Metadata-Version: 2.1
Name: local-feature-tutorial
Version: 0.0.2
Summary: DIY image retrieval with spatial verification
Home-page: https://github.com/ducha-aiki/local_feature_tutorial/tree/master/
Author: Dmytro Mishkin
Author-email: ducha.aiki@gmail.com
License: Apache Software License 2.0
Keywords: wide baseline stereo,SIFT,HardNet,image retrieval
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: google-colab
Requires-Dist: Pillow
Requires-Dist: opencv-python (>=4.4.0)
Requires-Dist: torch
Requires-Dist: matplotlib
Requires-Dist: wget
Requires-Dist: kornia (>=0.4.1)
Requires-Dist: torchvision
Requires-Dist: fastprogress (>=0.2.4)
Requires-Dist: numpy
Requires-Dist: pydegensac

# Hands-on wide baseline tutorial
> Summary description here.


We will build, step-by-step, an image retrieval with spatial verification and use it for the dataset cleaning purpose. We will not build the components from scratch, instead will be using a ready packages.

## Install

`pip install local_feature_tutorial`

## How to use

Fill me in please! Don't forget code examples:

```python
1+1
```




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