Awesome
Tools for mini-ImageNet Dataset
This repo provides python source code for creating mini-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.
Summary
About mini-ImageNet
The mini-ImageNet dataset was proposed by Vinyals et al. for few-shot learning evaluation. Its complexity is high due to the use of ImageNet images but requires fewer resources and infrastructure than running on the full ImageNet dataset. In total, there are 100 classes with 600 samples of 84×84 color images per class. These 100 classes are divided into 64, 16, and 20 classes respectively for sampling tasks for meta-training, meta-validation, and meta-test.
Please note that the split files in csv_files folder are created by Ravi and Larochelle (GitHub link). Vinyals et al. didn't include their split files for mini-ImageNet when they first released their paper, so Ravi and Larochelle created their own splits. Additional split files are provided here.
Requirements
- Python 2.7 or 3.x
- numpy
- tqdm
- opencv-python
- Pillow
Installation
Install via PyPI:
pip install miniimagenettools
Install via GitHub:
git clone https://github.com/yaoyao-liu/mini-imagenet-tools.git
Usage
First, you need to download the image source files from ImageNet website. If you already have it, you may use it directly. Some people report the ImageNet website is not working. Here is an alternative download link. Please carefully read the terms for ImageNet before you download it.
Filename: ILSVRC2012_img_train.tar
Size: 138 GB
MD5: 1d675b47d978889d74fa0da5fadfb00e
Then clone the repo:
git clone https://github.com:y2l/mini-imagenet-tools.git
cd mini-imagenet-tools
To generate mini-ImageNet dataset from tar file:
python mini_imagenet_generator.py --tar_dir [your_path_of_the_ILSVRC2012_img_train.tar]
To generate mini-ImageNet dataset from untarred folder:
python mini_imagenet_generator.py --imagenet_dir [your_path_of_imagenet_folder]
If you want to resize the images to the specified resolution:
python mini_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 MiniImageNetDataLoader class:
from miniimagenettools.mini_imagenet_dataloader import MiniImageNetDataLoader
dataloader = MiniImageNetDataLoader(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)
...
Performance
Check the SOTA results for mini-ImageNet on this page.
Download Processed Images
Download jpg files (Thanks for the contribution by @vainaijr)
Acknowledgement
Optimization as a Model for Few-Shot Learning