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+<h1 id="model-classification-in-rocket-league-sideswipe">Model
+classification in Rocket League Sideswipe</h1>
+<p>Laurent Fainsin Damien Guillotin Pierre-Eliot Jourdan</p>
+<p>Lien vers notre <a
+href="https://git.inpt.fr/tocard-inc/enseeiht/projet-classification">dépot
+gitlab</a></p>
+<h2 id="description">Description</h2>
+<p>Le but de ce projet est de permettre la reconnaissance
+(classification) de voitures dans le jeu vidéo mobile Rocket League
+Sideswipe, et ce peu importe les accessoires équipés tels que les roues,
+les chapeaux, les stickers, les couleurs…</p>
+<h2 id="méthodolgie-dacquisition-du-dataset">Méthodolgie d’acquisition
+du dataset</h2>
+<p>Pour acquérir les 400 000 images de notre dataset, nous avons
+simplement écrit un script capable d’automatiser la customisation des
+modèles ainsi que la prise de screenshots. Il nous aura fallu environ 18
+heures pour constituer notre dataset.</p>
+<p>Notre dataset est un dossier contenant toutes nos images et une base
+de données sqlite3 faisant le lien entre les noms de nos screenshots
+(des uuids) et les paramètre de la voiture dans l’image.</p>
+<p>Par exemple <code>ec7d32da-ad24-11ec-813b-e0d4e8390134.jpg</code>
+correspond au modèle de voiture 2 (la werewolf) à la team 0 (les bleus),
+à la couleur primaire 50%, à la couleur secondaire 0%, au chapeau n°12
+(<insérer nom chapeau>), au sticker n°1 (<insérer nom sticker>), à la
+roue n°4 (<insérer nom roue>) et à la 3ème rotation.</p>
+<p>Voici l’image associée :</p>
+<p><img src="image_methodo.jpg" /></p>
+<h2 id="méthodologie">Méthodologie</h2>
+<ul class="incremental">
+<li>Description de votre méthodologie pour partitionner les images en
+ensembles d’entraı̂nement, de validation et de test.</li>
+</ul>
+<p>-&gt; Voir la doc de tensorflow.</p>
+<h2 id="pronostic">Pronostic</h2>
+<p>Si l’on décide d’évaluer le réseau sur des voitures avec une rotation
+qui est la même que lors de l’aquisition des données, le résultat
+devrait être plus que correct mais si l’on évalue le réseau sur une
+rotation différente, c’est la que les problèmes peuvent arriver, il se
+peut que l’on sur-apprenne la discretion de nos rotations.</p>
+<h2 id="chargement-de-nos-données">Chargement de nos données</h2>
+<ul class="incremental">
+<li>Script de chargement de vos données.</li>
+</ul>
+<h2 id="exemple-du-dataset">Exemple du dataset</h2>
+<p><img src="demo_datas.gif" /></p>
+<table>
+<colgroup>
+<col style="width: 8%" />
+<col style="width: 23%" />
+<col style="width: 23%" />
+<col style="width: 23%" />
+<col style="width: 23%" />
+</colgroup>
+<thead>
+<tr class="header">
+<th style="text-align: left;">Rotation</th>
+<th style="text-align: center;">Octane</th>
+<th style="text-align: center;">AfterShock</th>
+<th style="text-align: center;">Werewolf</th>
+<th style="text-align: center;">Breakout</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td style="text-align: left;">0</td>
+<td style="text-align: center;"><img src="demo_datas/0_0.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/1_0.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/2_0.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/3_0.jpg" /></td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">1</td>
+<td style="text-align: center;"><img src="demo_datas/0_1.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/1_1.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/2_1.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/3_1.jpg" /></td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">2</td>
+<td style="text-align: center;"><img src="demo_datas/0_2.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/1_2.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/2_2.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/3_2.jpg" /></td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">3</td>
+<td style="text-align: center;"><img src="demo_datas/0_3.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/1_3.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/2_3.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/3_3.jpg" /></td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">4</td>
+<td style="text-align: center;"><img src="demo_datas/0_4.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/1_4.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/2_4.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/3_4.jpg" /></td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">5</td>
+<td style="text-align: center;"><img src="demo_datas/0_5.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/1_5.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/2_5.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/3_5.jpg" /></td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">6</td>
