FCIL-main.zip联邦增量学习

 # Tools for mini-ImageNet Dataset [](https://github.com/y2l/meta-transfer-learning-tensorflow/blob/master/LICENSE) [](https://www.python.org/) [](https://pypi.org/project/miniimagenettools/) [](https://pepy.tech/project/miniimagenettools)  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](https://github.com/y2l/meta-transfer-learning-tensorflow). ### Summary * [About mini-ImageNet](#about-mini-ImageNet) * [Requirements](#requirements) * [Installation](#installation) * [Usage](#usage) * [Performance](#performance) * [Acknowledgement](#acknowledgement) ### About mini-ImageNet The mini-ImageNet dataset was proposed by [Vinyals et al.](http://papers.nips.cc/paper/6385-matching-networks-for-one-shot-learning.pdf) 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](https://arxiv.org/pdf/1409.0575.pdf). 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](https://openreview.net/pdf?id=rJY0-Kcll) ([GitHub link](https://github.com/twitter/meta-learning-lstm/tree/master/data/miniImagenet)). [Vinyals et al.](http://papers.nips.cc/paper/6385-matching-networks-for-one-shot-learning.pdf) didn't include their split files for mini-ImageNet when they first released their paper, so [Ravi and Larochelle](https://openreview.net/pdf?id=rJY0-Kcll) created their own splits. Additional split files are provided [here](https://github.com/yaoyao-liu/mini-imagenet-tools/tree/master/mini_imagenet_split). ### Requirements - Python 2.7 or 3.x - numpy - tqdm - opencv-python - Pillow ### Installation Install via PyPI: ```bash pip install miniimagenettools ``` Install via GitHub: ```bash git clone https://github.com/yaoyao-liu/mini-imagenet-tools.git ``` ### 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. Some people report the ImageNet website is not working. Here is an [alternative download link](https://academictorrents.com/details/a306397ccf9c2ead27155983c254227c0fd938e2). 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: ```bash python mini_imagenet_generator.py --tar_dir [your_path_of_the_ILSVRC2012_img_train.tar] ``` To generate mini-ImageNet dataset from untarred folder: ```bash python mini_imagenet_generator.py --imagenet_dir [your_path_of_imagenet_folder] ``` If you want to resize the images to the specified resolution: ```bash 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: ```python 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](https://few-shot.yyliu.net/miniimagenet.html). ### Download Processed Images [Download jpg files](https://drive.google.com/open?id=137M9jEv8nw0agovbUiEN_fPl_waJ2jIj) (Thanks for the contribution by [@vainaijr](https://github.com/vainaijr)) [Download tar files](https://mtl.yyliu.net/download/) ### Acknowledgement [Model-Agnostic Meta-Learning](https://github.com/cbfinn/maml) [Optimization as a Model for Few-Shot Learning](https://github.com/gitabcworld/FewShotLearning) [Meta-Learning for Semi-Supervised Few-Shot Classification](https://github.com/renmengye/few-shot-ssl-public) [@ChristopherDaw](https://github.com/ChristopherDaw)