https://github.com/IlyaDobrynin/GridMixup

A GridMixup augmentation, inspired by GridMask and CutMix
https://github.com/IlyaDobrynin/GridMixup

Last synced: 3 months ago
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A GridMixup augmentation, inspired by GridMask and CutMix

• Host: GitHub
• URL: https://github.com/IlyaDobrynin/GridMixup
• Owner: IlyaDobrynin
• License: mit
• Created: 2020-12-14T07:38:38.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2020-12-24T14:04:03.000Z (about 4 years ago)
• Last Synced: 2024-08-03T23:12:50.283Z (6 months ago)
• Language: Python
• Homepage:
• Size: 23.4 KB
• Stars: 44
• Watchers: 3
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# GridMixup

A GridMixup augmentation, inspired by GridMask and CutMix

##  Easy install

`pip install git+https://github.com/IlyaDobrynin/GridMixup.git`

## Overview

This simple augmentation is inspired by the [GridMask](https://arxiv.org/abs/2001.04086) and [CutMix](https://arxiv.org/abs/1905.04899) augmentations.

The combination of this two augmentations forms proposed method.

### Example

To run simple examples notebooks, you should install requirements:

```

pip install -r requirements.txt

```

Simple examples are here: [demo](examples/demo.ipynb) and [pipeline demo](examples/simple_train_demo.ipynb)

TlDr:

```

from gridmix import GridMixupLoss

gridmix_cls = GridMixupLoss(

    alpha=(0.4, 0.7),

    hole_aspect_ratio=1.,

    crop_area_ratio=(0.5, 1),

    crop_aspect_ratio=(0.5, 2),

    n_holes_x=(2, 6)

)

images, targets = batch['images'], batch['targets']

images_mixed, targets_mixed = gridmix_cls.get_sample(images=images, targets=targets)

preds = model(images_mixed)

loss = criterion(preds, targets_mixed) 

```

**Before**


![](images/img.png)


**After**


![](images/img_1.png)

GridMixup loss defined as:

`lam * CrossEntropyLoss(preds, trues1) + (1 - lam) * CrossEntropyLoss(preds, trues2)`

where:

- `lam` - the area of the main image

- `(1 - lam)` - area of the secondary image 

### Parameters

GridMixupLoss takes follow arguments:

- `alpha` - parameter define area of the main image in mixed image. Could be `float` or `Tuple[float, float]`.

    - if `float`: lambda parameter gets from the beta-dictribution np.random.beta(alpha, alpha);

    - if `Tuple[float, float]`: lambda parameter gets from the uniform distribution np.random.uniform(alpha[0], alpha[1]).

- `n_holes_x` - number of holes in crop by X axis.

- `hole_aspect_ratio` - aspect ratio of holes.

- `crop_area_ratio` - parameter define area of the secondary image on a mixed image.

- `crop_aspect_ratio` - aspect ratio of crop.