+<td style="text-align: center;"><img src="demo_datas/0_6.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/1_6.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/2_6.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/3_6.jpg" /></td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">7</td>
+<td style="text-align: center;"><img src="demo_datas/0_7.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/1_7.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/2_7.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/3_7.jpg" /></td>
+</tr>
+<tr class="odd">
+<td style="text-align: left;">8</td>
+<td style="text-align: center;"><img src="demo_datas/0_8.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/1_8.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/2_8.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/3_8.jpg" /></td>
+</tr>
+<tr class="even">
+<td style="text-align: left;">9</td>
+<td style="text-align: center;"><img src="demo_datas/0_9.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/1_9.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/2_9.jpg" /></td>
+<td style="text-align: center;"><img src="demo_datas/3_9.jpg" /></td>
+</tr>
+</tbody>
+</table>
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+# Model classification in Rocket League Sideswipe
+
+Laurent Fainsin --
+Damien Guillotin --
+Pierre-Eliot Jourdan
+
+Lien vers notre [dépot gitlab](https://git.inpt.fr/tocard-inc/enseeiht/projet-classification)
+
+## Description
+
+Le but de ce projet est de permettre la reconnaissance (classification) de voitures dans le jeu vidéo mobile Rocket League Sideswipe, et ce peu importe les accessoires équipés tels que les roues, les chapeaux, les stickers, les couleurs...
+
+## Méthodolgie d'acquisition du dataset
+
+Pour acquérir les 400 000 images de notre dataset, nous avons simplement écrit un script capable d'automatiser la customisation des modèles ainsi que la prise de screenshots. Il nous aura fallu environ 18 heures pour constituer notre dataset.
+
+Notre dataset est un dossier contenant toutes nos images et une base de données sqlite3 faisant le lien entre les noms de nos screenshots (des uuids) et les paramètre de la voiture dans l'image.
+
+Par exemple `ec7d32da-ad24-11ec-813b-e0d4e8390134.jpg` correspond au modèle de voiture 2 (la werewolf) à la team 0 (les bleus), à la couleur primaire 50%, à la couleur secondaire 0%, au chapeau n°12 (<insérer nom chapeau>), au sticker n°1 (<insérer nom sticker>), à la roue n°4 (<insérer nom roue>) et à la 3ème rotation.
+
+Voici l'image associée :
+
+![](image_methodo.jpg)
+
+## Méthodologie
+
+* Description de votre méthodologie pour partitionner les images en ensembles d’entraı̂nement, de validation et de test.
+
+-> Voir la doc de tensorflow.
+
+## Pronostic
+
+Si l'on décide d'évaluer le réseau sur des voitures avec une rotation qui est la même que lors de l'aquisition des données, le résultat devrait être plus que correct mais si l'on évalue le réseau sur une rotation différente, c'est la que les problèmes peuvent arriver, il se peut que l'on sur-apprenne la discretion de nos rotations.
+
+## Chargement de nos données
+
+* Script de chargement de vos données.
+
+## Exemple du dataset
+
+![](demo_datas.gif)
+
+| Rotation |         Octane          |       AfterShock        |        Werewolf         |        Breakout         |
+| :------- | :---------------------: | :---------------------: | :---------------------: | :---------------------: |
+| 0        | ![](demo_datas/0_0.jpg) | ![](demo_datas/1_0.jpg) | ![](demo_datas/2_0.jpg) | ![](demo_datas/3_0.jpg) |
+| 1        | ![](demo_datas/0_1.jpg) | ![](demo_datas/1_1.jpg) | ![](demo_datas/2_1.jpg) | ![](demo_datas/3_1.jpg) |
+| 2        | ![](demo_datas/0_2.jpg) | ![](demo_datas/1_2.jpg) | ![](demo_datas/2_2.jpg) | ![](demo_datas/3_2.jpg) |
+| 3        | ![](demo_datas/0_3.jpg) | ![](demo_datas/1_3.jpg) | ![](demo_datas/2_3.jpg) | ![](demo_datas/3_3.jpg) |
+| 4        | ![](demo_datas/0_4.jpg) | ![](demo_datas/1_4.jpg) | ![](demo_datas/2_4.jpg) | ![](demo_datas/3_4.jpg) |
+| 5        | ![](demo_datas/0_5.jpg) | ![](demo_datas/1_5.jpg) | ![](demo_datas/2_5.jpg) | ![](demo_datas/3_5.jpg) |
+| 6        | ![](demo_datas/0_6.jpg) | ![](demo_datas/1_6.jpg) | ![](demo_datas/2_6.jpg) | ![](demo_datas/3_6.jpg) |
+| 7        | ![](demo_datas/0_7.jpg) | ![](demo_datas/1_7.jpg) | ![](demo_datas/2_7.jpg) | ![](demo_datas/3_7.jpg) |
+| 8        | ![](demo_datas/0_8.jpg) | ![](demo_datas/1_8.jpg) | ![](demo_datas/2_8.jpg) | ![](demo_datas/3_8.jpg) |
+| 9        | ![](demo_datas/0_9.jpg) | ![](demo_datas/1_9.jpg) | ![](demo_datas/2_9.jpg) | ![](demo_datas/3_9.jpg) |
\ No newline at end of file
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diff --git a/src/main.py b/src/main.py
index a803ea4..62ea4ee 100644
--- a/src/main.py
+++ b/src/main.py
@@ -10,7 +10,7 @@ NB_ETA = 25
 
 NB_ROTATIONS = 10
 
-N_START = 5245
+N_START = 7023
 N_STOP = 0
 
 DELAY_TRANSITION = 0.3