feat(scoring): precision + success

.gitignore

@@ -4,3 +4,5 @@ models
 __pycache__
 
 venv
+
+*.avi

README.md

@@ -1,7 +1,7 @@
 # DNN [![Profile][title-img]][profile]
 
 [title-img]:https://img.shields.io/badge/-SCIA--PRIME-red
-[profile]:https://github.com/bictole
+[profile]:https://github.com/Pypearl
 
 ## AUTHORS
 Alexandre Lemonnier <alexandre.lemonnier@epita.fr\>\
@@ -24,4 +24,38 @@ Les surveys sont des sortes de méta-analyse d'un domaine (ici le visual object
 Pour avoir l'article entier : 
 https://sci-hub.hkvisa.net/
 
-Puis entrer le doi de l'article : doi.org/10.1145/3309665
+Puis entrer le doi de l'article : doi.org/10.1145/3309665
+
+## Installation
+
+```bash
+python3 -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
+```
+
+
+## Description
+
+This github contains multiple notebook to execute in a specific order.  
+
+---
+
+The following notebooks creates .py files that are used by the other notebooks.
+
+- [dataset](dataset.ipynb) : Download the dataset and extract it with a specific structure
+- [model](model.ipynb) : Load the models with their default values from different github
+- [scoring](scoring.ipynb) : Define function to score the models predictions
+
+--- 
+
+The following notebook will create folder containing the results of the benchmark.
+
+- [benchmark](benchmark.ipynb) : Run the benchmark for each model on the dataset using the scoring functions
+
+---
+
+The following notebooks will create graphs and tables to analyse the results of the benchmark.
+
+- [benchmark_stats](benchmark_stats.ipynb) : Compute the statistics of the benchmark using the files generated by the previous notebook
+