Metadata-Version: 2.1
Name: vision-mamba
Version: 0.0.1
Summary: Vision Mamba - Pytorch
Home-page: https://github.com/kyegomez/VisionMamba
License: MIT
Keywords: artificial intelligence,deep learning,optimizers,Prompt Engineering
Author: Kye Gomez
Author-email: kye@apac.ai
Requires-Python: >=3.6,<4.0
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Dist: einops
Requires-Dist: swarms
Requires-Dist: torch
Requires-Dist: zetascale
Project-URL: Documentation, https://github.com/kyegomez/VisionMamba
Project-URL: Repository, https://github.com/kyegomez/VisionMamba
Description-Content-Type: text/markdown

[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)

# Vision Mamba
Implementation of Vision Mamba from the paper: "Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model" It's 2.8x faster than DeiT and saves 86.8% GPU memory when performing batch inference to extract features on high-res images



## Installation

You can install the package using pip

```bash
pip install -e .
```

# Usage
```python

```



### Code Quality 🧹

- `make style` to format the code
- `make check_code_quality` to check code quality (PEP8 basically)
- `black .`
- `ruff . --fix`

### Tests 🧪

[`pytests`](https://docs.pytest.org/en/7.1.x/) is used to run our tests.

# License
MIT

