https://github.com/Scitator/catalyst-examples

Examples
https://github.com/Scitator/catalyst-examples

computer-vision data-science deep-learning deep-neural-networks deep-reinforcement-learning machine-learning python pytorch

Last synced: about 1 month ago
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Examples

• Host: GitHub
• URL: https://github.com/Scitator/catalyst-examples
• Owner: Scitator
• License: mit
• Archived: true
• Created: 2018-09-24T12:20:03.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2019-01-23T05:45:06.000Z (over 5 years ago)
• Last Synced: 2024-04-13T12:06:01.805Z (5 months ago)
• Topics: computer-vision, data-science, deep-learning, deep-neural-networks, deep-reinforcement-learning, machine-learning, python, pytorch
• Language: Jupyter Notebook
• Homepage: https://github.com/Scitator/catalyst
• Size: 63.5 KB
• Stars: 54
• Watchers: 3
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Catalyst examples

**Archived. Moved to [Catalyst](https://github.com/catalyst-team/catalyst)**

Run all examples from this dir.

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Notebooks

1. [cifar10 notebook](https://github.com/Scitator/catalyst-examples/blob/master/notebook-example.ipynb)

    - cifar10 classification model

    - simple example of lib usage

2. [segmentation notebook](https://github.com/Scitator/catalyst-examples/blob/master/segmentation-example.ipynb)

    - segmentation with unet

    - model training and inference

    - predictions visialization

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Pipelines

1. [cifar10 model training](https://github.com/Scitator/catalyst-examples/tree/master/cifar_simple)

    - pipeline example of lib usage

    - local and docker runs

    - tensorboard metrics visualization

2. [cifar10 model training with stages](https://github.com/Scitator/catalyst-examples/tree/master/cifar_stages)

    - pipeline example with stages support

3. [finetune](https://github.com/Scitator/catalyst-examples/tree/master/finetune)

    - classification model training and inference

    - different augmentations and stages usage example

    - index model creating

    - embeddings projector

    - LrFinder usage

    - grid search metrics visualization