Fer-pytorch

Added the following fixes:
* Fixed the visualization for the case when there is more than one person on the image;
* Fixed the output video saving option in analyze_video method in the FER class;
* Refactored the tests;
* Updated the README file.

Key updates
* New pretrained weights that correspond to MobileNetv2 model has been added;
* Some minor fixes made to FER inference class for convenience purposes;
* Updated README file: the information about the web application has been added.

Key updates
* Config files are set using [Hydra](https://hydra.cc/docs/intro/) library.
* Updated README file.

Key updates
* Moved training to [Pytorch Lightning](https://www.pytorchlightning.ai/) package.
* Added possibility to choose model architecture for inference.

First release of **fer-pytorch** package for facial expression recognition implemented in Pytorch.

Key features:

- Possibility to train the wide variety of classification neural net architectures on FER+ dataset using the famous [timm](https://rwightman.github.io/pytorch-image-models/) library;
- Inference class for applying the fer model on different types of data (images, list of images, video files and e.t.c.);
- Output images and video files can be saved;
- Convenient interface for downloading the ready-to-use pretrained on FER+ dataset weights from the internet, as well as loading custom ones stored locally;
- Convenient tool for downloading the FER+ dataset and storing it in such a way, that the training can be launched immediately.