1702 lines
967 KiB
Plaintext
1702 lines
967 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Kd4SVfDxz5aw"
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},
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"source": [
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"<h1>Localisation et détection d'objet</h1>\n",
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"\n",
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"Dans ce TP, nous allons mettre en pratique certaines des méthodes présentées en cours pour localiser des objets dans une image.\n",
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"\n",
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"En localisation et détection, on cherche à déterminer la position d'un objet, ainsi que sa classe, sous la forme d'une boîte englobante de largeur $b_w$ et hauteur $b_h$, et dont le centre a pour coordonnées le point $(b_x, b_y)$. \n",
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"\n",
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"<center> <img src=\"https://drive.google.com/uc?id=1_jHHv6ZDe-3Xz25jIZ6o177laBEfmMRR\" style=\"width:500;height:300px;\"></center>\n",
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"<caption><center> Figure 1: Modèle de boîte englobante utilisé pour la localisation </center></caption>\n",
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"\n",
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"Le problème de localisation considère qu'un seul objet est présent sur l'image, alors que le problème de détection cherche à déterminer l'ensemble des objets présents sur l'image.\n",
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"\n",
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"\n",
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"\n",
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"Pour commencer, récupérez les images de la base de données :\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {
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"id": "2ZjveWpbuNeV"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"fatal: le chemin de destination 'mangeoires_loc' existe déjà et n'est pas un répertoire vide.\n"
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]
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}
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],
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"source": [
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"!git clone https://github.com/axelcarlier/mangeoires_loc.git"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "LF6aRZLE2-Yl"
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},
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"source": [
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"La base de données consiste en des photographies prises par une caméra reliée à une Raspberry Pi, cachée dans une mangeoire. Plusieurs mangeoires sont disséminées dans la nature en Occitanie, et l'objectif de [ce projet](https://econect.cnrs.fr/) est la reconnaissance des espèces et le comptage des individus qui viennent se poser devant le caméra, afin de suivre l'évolution des populations d'oiseaux et ainsi monitorer la biodiversité.\n",
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"\n",
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"La base de données qui vous est fournie regroupe 11 espèces d'animaux, majoritairement des oiseaux, désignés par un code : \n",
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"\n",
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"1. Mésange charbonnière (**MESCHA**)\n",
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"2. Verdier d'Europe (**VEREUR**)\n",
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"3. Écureuil roux (**ECUROU**)\n",
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"4. Pie bavarde (**PIEBAV**)\n",
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"5. Sittelle torchepot (**SITTOR**)\n",
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"6. Pinson des arbres (**PINARB**)\n",
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"7. Mésange noire (**MESNOI**)\n",
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"8. Mésange nonnette (**MESNON**)\n",
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"9. Mésange bleue (**MESBLE**)\n",
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"10. Rouge-gorge (**ROUGOR**)\n",
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"11. Accenteur mouchet (**ACCMOU**)\n",
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"\n",
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"\n",
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"\n",
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"<center> <img src=\"https://drive.google.com/uc?id=1Eit86N4D0Ea7ai1rBa0o-GmTwckhP9i0\" width=200>\n",
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"<img src=\"https://drive.google.com/uc?id=1lC7WL93UqDT_KV2m21yx99N5dkf98nCY\" width=200>\n",
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"<img src=\"https://drive.google.com/uc?id=10tzJORcSrSckDWmBDBtc8FUirxdbRxri\" width=200>\n",
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"<img src=\"https://drive.google.com/uc?id=1IHZp_B6bc8bADtAReRHhcyOjDQZAXDBQ\" width=200></center>\n",
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"<caption><center> Figure 2: Exemples d'images de la base de données </center></caption>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "kxZ6cVouz9Lp"
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},
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"source": [
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"# Localisation et classification d'objet\n",
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"\n",
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"Dans cette partie, nous allons nous concentrer sur le problème de la localisation d'un seul objet par classe. La base de données a été épurée pour se concentrer uniquement sur ce cas.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {
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"id": "SBA3fa8RSDpt"
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},
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"outputs": [],
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"source": [
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"import PIL\n",
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"from PIL import Image\n",
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"import csv\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"import tensorflow as tf\n",
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"\n",
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"from tensorflow import keras\n",
