Package: auto-deep-learning
Auto-Deep-Learning (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.AlertThis package is still on development, but Start the Project to know further updates in next days!
For the moment, would be for computer vision classification tasks on images (multi-modal included).InstallationUse the package managerpipto installauto_deep_learning.To install the package:pipinstallauto_deep_learningIf using an old version of the package, update it:pipinstall--upgradeauto_deep_learningProject StructureThe project structure is the following one:├──auto_deep_learning# Python Package│├──cloud# Cloud module for saving & service DL models││├──aws# Amazon Web Services││└──gcp# Google Cloud│├──enum# Enumerations for the model│├──exceptions# Exceptions││├──model# Exceptions related to the definition/creation of the model││└──utils# Exceptions related to the utilities folder││└──data_handler# Exceptions related to handling the data│├──model# Module for creating & training the models││└──arch# Architectures supported of the models││└──convolution│├──schemas# Schemas of expected outputs│└──utils# Utilities for the project│├──data_handler# Utilities related to handling the data││├──creator# Utilities related to creating the loaders││└──transform# Utilities related to the transformation of the data│└──model# Utilities related to the creation of the model├──examples# Examples of how the package can be used└──tests# TestsBasic UsageHow easy can be to create and train a deep learning model:fromauto_deep_learningimportModelfromauto_deep_learning.utilsimportDataCreator,DataSampler,image_folder_convertiondf=image_folder_convertion()data=DataCreator(df)data_sampled=DatasetSampler(data)model=Model(data_sampled)model.fit()model.predict('image.jpg')We provide also with a configuration object, where it centralizes some of the most important configurations that you might want to do:ConfigurationObject(n_epochs:int=10,batch_size_train:int=64,batch_size_valid:int=128,batch_size_test:int=128,valid_size:float=0.1,test_size:float=0.05,image_size:int=224,num_workers:int=6,objective:ModelObjective=ModelObjective.THROUGHPUT,img_transformers:Dict[str,ImageTransformer]={'train':ImageTransformer(rotation=3.0,color_jitter_brightness=3.0,color_jitter_contrast=3.0,color_jitter_hue=3.0,color_jitter_saturation=3.0,color_jitter_enabled=True,resized_crop_enabled=True),'valid':ImageTransformer(),'test':ImageTransformer()})So by default, it is going to do image augmentation on the training data.
Note that if for example we did not want to make a validation split because our dataset is too small, we would change this value as:conf_obj=ConfigurationObject()conf_obj.valid_size=0.0DatasetThe 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 train/valid/testFor better performance, it is suggested that the classes and the type are of dtypecategoryin the pandas DataFrame.
If the type is not provided in the dataframe, you should use the utils function ofdata_split_types(inutils.dataset.samplerfile).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 ofimage_folder_convertion(inutils.functions), where it is expecting a path to the parent folder where thetrain/and/testfolders are.