https://github.com/schatty/prototypical-networks-tf

Implementation of Prototypical Networks for Few-shot Learning in TensorFlow 2.0
https://github.com/schatty/prototypical-networks-tf

few-shot-learning omniglot one-shot-learning prototypical-networks tensorflow2

Last synced: 3 months ago
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Implementation of Prototypical Networks for Few-shot Learning in TensorFlow 2.0

• Host: GitHub
• URL: https://github.com/schatty/prototypical-networks-tf
• Owner: schatty
• License: mit
• Created: 2019-03-30T02:19:29.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2023-10-17T22:57:26.000Z (over 1 year ago)
• Last Synced: 2024-08-02T19:01:41.124Z (6 months ago)
• Topics: few-shot-learning, omniglot, one-shot-learning, prototypical-networks, tensorflow2
• Language: Python
• Homepage:
• Size: 906 KB
• Stars: 51
• Watchers: 3
• Forks: 17
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• Awesome-Tensorflow2 - schatty/prototypical-networks-tf

README

        
# Prototypical Networks for Few-shot in TensorFlow 2.0

Implementation of Prototypical Networks for Few-shot Learning paper (https://arxiv.org/abs/1703.05175) in TensorFlow 2.0. Model has been tested on Omniglot and miniImagenet datasets with the same splitting as in the paper.



### Dependencies and Installation

* The code has been tested on Ubuntu 18.04 with Python 3.6.8 and TensorFflow 2.0.0-alpha0

* The two main dependencies are TensorFlow and Pillow package (Pillow is included in dependencies)

* To install `prototf` lib run `pytnon setup.py install`

* Run `bash data/download_omniglot.sh` from repo's root directory to download Omniglot dataset

* miniImagenet was downloaded from brilliant repo from `renmengye` (https://github.com/renmengye/few-shot-ssl-public) and placed into `data/mini-imagenet` folder

### Repository Structure

The repository organized as follows. `data` directory contains scripts for dataset downloading and used as a default directory for datasets. `prototf` is the library containing the model itself (`prototf/models`) and logic for datasets loading and processing (`prototf/data`). `scripts` directory contains scripts for launching the training. `train/run_train.py` and `eval/run_eval.py` launch training and evaluation respectively. `tests` folder contains basic training procedure on small-valued parameters to check general correctness. `results` folder contains .md file with current configuration and details of conducted experiments.

### Training

* Training and evaluation configurations are specified through config files, each config describes single train+eval evnironment.

* Run `python scripts/train/run_train.py --config scripts/config_omniglot.conf` to run training on Omniglot with default parameters.

* Run `python scripts/train/run_train.py --config scripts/config_miniimagenet.conf` to run training on miniImagenet with default parmeters

### Evaluating

* Run `python scripts/eval/run_eval.py --config scripts/config_omniglot.conf` to run evaluation on Omniglot

* Run `python scripts/eval/run_eval.py --config scripts/config_miniimagenet.conf` to run evaluation on miniImagenet

### Tests

* Run `python -m unittest tests/test_omniglot.py` from repo's root to test Omniglot

* Run `python -m unittest tests/test_mini_imagenet.py` from repo's root test miniImagenet 

### Results

Omniglot:

| Evnironment                 | 5-way-5-shot     | 5-way-1-shot     | 20-way-5-shot    | 20-way-1shot     |

|-----------------------------|------------------|------------------|------------------|------------------|

| Accuracy                    | 99.4%            | 97.4%            | 98.4%            | 92.2%            |

miniImagenet

| Evnironment                 | 5-way-5-shot     | 5-way-1-shot     | 

|-----------------------------|------------------|------------------|

| Accuracy                    | 66.0%            | 43.5%            |

Additional settings can be found in `results` folder in the root of repository.