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"from tensorflow.keras import layers\n",
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"from tensorflow.keras import models\n",
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"from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout, Input\n",
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"from tensorflow.keras.models import Model, Sequential\n",
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"from tensorflow.keras.optimizers import Adam\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "HKRm5oT-_Qsw"
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},
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"source": [
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"## Préparation des données\n",
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"\n",
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"Le code ci-dessous permet de charger les données et les formater pour la classification. Prenez le temps de regarder un peu le format des labels $y$.\n",
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"Notez que les images sont rendues carrées lors du chargement."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {
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"id": "Q54zSuMvGM-5"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['MESCHA', 'VEREUR', 'ECUROU', 'PIEBAV', 'SITTOR', 'PINARB', 'MESNOI', 'MESNON', 'MESBLE', 'ROUGOR', 'ACCMOU']\n"
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]
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}
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],
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"source": [
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"# Lecture du CSV contenant les informations relatives à la base de données\n",
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"dataset = []\n",
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"with open('mangeoires_loc/bd_mangeoires_equilibre.csv', newline='') as csvfile:\n",
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"\tfilereader = csv.reader(csvfile, delimiter=' ', quotechar='|')\n",
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"\tfor row in filereader:\n",
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"\t\tdata = row[0].split(',')\n",
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"\t\tif data[0] != 'Data':\n",
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"\t\t\tbox = [float(data[5]), float(data[6]), float(data[7]), float(data[8])]\n",
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"\t\t\tnew_entry = {'type': data[0], 'specie': data[1], 'path': data[2], 'shape': [float(data[3]), float(data[4])], 'box': box}\n",
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"\t\t\tdataset.append(new_entry)\n",
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"\n",
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"# Nombre de classes de la base de données et intitulé des classes\n",
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"class_labels = list(dict.fromkeys([item['specie'] for item in dataset]))\n",
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"num_classes = len(class_labels)\n",
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"\n",
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"# Extraction des données d'apprentissage et de test \n",
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"dataset_train = [item for item in dataset if item['type']=='TRAIN']\n",
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"dataset_test = [item for item in dataset if item['type']=='TEST']\n",
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"\n",
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"print(class_labels)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {
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"id": "JPzqdJBVJLWJ"
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},
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"outputs": [],
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"source": [
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"def build_localization_tensors(image_size, dataset, num_classes):\n",
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" # Préparation des structures de données pour x et y\n",
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" x = np.zeros((len(dataset), image_size, image_size, 3))\n",
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" y = np.empty((len(dataset), num_classes + 5)) # 1 + 4 + num_classes : présence / boîte englobante / classes\n",
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"\n",
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" # Compteur de parcours du dataset\n",
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" i = 0\n",
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"\n",
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" for item in dataset:\n",
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" # Lecture de l'image\n",
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" img = Image.open('mangeoires_loc/' + item['path'])\n",
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" # Mise à l'échelle de l'image\n",
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" img = img.resize((image_size,image_size), Image.ANTIALIAS)\n",
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" # Remplissage de la variable x\n",
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" x[i] = np.asarray(img)\n",
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"\n",
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" y[i, 0] = 1 # Un objet est toujours présent !\n",
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"\n",
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" # Coordonnées de boîte englobante\n",
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" img_shape = item['shape']\n",
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" box = item['box']\n",
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" bx = (box[0] + (box[2] - box[0])/2)/img_shape[0]\n",
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" by = (box[1] + (box[3] - box[1])/2)/img_shape[1]\n",
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" bw = (box[2] - box[0])/img_shape[0]\n",
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" bh = (box[3] - box[1])/img_shape[1]\n",
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" y[i, 1] = bx\n",
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" y[i, 2] = by\n",
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" y[i, 3] = bw\n",
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" y[i, 4] = bh\n",
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"\n",
