README.md

---
license: mit
datasets:
- cifar10
- cifar100
- zh-plus/tiny-imagenet
- imagenet-1k
- jxie/stl10
language:
- en
metrics:
- accuracy
tags:
- self-supervised learning
- barlow-twins
---
# Mixed Barlow Twins
[**Guarding Barlow Twins Against Overfitting with Mixed Samples**](https://arxiv.org/abs/2312.02151)<br>

[Wele Gedara Chaminda Bandara](https://www.wgcban.com) (Johns Hopkins University), [Celso M. De Melo](https://celsodemelo.net) (U.S. Army Research Laboratory), and [Vishal M. Patel](https://engineering.jhu.edu/vpatel36/) (Johns Hopkins University) <br>

## 1 Overview of Mixed Barlow Twins

TL;DR
- Mixed Barlow Twins aims to improve sample interaction during Barlow Twins training via linearly interpolated samples. 
- We introduce an additional regularization term to the original Barlow Twins objective, assuming linear interpolation in the input space translates to linearly interpolated features in the feature space.
- Pre-training with this regularization effectively mitigates feature overfitting and further enhances the downstream performance on `CIFAR-10`, `CIFAR-100`, `TinyImageNet`, `STL-10`, and `ImageNet` datasets.

<img src="figs/mix-bt.svg" width="1024">

$C^{MA} = (Z^M)^TZ^A$

$C^{MB} = (Z^M)^TZ^B$

$C^{MA}_{gt} = \lambda (Z^A)^TZ^A + (1-\lambda)\mathtt{Shuffle}^*(Z^B)^TZ^A$

$C^{MB}_{gt} = \lambda (Z^A)^TZ^B + (1-\lambda)\mathtt{Shuffle}^*(Z^B)^TZ^B$

## 2 Usage
### 2.1 Requirements

Before using this repository, make sure you have the following prerequisites installed:

- [Anaconda](https://www.anaconda.com/download/)
- [PyTorch](https://pytorch.org)

You can install PyTorch with the following [command](https://pytorch.org/get-started/locally/) (in Linux OS):
```bash
conda install pytorch torchvision pytorch-cuda=11.8 -c pytorch -c nvidia
```

### 2.2 Installation

To get started, clone this repository:
```bash
git clone https://github.com/wgcban/mix-bt.git
```

Next, create the [conda](https://docs.conda.io/projects/conda/en/stable/) environment named `ssl-aug` by executing the following command:
```bash
conda env create -f environment.yml
```

All the train-val-test statistics will be automatically upload to [`wandb`](https://wandb.ai/home), and please refer [`wandb-quick-start`](https://wandb.ai/quickstart?utm_source=app-resource-center&utm_medium=app&utm_term=quickstart) documentation if you are not familiar with using `wandb`. 

### 2.3 Supported Pre-training Datasets

This repository supports the following pre-training datasets:
- `CIFAR-10`: https://www.cs.toronto.edu/~kriz/cifar.html
- `CIFAR-100`: https://www.cs.toronto.edu/~kriz/cifar.html
- `Tiny-ImageNet`: https://github.com/rmccorm4/Tiny-Imagenet-200
- `STL-10`: https://cs.stanford.edu/~acoates/stl10/
- `ImageNet`: https://www.image-net.org

`CIFAR-10`, `CIFAR-100`, and `STL-10` datasets are directly available in PyTorch. 

To use `TinyImageNet`, please follow the preprocessing instructions provided in the [TinyImageNet-Script](https://gist.github.com/moskomule/2e6a9a463f50447beca4e64ab4699ac4). Download these datasets and place them in the `data` directory.

### 2.4 Supported Transfer Learning Datasets
You can download and place transfer learning datasets under their respective paths, such as 'data/DTD'. The supported transfer learning datasets include:
- `DTD`: https://www.robots.ox.ac.uk/~vgg/data/dtd/ 
- `MNIST`: http://yann.lecun.com/exdb/mnist/
- `FashionMNIST`: https://github.com/zalandoresearch/fashion-mnist
- `CUBirds`: http://www.vision.caltech.edu/visipedia/CUB-200-2011.html
- `VGGFlower`: https://www.robots.ox.ac.uk/~vgg/data/flowers/102/
- `Traffic Signs`: https://benchmark.ini.rub.de/gtsdb_dataset.html
- `Aircraft`: https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/

### 2.5 Supported SSL Methods

This repository supports the following Self-Supervised Learning (SSL) methods:

