Update README.md

README.md

@@ -1,152 +1,18 @@
 # SPIGA: Shape Preserving Facial Landmarks with Graph Attention Networks.
 
-[![Project Page](https://badgen.net/badge/color/Project%20Page/purple?icon=atom&label)](https://bmvc2022.mpi-inf.mpg.de/155/) 
-[![arXiv](https://img.shields.io/badge/arXiv-2210.07233-b31b1b.svg)](https://arxiv.org/abs/2210.07233)
-[![PyPI version](https://badge.fury.io/py/spiga.svg)](https://badge.fury.io/py/spiga)
-[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](LICENSE)
-[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/andresprados/SPIGA/blob/main/colab_tutorials/video_demo.ipynb)
 
-This repository contains the source code of **SPIGA, a face alignment and headpose estimator** that takes advantage of the complementary benefits from CNN and GNN architectures producing plausible face shapes in presence of strong appearance changes. 
+This repository contains the models weights of **SPIGA, a face alignment and headpose estimator** that takes advantage of the complementary benefits from CNN and GNN architectures producing plausible face shapes in presence of strong appearance changes. 
 
 <p align="center">
     <img src="https://raw.githubusercontent.com/andresprados/SPIGA/main/assets/spiga_scheme.png" width="80%">
 </p>
 
-**It achieves top-performing results in:**
-
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/shape-preserving-facial-landmarks-with-graph/pose-estimation-on-300w-full)](https://paperswithcode.com/sota/pose-estimation-on-300w-full?p=shape-preserving-facial-landmarks-with-graph)
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/shape-preserving-facial-landmarks-with-graph/head-pose-estimation-on-wflw)](https://paperswithcode.com/sota/head-pose-estimation-on-wflw?p=shape-preserving-facial-landmarks-with-graph)
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/shape-preserving-facial-landmarks-with-graph/pose-estimation-on-merl-rav)](https://paperswithcode.com/sota/pose-estimation-on-merl-rav?p=shape-preserving-facial-landmarks-with-graph)
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/shape-preserving-facial-landmarks-with-graph/face-alignment-on-merl-rav)](https://paperswithcode.com/sota/face-alignment-on-merl-rav?p=shape-preserving-facial-landmarks-with-graph)
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/shape-preserving-facial-landmarks-with-graph/face-alignment-on-wflw)](https://paperswithcode.com/sota/face-alignment-on-wflw?p=shape-preserving-facial-landmarks-with-graph)
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/shape-preserving-facial-landmarks-with-graph/face-alignment-on-300w-split-2)](https://paperswithcode.com/sota/face-alignment-on-300w-split-2?p=shape-preserving-facial-landmarks-with-graph)
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/shape-preserving-facial-landmarks-with-graph/face-alignment-on-cofw-68)](https://paperswithcode.com/sota/face-alignment-on-cofw-68?p=shape-preserving-facial-landmarks-with-graph)
-[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/shape-preserving-facial-landmarks-with-graph/face-alignment-on-300w)](https://paperswithcode.com/sota/face-alignment-on-300w?p=shape-preserving-facial-landmarks-with-graph)
-
 
