https://github.com/PiePline/pietoolbelt

Toolbelt for PiePline training pipeline
https://github.com/PiePline/pietoolbelt

computer-vision dataset deep-learning loss-functions metrics neural-network

Last synced: 3 months ago
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Toolbelt for PiePline training pipeline

• Host: GitHub
• URL: https://github.com/PiePline/pietoolbelt
• Owner: PiePline
• License: mit
• Created: 2020-05-04T12:55:56.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-05-01T13:45:34.000Z (over 1 year ago)
• Last Synced: 2024-07-17T13:20:29.528Z (4 months ago)
• Topics: computer-vision, dataset, deep-learning, loss-functions, metrics, neural-network
• Language: Python
• Size: 176 KB
• Stars: 1
• Watchers: 2
• Forks: 2
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 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

* `augmentations`

    * `augmentations.detection` - predefined augmentations for detection task

    * `augmentations.segmentation` - predefined augmentations for segmentation task

* `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` - models zoo and constructors

    * `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

* `steps` - some training process steps

    * `regression.train` - train step for regression task

    * `regression.bagging` - bagging step for regression task

    * `segmentation.bagging` - bagging step for segmentation task

    * `segmentation.inference` - inference for segmentation model

    * `segmentation.predict` - predict step for segmentation task

    * `stratification` - dataset stratification class

* `img_matcher` - image comparison and matching tool based on descriptors

* `mask_composer` - mask composer tools that can effectively combine masks for regular, instance or multiclass segmentation

* `train_config` - some predefined train configs for [PiePline](https://github.com/PiePline/piepline)

* `tta` - test time augmentations 

* `utils` - some utils

* `viz` - image visualisation tools