Metadata-Version: 2.1
Name: fastface
Version: 0.1.1
Summary: A face detection framework for edge devices using pytorch lightning
Home-page: https://github.com/borhanMorphy/light-face-detection
Author: Ömer BORHAN
Author-email: borhano.f.42@gmail.com
License: MIT
Project-URL: Documentation, https://fastface.readthedocs.io/en/latest/
Description: # FastFace: Lightweight Face Detection Framework
        
        ![PyPI](https://img.shields.io/pypi/v/fastface)
        [![Documentation Status](https://readthedocs.org/projects/fastface/badge/?version=latest)](https://fastface.readthedocs.io/en/latest/?badge=latest)
        [![Downloads](https://pepy.tech/badge/fastface)](https://pepy.tech/project/fastface)
        ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/fastface)
        ![PyPI - License](https://img.shields.io/pypi/l/fastface)
        
        **Easy-to-use face detection framework, developed using [pytorch-lightning](https://www.pytorchlightning.ai/).**<br>
        **Checkout [documentation](https://fastface.readthedocs.io/en/latest/) for more.**
        
        ## Key Features
        * :fire: **Use pretrained models for inference with just few lines of code**
        * :chart_with_upwards_trend: **Evaluate models on different datasets**
        * :hammer_and_wrench: **Train and prototype new models, using pre-defined architectures**
        * :rocket: **Export trained models with ease, to use in production**
        
        ## Contents
        - [Installation](#installation)
        - [Pretrained Models](#pretrained-models)
        - [Demo](#demo)
        - [Benchmarks](#benchmarks)
        - [Tutorials](#tutorials)
        - [References](#references)
        - [Citations](#citations)
        
        ## Installation
        From PyPI
        ```
        pip install fastface -U
        ```
        
        From source
        ```
        git clone https://github.com/borhanMorphy/light-face-detection.git
        cd light-face-detection
        pip install .
        ```
        
        ## Pretrained Models
        Pretrained models can be accessable via `fastface.FaceDetector.from_pretrained(<name>)`
        Name|Architecture|Configuration|Parameters|Model Size|Link
        :------:|:------:|:------:|:------:|:------:|:------:
        **lffd_original**|lffd|original|2.3M|9mb|[weights](https://drive.google.com/file/d/1qFRuGhzoMWrW9WNlWw9jHXPY51MBssQD/view?usp=sharing)
        **lffd_slim**|lffd|slim|1.5M|6mb|[weights](https://drive.google.com/file/d/1UOHllYp5NY4mV7lHmq0c9xsryRIufpAQ/view?usp=sharing)
        
        ## Demo
        Using package
        ```python
        import fastface as ff
        import imageio
        
        # load image as RGB
        img = imageio.imread("<your_image_file_path>")[:,:,:3]
        
        # build model with pretrained weights
        model = ff.FaceDetector.from_pretrained("lffd_original")
        # model: pl.LightningModule
        
        # get model summary
        model.summarize()
        
        # set model to eval mode
        model.eval()
        
        # [optional] move model to gpu
        model.to("cuda")
        
        # model inference
        preds, = model.predict(img, det_threshold=.8, iou_threshold=.4)
        # preds: {
        #    'boxes': [[xmin, ymin, xmax, ymax], ...],
        #    'scores':[<float>, ...]
        # }
        
        ```
        
        Using [demo.py](/demo.py) script
        ```
        python demo.py --model lffd_original --device cuda --input <your_image_file_path>
        ```
        sample output;
        ![alt text](resources/friends.jpg)
        
        ## Benchmarks
        **Following results are obtained with this repository**
        
        #### WIDER FACE
        validation set results
        Name|Easy|Medium|Hard|
        :------:|:------:|:------:|:------:
        **lffd_original**|**0.893**|**0.866**|**0.758**
        **lffd_slim**|**0.866**|**0.854**|**0.742**
        
        
        ## Tutorials
        * [Widerface Benchmark](./tutorials/widerface_benchmark/README.md)
        * [BentoML Deployment](./tutorials/bentoml_deployment/README.md)
        
        ## References
        - [LFFD Paper](https://arxiv.org/pdf/1904.10633.pdf)
        - [Official LFFD Implementation](https://github.com/YonghaoHe/A-Light-and-Fast-Face-Detector-for-Edge-Devices)
        
        ## Citations
        ```bibtex
        @inproceedings{LFFD,
            title={LFFD: A Light and Fast Face Detector for Edge Devices},
            author={He, Yonghao and Xu, Dezhong and Wu, Lifang and Jian, Meng and Xiang, Shiming and Pan, Chunhong},
            booktitle={arXiv:1904.10633},
            year={2019}
        }
        ```
        
Keywords: pytorch_lightning,face detection,edge AI,LFFD
Platform: UNKNOWN
Classifier: Environment :: Console
Classifier: Natural Language :: English
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: docs
Provides-Extra: dev
Provides-Extra: all
