Metadata-Version: 2.1
Name: auto-deep-learning
Version: 0.1.4.4
Summary: Automation of the creation of the architecture of the neural network based on the input
Home-page: https://github.com/Nil-Andreu/auto-deep-learning
Author: Nil Andreu
Author-email: nilandreug@email.com
License: MIT
Keywords: deep learning,machine learning,computer vision,convolutional neural networks,neural networks
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Description-Content-Type: text/markdown
License-File: LICENSE.txt

# Auto-Deep-Learning (Auto Deep Learning)
[![Downloads](https://pepy.tech/badge/auto_deep_learning)](https://pepy.tech/project/auto_deep_learning) ![Version](https://img.shields.io/badge/version-0.1.1-blue) ![Python-Version](https://img.shields.io/badge/python-3.9-blue) ![issues](https://img.shields.io/github/issues/Nil-Andreu/auto_deep_learning) ![PyPI - Status](https://img.shields.io/pypi/status/auto_deep_learning) ![License](https://img.shields.io/github/license/Nil-Andreu/auto_deep_learning) 

```auto_deep_learning```: with this package, you will be able to create, train and deploy neural networks automatically based on the input that you provide.

## Installation
Use the package manager [pip](https://pypi.org/project/pip/) to install *auto_deep_learning*.

To install the package:
```bash
    pip install auto_deep_learning
```

**If using an old version of the package, update it:**
```bash
    pip install --upgrade auto_deep_learning
```


## Basic Usage

### Dataset

The data that it expects is a pd.DataFrame(), where the columns are the following:
```
    - image_path: the path to the image
    - class1: the classification of the class nr. 1. For example: {t-shirt, glasses, ...}
    - class2: the classification of the class nr. 2. For example: {summer, winter, ...}
    - ...
    - split_type: whether it is for training/validation/testing
```
For better performance, it is suggested that the classes and the type are of dtype *category* in the pandas DataFrame.
If the type is not provided in the dataframe, you should use the utils function of *data_split_types* (in *utils.dataset.sampler* file). 

If instead you have the images ordered in the structure of ImageFolder, which is the following structure:
```
    train/  
        class1_value/
            1.jgp
            2.jpg
            ...
        class2_value/
            3.jpg
            4.jpg
            ...
    test/
        class1_value/
            1.jgp
            2.jpg
            ...
        class2_value/
            3.jpg
            4.jpg
            ...
```
For simplifying logic, we have provided a logic that gives you the expected dataframe that we wanted, with the function of *image_folder_convertion* (in *utils.functions*), where it is expecting a path to the parent folder where the *train/* and */test* folders are.