- [`SimCLR`](https://arxiv.org/abs/2002.05709): contrastive learning for SSL 
- [`BYOL`](https://arxiv.org/abs/2006.07733): distilation for SSL
- [`Witening MSE`](http://proceedings.mlr.press/v139/ermolov21a/ermolov21a.pdf): infomax for SSL
- [`Barlow Twins`](https://arxiv.org/abs/2103.03230): infomax for SSL
- **`Mixed Barlow Twins (ours)`**: infomax + mixed samples for SSL

### 2.6 Pre-Training with Mixed Barlow Twins
To start pre-training and obtain k-NN evaluation results for Mixed Barlow Twins on `CIFAR-10`, `CIFAR-100`, `TinyImageNet`, and `STL-10` with `ResNet-18/50` backbones, please run:
```bash
sh scripts-pretrain-resnet18/[dataset].sh
```
```bash
sh scripts-pretrain-resnet50/[dataset].sh
```

To start the pre-training on `ImageNet` with `ResNet-50` backbone, please run:
```bash
sh scripts-pretrain-resnet18/imagenet.sh
```

### 2.7 Linear Evaluation of Pre-trained Models
Before running linear evaluation, *ensure that you specify the `model_path` argument correctly in the corresponding .sh file*. 

To obtain linear evaluation results on `CIFAR-10`, `CIFAR-100`, `TinyImageNet`, `STL-10` with `ResNet-18/50` backbones, please run:
```bash
sh scripts-linear-resnet18/[dataset].sh
```
```bash
sh scripts-linear-resnet50/[dataset].sh
```

To obtain linear evaluation results on `ImageNet` with `ResNet-50` backbone, please run:
```bash
sh scripts-linear-resnet50/imagenet_sup.sh
```


### 2.8 Transfer Learning of Pre-trained Models
To perform transfer learning from pre-trained models on `CIFAR-10`, `CIFAR-100`, and `STL-10` to fine-grained classification datasets, execute the following command, making sure to specify the `model_path` argument correctly:
```bash
sh scripts-transfer-resnet18/[dataset]-to-x.sh
```

## 3 Pre-Trained Checkpoints
Download the pre-trained models from `checkpoints/` and store them in `checkpoints/`. This repository provides pre-trained checkpoints for both [`ResNet-18`](https://arxiv.org/abs/1512.03385) and [`ResNet-50`](https://arxiv.org/abs/1512.03385) architectures.

#### 3.1 ResNet-18
| Dataset        |  $d$   | $\lambda_{BT}$ | $\lambda_{reg}$ | Download Link to Pretrained Model | KNN Acc. | Linear Acc. |
| ----------     | ---  | ---------- | ---------- | ------------------------ | -------- | ----------- |
| `CIFAR-10`       | 1024 | 0.0078125  | 4.0        | [4wdhbpcf_0.0078125_1024_256_cifar10_model.pth](https://huggingface.co/wgcban/mix-bt/blob/main/checkpoints/4wdhbpcf_0.0078125_1024_256_cifar10_model.pth)     | 90.52    | 92.58        |
| `CIFAR-100`     | 1024 | 0.0078125  | 4.0        | [76kk7scz_0.0078125_1024_256_cifar100_model.pth](https://huggingface.co/wgcban/mix-bt/blob/main/checkpoints/76kk7scz_0.0078125_1024_256_cifar100_model.pth)     | 61.25     | 69.31        |
| `TinyImageNet`   | 1024 | 0.0009765  | 4.0        | [02azq6fs_0.0009765_1024_256_tiny_imagenet_model.pth](https://huggingface.co/wgcban/mix-bt/blob/main/checkpoints/02azq6fs_0.0009765_1024_256_tiny_imagenet_model.pth)     | 38.11    | 51.67        |
| `STL-10`        | 1024 | 0.0078125  | 2.0        | [i7det4xq_0.0078125_1024_256_stl10_model.pth](https://huggingface.co/wgcban/mix-bt/blob/main/checkpoints/i7det4xq_0.0078125_1024_256_stl10_model.pth)     | 88.94     | 91.02        |

