Metadata-Version: 2.4
Name: refrakt_core
Version: 0.1.0
Summary: A modular library for training, benchmarking, and experimenting with deep vision models.
Author-email: Akshath Mangudi <akshathmangudi@gmail.com>
License: MIT License
        
        Copyright (c) 2023 Akshath Mangudi
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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        SOFTWARE.
        
Project-URL: Homepage, https://github.com/refrakt-hub/refrakt_core
Project-URL: Bug Tracker, https://github.com/yourusername/refrakt_core/issues
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch>=2.0
Requires-Dist: torchvision
Requires-Dist: einops
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: requests
Requires-Dist: pillow
Dynamic: license-file

# refrakt-core (previously Re-Implementation)
Current re-implementations are:

| Paper                                                                                                          | Status    |
|----------------------------------------------------------------------------------------------------------------|-----------|
| [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) | Completed |
| [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)                               | Completed |
| [Autoencoders](https://arxiv.org/abs/2003.05991) | Completed |
| [Swin Transformer: Heirarchial Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) | Completed |
| [Attention Is All You Need](https://arxiv.org/abs/1706.03762) | Completed | 
| [A ConvNet for the 2020s](https://arxiv.org/pdf/2201.03545) | Completed | 
| [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/abs/1609.04802) | Completed |
| [A Simple Framework for Contrastive Learning of Visual Representations](https://arxiv.org/abs/2002.05709) | Completed |
| [Emerging Properties in Self-Supervised Vision Transformers](https://arxiv.org/abs/2104.14294) | Formulation |
| [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) | Formulation |
| [Masked Siamese Networks for Label-Efficient Learning](https://arxiv.org/abs/2204.07141) | Formulation |
| [Lagrangian Neural Networks](https://arxiv.org/abs/2003.04630) | Formulation |

## Contributions:

These are my personal implementations in order to educate myself. That being said, if there are any issues with the
code, such as incorrect math,
not enough comments or documentation, or poor modularity, please create an issue so I can review and make changes. Pull
requests must be the last resort.

## Licensing:

This repository is under the MIT License. See the LICENSE file for more details.
