From 125eb8cbd36c33922b3837194489033f66037032 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Laure=CE=B7t?= Date: Mon, 11 Apr 2022 23:08:27 +0200 Subject: [PATCH] fix: files not needed anymore --- docs/bigstone.css | 881 ---------------------------------------------- docs/rapport.html | 183 ---------- 2 files changed, 1064 deletions(-) delete mode 100644 docs/bigstone.css delete mode 100644 docs/rapport.html diff --git a/docs/bigstone.css b/docs/bigstone.css deleted file mode 100644 index d6cd3c4..0000000 --- a/docs/bigstone.css +++ /dev/null @@ -1,881 +0,0 @@ -@charset "UTF-8"; -/*! - * awsm.css v3.0.7 (https://igoradamenko.github.io/awsm.css/) - * Copyright 2015 Igor Adamenko (https://igoradamenko.com) - * Licensed under MIT (https://github.com/igoradamenko/awsm.css/blob/master/LICENSE.md) - */ -html { - font-family: system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, - Oxygen, Ubuntu, Cantarell, "PT Sans", "Open Sans", "Fira Sans", "Droid Sans", - "Helvetica Neue", Helvetica, Arial, sans-serif; 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-

Classification dans -Rocket League Sideswipe

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

Lien vers notre dépot GitHub.

-

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 -Sideswipe, et ce peu importe les accessoires équipés tels que les roues, -les chapeaux, les stickers, les couleurs…

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Méthodolgie d’acquisition -du dataset

-

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 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 -(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

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

- - - - - - - - - - - - - - - -
TrainTestValidation
70%15%15%
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Nous utiliserons les méthodes fournies par Tensorflow pour split -notre dataset via ces proportions.

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

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Pronostic

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Si l’on décide d’évaluer le réseau sur des voitures avec une rotation -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 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

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RotationOctaneAfterShockWerewolfBreakout
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