet dataset from tar file:

python tiered_imagenet_generator.py --tar_dir [your_path_of_the_ILSVRC2012_img_train.tar]

To generate tieredImageNet dataset from untarred folder:

python tiered_imagenet_generator.py --imagenet_dir [your_path_of_imagenet_folder]

If you want to resize the images to the specified resolution:

python tiered_imagenet_generator.py --tar_dir [your_path_of_the_ILSVRC2012_img_train.tar] --image_resize 100

P.S. In default settings, the images will be resized to 84 × 84.

If you don't want to resize the images, you may set --image_resize 0.

To use the TieredImageNetDataLoader class:

from tiered_imagenet_dataloader import TieredImageNetDataLoader

dataloader = TieredImageNetDataLoader(shot_num=5, way_num=5, episode_test_sample_num=15)

dataloader.generate_data_list(phase='train')
dataloader.generate_data_list(phase='val')
dataloader.generate_data_list(phase='test')

dataloader.load_list(phase='all')

for idx in range(total_train_step):
    episode_train_img, episode_train_label, episode_test_img, episode_test_label = \
        dataloader.get_batch(phase='train', idx=idx)
    ...

Acknowledgement

This repo uses the source code from the following repos:

Model-Agnostic Meta-Learning

Optimization as a Model for Few-Shot Learning