--- 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)
[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)
## 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. $C^{MA} = (Z^M)^TZ^A$ $C^{MB} = (Z^M)^TZ^B$ $C^{MA}_{gt} = \lambda (Z^A)^TZ^A + (1-\lambda)\mathtt{Shuffle}^*(Z^B)^TZ^A$ $C^{MB}_{gt} = \lambda (Z^A)^TZ^B + (1-\lambda)\mathtt{Shuffle}^*(Z^B)^TZ^B$ ## 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`: ## 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).