semvec/pypi-packages huggingface dataset:
augwrap 0.0.1a2
Image augmentation auxiliary tool
Requires Python: >=3.6

# AugWrap
[![PyPI version](https://badge.fury.io/py/augwrap.svg)](https://badge.fury.io/py/augwrap)



## Installation

```shell
$ pip install augwrap
```



## Basic Usage

1. Base dataset을 선택합니다. Pytorch 기반 데이터셋을 만들려면 `TorchBaseDataset`을 사용합니다. Tensorflow 기반 데이터셋을 만들려면 `TFBaseDataset`을 사용합니다.

   ```python
   import augwrap as aw
   
   dataset = aw.data.TorchBaseDataset(images, labels, classes)
   ```

2. `augwrap.data` 안의 모듈을 이용하여 학습 전 데이터를 처리합니다.

   ```python
   dataset = aw.data.LoadImages(dataset)
   dataset = aw.data.ResizeImages(dataset, (256, 256))
   dataset = aw.data.OneHotLabels(dataset)
   ```

3. 데이터 로더를 생성하여 학습에 사용합니다.

   ```python
   from torch.utils.data import DataLoader
   
   data_loader = DataLoader(
       dataset,
       batch_size = 16,
       shuffle = False,
       num_workers = 4
   )
   ```



## Augmentations

잘 알려진 어그멘테이션 도구인 [Albumentations](https://github.com/albumentations-team/albumentations)을 활용할 수 있도록 만들었습니다.

`augwrap.data.Augmentations`의 생성자 인자로 base dataset에서 파생된 객체와 Albumentations 객체를 받습니다.

Albumentations와 함께 사용할 수 있는 어그멘테이션 모듈을 `augwrap.augmentations`에 추가했습니다.

```python
import albumentations as A
import augwrap as aw

augmentations = A.Compose([
        A.RandomRotate90(p=1),
        A.GridDistortion(p=0.8),
        A.GaussNoise(p=0.75),
        aw.augmentations.CutMix(dataset, p=0.8),
])
dataset = aw.data.Augmentations(dataset, augmentations)
```
machdi 0.0.1.4.dev4
machdi is an open source machine learning module that provides create ML models, preprocessing algorithms and evaluation metrics. this project is developing.
Requires Python: >=3.6

# Example Package

Welcome to Mahdi Zare's Machine Learning Library. With the help of this package, you can create machine learning models and algorithms.
Included preprocessing, training, evaluation, and so on.
This module is similar to the sikit learn package.