#### 3.2 ResNet-50
| Dataset        |  $d$   | $\lambda_{BT}$ | $\lambda_{reg}$ | Download Link to Pretrained Model | KNN Acc. | Linear Acc. |
| ----------     | ---  | ---------- | ---------- | ------------------------ | -------- | ----------- |
| `CIFAR-10`       | 1024 | 0.0078125  | 4.0        | [v3gwgusq_0.0078125_1024_256_cifar10_model.pth](https://huggingface.co/wgcban/mix-bt/blob/main/checkpoints/v3gwgusq_0.0078125_1024_256_cifar10_model.pth)     | 91.39     | 93.89        |
| `CIFAR-100`      | 1024 | 0.0078125  | 4.0        | [z6ngefw7_0.0078125_1024_256_cifar100_model.pth](https://huggingface.co/wgcban/mix-bt/blob/main/checkpoints/z6ngefw7_0.0078125_1024_256_cifar100_model.pth)     | 64.32     | 72.51        |
| `TinyImageNet`   | 1024 | 0.0009765  | 4.0        | [kxlkigsv_0.0009765_1024_256_tiny_imagenet_model.pth](https://huggingface.co/wgcban/mix-bt/blob/main/checkpoints/kxlkigsv_0.0009765_1024_256_tiny_imagenet_model.pth)     | 42.21     | 51.84        |
| `STL-10`        | 1024 | 0.0078125  | 2.0        | [pbknx38b_0.0078125_1024_256_stl10_model.pth](https://huggingface.co/wgcban/mix-bt/blob/main/checkpoints/pbknx38b_0.0078125_1024_256_stl10_model.pth)     | 87.79     | 91.70        |

**On `ImageNet`**
| # Epochs        |  $d$   | $\lambda_{BT}$ | $\lambda_{reg}$ | Download Link to Pretrained Model | Linear Acc. |
| ----------     | ---  | ---------- | ---------- | ------------------------ | ----------- |
| 300             | 8192 | 0.0051  | 0.0 (BT)        | [3on0l4wl_0.0000_8192_1024_imagenet_resnet50.pth](https://huggingface.co/wgcban/mix-bt/blob/main/checkpoints/3on0l4wl_0.0000_8192_1024_imagenet_resnet50.pth)   | 71.3        |
| 300             | 8192 | 0.0051  | 0.0025       | [l418b9zw_0.0025_8192_1024_imagenet_resnet50.pth](https://huggingface.co/wgcban/mix-bt/blob/main/checkpoints/l418b9zw_0.0025_8192_1024_imagenet_resnet50.pth)     |  70.9        |
| 300             | 8192 | 0.0051  | 0.1        | [13awtq23_0.1000_8192_1024_imagenet_resnet50.pth](https://huggingface.co/wgcban/mix-bt/blob/main/checkpoints/13awtq23_0.1000_8192_1024_imagenet_resnet50.pth)   | 71.6        |
| 300             | 8192 | 0.0051  | 1.0        | [3fb1op86_1.0000_8192_1024_imagenet_resnet50.pth](https://huggingface.co/wgcban/mix-bt/blob/main/checkpoints/3fb1op86_1.0000_8192_1024_imagenet_resnet50.pth)     | **72.2**        |
| 300             | 8192 | 0.0051  | 3.0        | [TBU]()     | TBU        |
| 300             | 8192 | 0.0051  | 5.0        | [TBU]()     | TBU        |

## 4 Training Statistics
Here we provide some training and validation (linear probing) statistics for Barlow Twins *vs.* Mixed Barlow Twins with `ResNet-50` backbone on `ImageNet`:

<img src="figs/in-loss-bt.png" width="256"/> <img src="figs/in-loss-reg.png" width="256"/> <img src="figs/in-linear.png" width="256"/> 

## 5 Disclaimer
A large portion of the code is from [Barlow Twins HSIC](https://github.com/yaohungt/Barlow-Twins-HSIC) (for experiments on small datasets: `CIFAR-10`, `CIFAR-100`, `TinyImageNet`, and `STL-10`) and official implementation of Barlow Twins [here](https://github.com/facebookresearch/barlowtwins) (for experiments on `ImageNet`), which is a great resource for academic development.

Also, note that the implementation of SOTA methods ([SimCLR](https://arxiv.org/abs/2002.05709), [BYOL](https://arxiv.org/abs/2006.07733), and [Witening-MSE](https://arxiv.org/abs/2007.06346)) in `ssl-sota` are copied from [Witening-MSE](https://github.com/htdt/self-supervised).

We would like to thank all of them for making their repositories publicly available for the research community. 🙏

## 6 Reference
If you feel our work is useful, please consider citing our work. Thanks!
```bibtex
@misc{bandara2023guarding,
      title={Guarding Barlow Twins Against Overfitting with Mixed Samples}, 
      author={Wele Gedara Chaminda Bandara and Celso M. De Melo and Vishal M. Patel},
      year={2023},
      eprint={2312.02151},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```

## 7 License
This code is under MIT licence, you can find the complete file [here](https://github.com/wgcban/mix-bt/blob/main/LICENSE).