https://github.com/yaoyao-liu/tiered-imagenet-tools

Tools for generating tieredImageNet dataset and processing batches
https://github.com/yaoyao-liu/tiered-imagenet-tools

dataset few-shot few-shot-learning meta-learning tiered-imagenet

Last synced: about 2 months ago
JSON representation

Tools for generating tieredImageNet dataset and processing batches

• Host: GitHub
• URL: https://github.com/yaoyao-liu/tiered-imagenet-tools
• Owner: yaoyao-liu
• License: mit
• Created: 2019-04-29T08:32:16.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-12-15T20:13:13.000Z (almost 4 years ago)
• Last Synced: 2024-04-10T06:07:09.826Z (5 months ago)
• Topics: dataset, few-shot, few-shot-learning, meta-learning, tiered-imagenet
• Language: Python
• Homepage: https://mtl.yyliu.net/datasets
• Size: 985 KB
• Stars: 73
• Watchers: 6
• Forks: 12
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-few-shot-meta-learning - tiered-imagenet github (非官方)

README

        


    



# Tools for *tiered*ImageNet Dataset

[![LICENSE](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/y2l/tiered-imagenet-tools/blob/master/LICENSE)

This repo provides python source codes for creating *tiered*ImageNet 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](https://github.com/y2l/meta-transfer-learning-tensorflow).

### Summary

* [About *tiered*ImageNet](#about-tieredImageNet)

* [Requirements](#requirements)

* [Usage](#usage)

* [Performance](#performance)

* [Acknowledgement](#acknowledgement)

### About *tiered*ImageNet

The [*tiered*ImageNet](https://arxiv.org/pdf/1803.00676.pdf) 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](http://www.image-net.org/challenges/LSVRC/2012/). 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 *tiered*ImageNet dataset from tar file:

```bash

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

```

To generate *tiered*ImageNet dataset from untarred folder:

```bash

python tiered_imagenet_generator.py --imagenet_dir [your_path_of_imagenet_folder]

```

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

```bash

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:

```python

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](https://github.com/cbfinn/maml)

[Optimization as a Model for Few-Shot Learning](https://github.com/gitabcworld/FewShotLearning)