semvec/pypi-packages huggingface dataset:
quickmatch 0.1.0
A command-line tool for obtaining face-matcher embeddings across 4 SOTA face-matchers.
Requires Python: >=3.6

# QUICKMATCH

A simply command-line tool for conveniently using SOTA face-recognition networks. 

## Provided Face-Matchers

1. <a href="https://openaccess.thecvf.com/content/CVPR2022W/Biometrics/papers/Boutros_ElasticFace_Elastic_Margin_Loss_for_Deep_Face_Recognition_CVPRW_2022_paper.pdf">ElasticFace</a>
2. <a href="https://openaccess.thecvf.com/content_CVPR_2019/papers/Deng_ArcFace_Additive_Angular_Margin_Loss_for_Deep_Face_Recognition_CVPR_2019_paper.pdf">ArcFace</a>
3. <a href="https://openaccess.thecvf.com/content_cvpr_2017/papers/Liu_SphereFace_Deep_Hypersphere_CVPR_2017_paper.pdf">SphereFace</a>
4. <a href="https://arxiv.org/pdf/1503.03832">FaceNet</a>

## Installation

Installation can be simply done by `pip install quickmatch`. 

### Dependancies

The following Python libraries are required for running inference on the face-matchers and can be installed by using `pip install <library-name>`. The package will automatically install them at the versions that were used to test it.

```plaintext
easydict
torch
tqdm
facenet-pytorch
onedrivedownloader
```

## Usage

This library functions as a commandline tool which takes in a directory of images and the face-matcher you want to use create face-matcher embeddings that are stored in a `.pt` file. This `.pt` contains a stack of PyTorch tensors corresponding to all images. Note that the shape of the PyTorch tensor stack will be `[N, D]` where `N` is the number of images you provided in the input directory and `D` is the dimension that the matchers embed to (`D`$=512$ for all matchers).

For example, if you want to use "ArcFace" on a folder of images called `my_face_shots`, run the command by specifying these inputs and the output path of the file.

```bash
python3 -m ez-face-match --matcher=ArcFace --input-folder=my_face_shots --output-path=./matcher_embeddings.pt
```

The `main.py` file automatically checks if `"cuda"` is enabled or not (<a href="https://pytorch.org/">PyTorch</a> must be installed and compiled with CUDA). Note, however, using a purely CPU runtime for the inference of these networks may take significantly longer. Additionally, upon first time use, the script will create a folder named `quickmatch_pretrained_models` at your default `pip` cache location. Here, the model weights will be downloaded and loaded automatically.
catalight 0.2.0
Photoreactor system automation, data handling, and analysis.
Requires Python: >=3.7

Welcome to Catalight!
=======================
.. figure:: https://raw.githubusercontent.com/Dionne-Lab/catalight/main/docs/source/_static/images/catalight_header_g.png
    :width: 800

Introduction
------------
Catalight is a tool set developed by scientists at Stanford to help automate photocatalysis experiments. Catalight is capable of connecting to hardware, coordinating instruments to run a variety of different experimental procedures, and organizing and analyzing data collected by the system. The project is designed in a modular format to enable users to bring their own unique hardware while still utilizing the core features of experiment management and data handling. The program functions in both a scripted version and a GUI to aid users in planning experimental procedures and manually controlling hardware.

To date, catalight is compatible with Alicat mass flow controllers, heater systems controlled by Watlow components, Measurement Computing data acquisition boards (used for communication with (ThorLabs laser diode drivers), NewPort 843-R power meter, and SRI gas chromatographs. Support for additional hardware is planned for the future, including FTIR data collection and analysis and NKT Photonics lasers. We strongly encourage interested users to consider contributing to the project by adding new device support and helping expand the catalight functionality.

**ReadtheDocs Page:**
https://catalight.readthedocs.io/en/latest/

**PyPI Page:**
https://pypi.org/project/catalight/


D-Lab Hardware Configuration:

.. figure:: https://github.com/Dionne-Lab/catalight/raw/main/docs/source/_static/images/overview.png
    :width: 800