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SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data (AAAI 2021)

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- comm.yml
- __init__.py
- aircraft.py
- car.py
- cub.py
- nabirds.py
- tfs.py
- overview.jpg
- __init__.py
- densenet_ft.py
- efficientnet_ft.py
- inception.py
- inception_ft.py
- modelutil.py
- resnet.py
- resnet_ft.py
- comm_test.py
- comm_train.py
- __init__.py
- conf.py
- custom_ops.py
- dataloader.py
- eval.py
- io.py
- log.py
- misc.py
- mixmethod.py
- stat.py
- trainutil.py
.gitignore
main.py
README.md

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# SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data (AAAI 2021)

PyTorch implementation of SnapMix | [paper](https://arxiv.org/abs/2012.04846)

## Method Overview

![SnapMix](https://raw.githubusercontent.com/Shaoli-Huang/SnapMix/main/imgs/overview.jpg)

## Cite
```
@inproceedings{huang2021snapmix,
    title={SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data},
    author={Shaoli Huang, Xinchao Wang, and Dacheng Tao},
    year={2021},
    booktitle={AAAI Conference on Artificial Intelligence},
}
```

## Setup
### Install Package Dependencies
```
torch
torchvision 
PyYAML
easydict
tqdm
scikit-learn
efficientnet_pytorch
pandas
opencv
```
### Datasets
***create a soft link to the dataset directory***

CUB dataset
```
ln -s /your-path-to/CUB-dataset data/cub
```
Car dataset
```
ln -s /your-path-to/Car-dataset data/car
```
Aircraft dataset
```
ln -s /your-path-to/Aircraft-dataset data/aircraft
```

## Training

### Training with Imagenet pre-trained weights

***1. Baseline and Baseline+***

To train a model on CUB dataset using the Resnet-50 backbone,

``` python main.py ```   # baseline

``` python main.py --midlevel```  # baseline+

To train model on other datasets using other network backbones, you can specify the following arguments:

``` --netname: name of network architectures (support 4 network families: ResNet,DenseNet,InceptionV3,EfficientNet) ```

``` --dataset: dataset name```

For example,

``` python main.py --netname resnet18 --dataset cub ```   # using the Resnet-18 backbone on CUB dataset

``` python main.py --netname efficientnet-b0 --dataset cub ```   # using the EfficientNet-b0 backbone on CUB dataset

``` python main.py --netname inceptoinV3 --dataset aircraft ```  # using the inceptionV3 backbone on Aircraft dataset

***2. Training with mixing augmentation***

Applying SnapMix in training ( we used the hyperparameter values (prob=1., beta=5) for SnapMix in most of the experiments.):

```python main.py --mixmethod snapmix --beta 5 --netname resnet50 --dataset cub ``` # baseline

```python main.py --mixmethod snapmix --beta 5 --netname resnet50 --dataset cub --midlevel ``` # baseline+

Applying other augmentation methods (currently support cutmix,cutout,and mixup) in training:

```python main.py --mixmethod cutmix --beta 3 --netname resnet50 --dataset cub ```   # training with CutMix

```python main.py --mixmethod mixup --prob 0.5 --netname resnet50 --dataset cub ```  # training with MixUp

***3. Results***

***ResNet architecture.***

|  Backbone | Method | CUB   | Car    |   Aircraft |  
|:--------|:--------|--------:|------:|--------:|
|Resnet-18 | Baseline| 82.35% |  91.15% | 87.80% |  
|Resnet-18 | Baseline + SnapMix| 84.29% |  93.12% | 90.17% |
|Resnet-34 | Baseline| 84.98% |  92.02% | 89.92% |  
|Resnet-34 | Baseline + SnapMix| 87.06% |  93.95% | 92.36% |
|Resnet-50 | Baseline| 85.49% |  93.04% | 91.07% |  
|Resnet-50 | Baseline + SnapMix| 87.75% |  94.30% | 92.08% |
|Resnet-101 | Baseline| 85.62% |  93.09% | 91.59% |  
|Resnet-101 | Baseline + SnapMix| 88.45% |  94.44% | 93.74% |
|Resnet-50 | Baseline+| 87.13% |  93.80% | 91.68% |  
|Resnet-50 | Baseline+ + SnapMix| 88.70% |  95.00% | 93.24% |
|Resnet-101 | Baseline+| 87.81% |  93.94% | 91.85% |  
|Resnet-101 | Baseline+ + SnapMix| 89.32% |  94.84% | 94.05% |

***InceptionV3 architecture.***

|  Backbone | Method | CUB   | 
|:--------|:--------|--------:|
|InceptionV3 | Baseline| 82.22% |
|InceptionV3 | Baseline + SnapMix| 85.54%|

***DenseNet architecture.***

|  Backbone | Method | CUB   | 
|:--------|:--------|--------:|
|DenseNet121 | Baseline| 84.23% |  
|DenseNet121| Baseline + SnapMix| 87.42%|

### Training from scratch

To train a model without using ImageNet pretrained weights:

```python main.py --mixmethod snapmix --prob 0.5 --netname resnet18 --dataset cub --pretrained 0``` # resnet-18 backbone

```python main.py --mixmethod snapmix --prob 0.5 --netname resnet50 --dataset cub --pretrained 0 ``` # resnet-50 backbone

***2. Results***

|  Backbone | Method | CUB   | 
|:--------|:--------|--------:|
|Resnet-18 | Baseline| 64.98% |
|Resnet-18 | Baseline + SnapMix| 70.31%|
|Resnet-50 | Baseline| 66.92% |  
|Resnet-50| Baseline + SnapMix| 72.17%|

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