Metadata-Version: 2.1
Name: pietoolbelt
Version: 0.2.2
Summary: Toolbelt for PiePline training pipeline
Home-page: https://github.com/PiePline/PieToolbelt
Author: Anton Fedotov
Author-email: anton.fedotov.af@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Requires-Dist: piepline (>=0.2.2)
Requires-Dist: opencv-python
Requires-Dist: scikit-learn
Requires-Dist: numpy
Requires-Dist: tqdm
Requires-Dist: torch (>=0.4.1)
Requires-Dist: torchvision

# PiePline toolbelt

# Installation:
[![PyPI version](https://badge.fury.io/py/pietoolbelt.svg)](https://badge.fury.io/py/pietoolbelt)
[![PyPI Downloads/Month](https://pepy.tech/badge/pietoolbelt/month)](https://pepy.tech/project/pietoolbelt)
[![PyPI Downloads](https://pepy.tech/badge/pietoolbelt)](https://pepy.tech/project/pietoolbelt)

`pip install pietoolbelt`

##### Install latest version before it's published on PyPi
`pip install -U git+https://github.com/PiePline/pietoolbelt`

# Functional
* Datasets
    * `datasets.stratification` - stratification by histogram
    * `datasets.utils` - set of datasets constructors that
* Losses
    * `losses.common` - losses utils
    * `losses.regression` - regression losses
    * `losses.segmentation` - losses for single and multi-class segmentation
    * `losses.detection` - losses for detection task
* Metrics
    * `metrics.common` - common utils for metrics
    * CPU - metrics, that calculates by `numpy`
        * `metrics.cpu.classification` - classification metrics
        * `metrics.cpu.detection` - detection metrics
        * `metrics.cpu.regression` - regression metrics
        * `metrics.cpu.segmentation` - segmentation metrics
    * Torch - metrics, that calculates by `torch`
        * `metrics.torch.classification` - classification metrics
        * `metrics.torch.detection` - detection metrics
        * `metrics.torch.regression` - regression metrics
        * `metrics.torch.segmentation` - segmentation metrics
* Models
    * `decoders.unet` - UNet decoder, that automatically constructs by encoder
    * `encoders.common` - basic interfaces for encoders
    * `encoders.inception` - Inceptionv3 encoder
    * `encoders.mobile_net` - MobileNetv2 encoder
    * `encoders.resnet` - ResNet encoders
    * `albunet` - albunet model
    * `utils` - models utils
    * `weights_storage` - pretrained weights storage
* Pipeline steps
    * `regression.train` - train step for regression task
    * `regression.bagging` - bagging step for regression task
* `img_matcher` - image comparision and matching tool based on descriptors
* `mask_composer` - mask composer tools that can effectively combine masks for regular, instance or multiclass segmentation
* `utils` - some utils
* `viz` - image visualisation tools


