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
pip install -e .
Usage
Code Quality 🧹
make styleto format the codemake check_code_qualityto check code quality (PEP8 basically)black .ruff . --fix
Tests 🧪
pytests is used to run our tests.
License
MIT
1 maintainer
Release files for vision-mamba 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| vision_mamba-0.0.1.tar.gz | 3.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| vision_mamba-0.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 7.4 kB
Release files / vision_mamba-0.0.1.tar.gz
| Download URL | vision_mamba-0.0.1.tar.gz |
|---|---|
| Size | 3.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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poetry/1.3.2 CPython/3.11.0 Darwin/22.4.0
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Release files / vision_mamba-0.0.1-py3-none-any.whl
| Download URL | vision_mamba-0.0.1-py3-none-any.whl |
|---|---|
| Size | 3.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/1.3.2 CPython/3.11.0 Darwin/22.4.0
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