Spellbook
This repo aims to optimize usability of ML techniques by implementing a standardized code piece suitable to various ML tasks such as classification, object detection, regression, etc.
By directly importing the code piece the learning canvas may be built rather fast and allow engineers to focus on task-specific issues rather than cross-task, reusable actions.
The goal is to build a machine learning pipeline, where you would only change global variables contained in the globalVariables.py file and start the whole data engineering and model/s training process in either magicClassification.py or magicDetection.py files. Data preprocessing is done differently for each project/competition beforehand, that is before it is fed to the pipeline.
Spellbook Contents
The table below contains the descriptions for each of the scripts this repo provides:
| Script | Description |
|---|---|
callbacks.py |
Custom callbacks for Keras library |
globalVariables.py |
Global variables and parameters such as data shapes, learning rate decay, etc. |
helpers.py |
Miscellaneous functions |
layers.py |
Custom / Specific layers. |
losses.py |
Custom loss functions |
magicClassification.py |
Main wrapper used to fit given models with given parameters and data. Used for classification/regression tasks. |
magicDetection.py |
Main wrapper used to fit given models with given parameters and data. Used for object localization tasks. |
metrics.py |
Custom metrics |
models.py |
Custom models as well as pre-trained Tensorflow models (such as ImageNet) |
optimizers.py |
Custom optimizers |
permutationFunctions.py |
Functions for data permutations |
prepareTrainDataset.py |
Functions to prepare datasets (initialization of tf.data.Dataset object with optional permutations) |
preprocessingFunctions.py |
Functions for data preprocessing |
templates.py |
Templates and formats on how to structure and document |
train.py |
Collection of train steps and full complete train cycles |