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https://github.com/acl21/deep-active-learning-pytorch
A PyTorch toolkit with 8 popular deep active learning query methods implemented.
https://github.com/acl21/deep-active-learning-pytorch
deep-active-learning image-classification pytorch toolkit
Last synced: 8 days ago
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A PyTorch toolkit with 8 popular deep active learning query methods implemented.
- Host: GitHub
- URL: https://github.com/acl21/deep-active-learning-pytorch
- Owner: acl21
- License: mit
- Created: 2020-11-15T12:53:58.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2021-09-14T21:22:57.000Z (about 3 years ago)
- Last Synced: 2024-08-02T15:36:41.311Z (3 months ago)
- Topics: deep-active-learning, image-classification, pytorch, toolkit
- Language: Python
- Homepage:
- Size: 2.06 MB
- Stars: 81
- Watchers: 2
- Forks: 9
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Deep Active Learning Toolkit for Image Classification in PyTorch
This is a code base for deep active learning for image classification written in [PyTorch](https://pytorch.org/). It is build on top of FAIR's [pycls](https://github.com/facebookresearch/pycls/). I want to emphasize that this is a derivative of the toolkit originally shared with me via email by Prateek Munjal _et al._, the authors of the paper _"Towards Robust and Reproducible Active Learning using Neural Networks"_, paper available [here](https://arxiv.org/abs/2002.09564).
## Introduction
The goal of this repository is to provide a simple and flexible codebase for deep active learning. It is designed to support rapid implementation and evaluation of research ideas. We also provide a results on CIFAR10 below.
The codebase currently only supports single-machine single-gpu training. We will soon scale it to single-machine multi-gpu training, powered by the PyTorch distributed package.
## Using the Toolkit
Please see [`GETTING_STARTED`](docs/GETTING_STARTED.md) for brief instructions on installation, adding new datasets, basic usage examples, etc.
## Active Learning Methods Supported
* Uncertainty Sampling
* Least Confidence
* Min-Margin
* Max-Entropy
* Deep Bayesian Active Learning (DBAL) [1]
* Bayesian Active Learning by Disagreement (BALD) [1]
* Diversity Sampling
* Coreset (greedy) [2]
* Variational Adversarial Active Learning (VAAL) [3]
* Query-by-Committee Sampling
* Ensemble Variation Ratio (Ens-varR) [4]
## Datasets Supported
* [CIFAR10/100](https://www.cs.toronto.edu/~kriz/cifar.html)
* [MNIST](http://yann.lecun.com/exdb/mnist/)
* [SVHN](http://ufldl.stanford.edu/housenumbers/)
* [Tiny ImageNet](https://www.kaggle.com/c/tiny-imagenet) (Download the zip file [here](http://cs231n.stanford.edu/tiny-imagenet-200.zip))
* Long-Tail CIFAR-10/100
Follow the instructions in [`GETTING_STARTED`](docs/GETTING_STARTED.md) to add a new dataset.
## Results on CIFAR10 and CIFAR100
The following are the results on CIFAR10 and CIFAR100, trained with hyperameters present in `configs/cifar10/al/RESNET18.yaml` and `configs/cifar100/al/RESNET18.yaml` respectively. All results were averaged over 3 runs.
### CIFAR10 at 60%
```
| AL Method | Test Accuracy |
|:----------------:|:---------------------------:|
| DBAL | 91.670000 +- 0.230651 |
| Least Confidence | 91.510000 +- 0.087178 |
| BALD | 91.470000 +- 0.293087 |
| Coreset | 91.433333 +- 0.090738 |
| Max-Entropy | 91.373333 +- 0.363639 |
| Min-Margin | 91.333333 +- 0.234592 |
| Ensemble-varR | 89.866667 +- 0.127410 |
| Random | 89.803333 +- 0.230290 |
| VAAL | 89.690000 +- 0.115326 |
```
### CIFAR100 at 60%
```
| AL Method | Test Accuracy |
|:----------------:|:---------------------------:|
| DBAL | 55.400000 +- 1.037931 |
| Coreset | 55.333333 +- 0.773714 |
| Max-Entropy | 55.226667 +- 0.536128 |
| BALD | 55.186667 +- 0.369639 |
| Least Confidence | 55.003333 +- 0.937248 |
| Min-Margin | 54.543333 +- 0.611583 |
| Ensemble-varR | 54.186667 +- 0.325628 |
| VAAL | 53.943333 +- 0.680686 |
| Random | 53.546667 +- 0.302875 |
```
## Citing this Repository
If you find this repo helpful in your research, please consider citing us and the owners of the original toolkit:
```
@article{Chandra2021DeepAL,
Author = {Akshay L Chandra and Vineeth N Balasubramanian},
Title = {Deep Active Learning Toolkit for Image Classification in PyTorch},
Journal = {https://github.com/acl21/deep-active-learning-pytorch},
Year = {2021}
}
@article{Munjal2020TowardsRA,
title={Towards Robust and Reproducible Active Learning Using Neural Networks},
author={Prateek Munjal and N. Hayat and Munawar Hayat and J. Sourati and S. Khan},
journal={ArXiv},
year={2020},
volume={abs/2002.09564}
}
```
## License
This toolkit is released under the MIT license. Please see the [LICENSE](LICENSE) file for more information.
## References
[1] Yarin Gal, Riashat Islam, and Zoubin Ghahramani. Deep bayesian active learning with image data. In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pages 1183–1192. JMLR. org, 2017.
[2] Ozan Sener and Silvio Savarese. Active learning for convolutional neural networks: A core-set approach. In International Conference on Learning Representations, 2018.
[3] Sinha, Samarth et al. Variational Adversarial Active Learning. 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (2019): 5971-5980.
[4] William H. Beluch, Tim Genewein, Andreas Nürnberger, and Jan M. Köhler. The power of ensembles for active learning in image classification. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9368–9377, 2018.