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+ border-left: 1px solid #1b2d3e; +} + +hr { + height: 1px; + margin: 2em 0; + border: 0; + background: #184165; +} + +details { + margin: 1em 0; +} +details[open] { + padding-bottom: 0.5em; + border-bottom: 1px solid #184165; +} + +summary { + display: inline-block; + font-weight: 700; + border-bottom: 1px dashed; + cursor: pointer; +} +summary::-webkit-details-marker { + display: none; +} + +noscript { + color: #a15433; +} + +::selection { + background: rgba(21, 112, 171, 0.25); +} diff --git a/docs/RAPPORT_0.html b/docs/index.html similarity index 60% rename from docs/RAPPORT_0.html rename to docs/index.html index e7953d4..9e6cf9d 100644 --- a/docs/RAPPORT_0.html +++ b/docs/index.html @@ -1,9 +1,35 @@ -

Model -classification in Rocket League Sideswipe

-

Laurent Fainsin Damien Guillotin Pierre-Eliot Jourdan

+ + + + + + + rapport + + + + + +
+

Classification dans +Rocket League Sideswipe

+Laurent Fainsin — Damien Guillotin — Pierre-Eliot Jourdan +

Lien vers notre dépot -gitlab

+gitlab.

+

Lien vers notre dataset.

Description

Le but de ce projet est de permettre la reconnaissance (classification) de voitures dans le jeu vidéo mobile Rocket League @@ -11,36 +37,57 @@ 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 +

Pour acquérir les quelque 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.

+(des uuids) et les paramètres 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 -(), au sticker n°1 (), à la -roue n°4 () et à la 3ème rotation.

-

Voici l’image associée :

+(le Rasta), au sticker n°1 (le Kana), à la roue n°4 (l’Helicoprion) et à +la 3ème rotation.

+

Voici l’image associée:

Méthodologie

- -

-> Voir la doc de tensorflow.

+

Nous pensons dans un premier temps fractionner notre dataset de la +manière suivante:

+ + + + + + + + + + + + + + + +
TrainTestValidation
70%15%15%
+

Nous utiliserons les méthodes fournies par Tensorflow pour split +notre dataset via ces proportions.

+

Notre objectif final serait d’utiliser l’augmentation pour obtenir +plus d’un million d’images.

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 +qui est la même que lors de l’acquisition 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.

+rotation différente, c’est là que les problèmes peuvent arriver, il se +peut que l’on sur-apprenne la discrétion de nos rotations.

Chargement de nos données

- +

Comme nous possédons un grand nombre d’images et comme celles-ci sont +réliées via un csv/sqlite3, nous allons devoir utiliser une structure +spéciale de Tensorflow pour charger les images via leur association +dans le csv/sqlite3, et aussi pour permettre de ne pas tout charger en +mémoire (car notre dataset sera très probablement de taille supérieur à +la RAM de nos machines).

Exemple du dataset

@@ -53,7 +100,7 @@ peut que l’on sur-apprenne la discretion de nos rotations.

- + @@ -62,70 +109,70 @@ peut que l’on sur-apprenne la discretion de nos rotations.

- + - + - + - + - + - + - + - + - + - + @@ -133,3 +180,5 @@ peut que l’on sur-apprenne la discretion de nos rotations.

RotationRotation Octane AfterShock Werewolf
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11
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66
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+ + diff --git a/docs/RAPPORT_0.md b/docs/rapport.md similarity index 96% rename from docs/RAPPORT_0.md rename to docs/rapport.md index 30cd0a1..528223c 100644 --- a/docs/RAPPORT_0.md +++ b/docs/rapport.md @@ -1,10 +1,15 @@ +
+ # Classification dans Rocket League Sideswipe Laurent Fainsin — Damien Guillotin — Pierre-Eliot Jourdan +
-Lien vers notre [dépot gitlab](https://git.inpt.fr/tocard-inc/enseeiht/projet-classification) +Lien vers notre [dépot gitlab](https://git.inpt.fr/tocard-inc/enseeiht/projet-classification). + +Lien vers notre [dataset](https://fainsil.users.inpt.fr/content/DL/dataset.zip). ## Description