 ## Setup
-The repository has been tested on Ubuntu 20.04 with CUDA 11.4, the latest version of cuDNN, Python 3.8 and Pytorch 1.12.1.
-To run the video analyzer demo or evaluate the algorithm, install the repository from the source code:
-
-```
-# Best practices: 
-#  1. Create a virtual environment.
-#  2. Install Pytorch according to your CUDA version.
-#  3. Install SPIGA from source code:
-
-git clone https://github.com/andresprados/SPIGA.git
-cd spiga
-pip install -e .  
-
-# To run the video analyzer demo install the extra requirements.
-pip install -e .[demo]
-```
-**Models:** By default, model weights are automatically downloaded on demand and stored at ```./spiga/models/weights/```.
-You can also download them from [Google Drive](https://drive.google.com/drive/folders/1olrkoiDNK_NUCscaG9BbO3qsussbDi7I?usp=sharing). 
-
-***Note:*** All the callable files provide a detailed parser that describes the behaviour of the program and their inputs. Please, check the operational modes by using the extension ```--help```.
-
-## Inference and Demo
-We provide an inference framework for SPIGA available at ```./spiga/inference```. The models can be easily deployed 
-in third-party projects by adding a few lines of code. Check out our inference and application tutorials
-for more information: 
-
-<div align="center">
-
-Tutorials | Notebook |
-:---: | :---: |
-Image Inference Example | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/andresprados/SPIGA/blob/main/colab_tutorials/image_demo.ipynb) |
-Face Video Analyzer Demo | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/andresprados/SPIGA/blob/main/colab_tutorials/video_demo.ipynb) |
-
-</div>
-
-### Face Video Analyzer Demo:
-The demo application provides a general framework for tracking, detecting and extracting features of human faces in images or videos.
-You can use the following commands to run the demo:
-
-```
-python ./spiga/demo/app.py \
-            [--input] \      # Webcam ID or Video Path. Dft: Webcam '0'.
-            [--dataset] \    # SPIGA pretrained weights per dataset. Dft: 'wflw'.
-            [--tracker] \    # Tracker name. Dft: 'RetinaSort'.
-            [--show] \       # Select the attributes of the face to be displayed. Dft: ['fps', 'face_id', 'landmarks', 'headpose']
-            [--save] \       # Save record.
-            [--noview] \     # Do not visualize window.
-            [--outpath] \    # Recorded output directory. Dft: './spiga/demo/outputs'
-            [--fps] \        # Frames per second.
-            [--shape] \      # Visualizer shape (W,H).
-```
-
-
-<p align="center">
-
-<img src="https://raw.githubusercontent.com/andresprados/SPIGA/main/assets/demo.gif" width=250px height=250px>
-&nbsp;&nbsp;&nbsp;
-<img src="https://raw.githubusercontent.com/andresprados/SPIGA/main/assets/results/carnaval.gif" width=300px height=250px>
-&nbsp;&nbsp;&nbsp;
-<img src="https://raw.githubusercontent.com/andresprados/SPIGA/main/assets/results/football.gif" width=230px height=250px>
-
-</p>
-
-***Note:*** For more information check the [Demo Readme](spiga/demo/readme.md) or call the app parser ```--help```.
-
-
-## Dataloaders and Benchmarks
-This repository provides general-use tools for the task of face alignment and headpose estimation:
-
-* **Dataloaders:** Training and inference dataloaders are available at ```./spiga/data```.
-Including the data augmentation tools used for training SPIGA and data-visualizer to analyze the dataset images and features.
-For more information check the [Data Readme](spiga/data/readme.md) .
-
-* **Benchmark:** A common benchmark framework to test any algorithm in the task of face alignment and headpose estimation
-is available at ```./spiga/eval/benchmark```. For more information check the following Evaluation Section and the [Benchmark Readme](spiga/eval/benchmark/readme.md).
-
-**Datasets:** To run the data visualizers or the evaluation benchmark please download the dataset images from the official websites 
-([300W](https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/), 
-[AFLW](https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/aflw/), 
-[WFLW](https://wywu.github.io/projects/LAB/WFLW.html), [COFW](http://www.vision.caltech.edu/xpburgos/ICCV13/)). 
-By default they should be saved following the next folder structure:
-```
-./spiga/data/databases/   # Default path can be updated by modifying 'db_img_path' in ./spiga/data/loaders/dl_config.py
-|
-└───/300w               
-│   └─── /images           
-│        | /private     
-│        | /test                   
-|        └ /train             
-|
-└───/cofw                   
-│   └─── /images
-|  
-└───/aflw                   
-│   └─── /data
-|        └ /flickr
-|  
-└───/wflw
-    └─── /images
-```
-**Annotations:** We have stored for simplicity the datasets annotations directly in ```./spiga/data/annotations```. We strongly recommend to move them out of the repository if you plan to use it as a git directory.
-
-**Results:** Similar to the annotations problem, we have stored the SPIGA results in ```./spiga/eval/results/<dataset_name>```. Remove them if need it.
-
-## Evaluation
-The models evaluation is divided in two scripts:
-
-**Results generation**: The script extracts the data alignments and headpose estimation from the desired ``` <dataset_name>``` trained network. Generating a ```./spiga/eval/results/results_<dataset_name>_test.json``` file which follows the same data structure defined by the dataset annotations.
-
-```
-python ./spiga/eval/results_gen.py <dataset_name>
-``` 
- 
-**Benchmark metrics**: The script generates the desired landmark or headpose estimation metrics. We have implemented an useful benchmark which allows you to test any model using a results file as input. 
-
-```
-python ./spiga/eval/benchmark/evaluator.py /path/to/<results_file.json> --eval lnd pose -s
-``` 
+The repository is available on [github](https://github.com/andresprados/SPIGA.git)
 
-***Note:*** You will have to interactively select the NME_norm and other parameters in the terminal window.
+## Results
 
-### Results Sum-up
 <details>
   <summary> WFLW Dataset </summary>