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" # Probabilités de classe, sous la forme d'une one-hot vector\n",
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" label = class_labels.index(item['specie'])\n",
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" classes_probabilities = keras.utils.to_categorical(label, num_classes=num_classes)\n",
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" y[i, 5:] = classes_probabilities\n",
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"\n",
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" i = i+1\n",
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"\n",
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" return x, y\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "fnlljZWc_i1L"
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},
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"source": [
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"Séparation des données d'entraînement pour extraire un ensemble de validation, et pré-traitement des données."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"metadata": {
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"id": "FJLRiuFX_VPL"
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/tmp/ipykernel_3171614/3333676307.py:13: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.\n",
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" img = img.resize((image_size,image_size), Image.ANTIALIAS)\n"
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]
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}
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],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"\n",
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"# Pour la suite du TP on considèrera des images de taille 64x64x3\n",
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"# Augmenter cette valeur donnerait de meilleurs résultats mais nécessiterait des calculs plus long.\n",
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"IMAGE_SIZE = 64\n",
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"\n",
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"# Lecture des données d'entraînement et de test\n",
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"x, y = build_localization_tensors(IMAGE_SIZE, dataset_train, num_classes)\n",
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"x_test, y_test = build_localization_tensors(IMAGE_SIZE, dataset_test, num_classes)\n",
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"\n",
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"#Extraction d'un ensemble de validation\n",
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"x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.10, random_state=42)\n",
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"\n",
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"# Pour améliorer l'entraînement, on peut centrer-réduire les coordonnées des bounding boxes...\n",
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"y_std = np.std(y_train, axis=0)\n",
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"y_mean = np.mean(y_train, axis=0)\n",
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"y_train[...,1:5] = (y_train[...,1:5] - y_mean[1:5])/y_std[1:5]\n",
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"y_val[...,1:5] = (y_val[...,1:5] - y_mean[1:5])/y_std[1:5]\n",
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"y_test[...,1:5] = (y_test[...,1:5] - y_mean[1:5])/y_std[1:5]\n",
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"\n",
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"# ... et normaliser les valeurs de couleur\n",
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"x_train = x_train/255\n",
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"x_val = x_val/255\n",
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"x_test = x_test/255"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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|||
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"id": "DE4wQYq3AKnA"
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},
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"source": [
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"## Fonctions utiles"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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|||
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"id": "n6NBpMtaM-C0"
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},
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"source": [
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"Une fonction de calcul de l'intersection sur union, qui nous sera utile pour les métriques d'évaluation de nos méthodes :\n",
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"\n",
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"$$ IoU (R_1, R_2) = \\frac{\\mathcal{A} (R_1 \\cap R_2)}{\\mathcal{A} (R_1 \\cup R_2)} = \\frac{\\mathcal{A} (R_1 \\cap R_2)}{\\mathcal{A} (R_1) + \\mathcal{A} (R_2) - \\mathcal{A} (R_1 \\cap R_2)} $$ \n",
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"\n",
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"<center>\n",
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"<img src=\"https://drive.google.com/uc?id=1BQx2kDOCltZ_5gcnKVWTVUVNmGk-7qBC\" width=500>\n",
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"</center>\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {
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"id": "rr-O1XuwLoL3"
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},
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"outputs": [],
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"source": [
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"### A COMPLETER\n",
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"def intersection_sur_union(box1, box2):\n",
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" \"\"\"\n",
|
|||
|
|
" Calcul de l'intersection sur union entre deux rectangles box1 et box2\n",
|
|||
|
|
"\n",
|
|||
|
|
" Arguments:\n",
|
|||
|
|
" box1, box2 -- les coordonnées des deux rectangles, chacun sous la forme [cx, cy, w, h]\n",
|
|||
|
|
" où (cx, cy) désigne les coordonnées du centre du rectangle, \n",
|
|||
|
|
" w sa largeur et h sa hauteur\n",
|
|||
|
|
"\n",
|
|||
|
|
" Retourne :\n",
|
|||
|
|
" iou -- la valeur d'intersection sur union entre les deux rectangles \n",
|
|||
|
|
" \"\"\"\n",
|
|||
|
|
"\n",
|
|||
|
|
" # unpacking\n",
|
|||
|
|
" cx1, cy1, w1, h1 = box1\n",
|
|||
|
|
" cx2, cy2, w2, h2 = box2\n",
|
|||
|
|
"\n",
|
|||
|
|
" # haut gauche R1\n",
|
|||
|
|
" x1 = cx1 - w1/2\n",
|
|||
|
|
" y1 = cy1 - h1/2\n",
|
|||
|
|
" \n",
|
|||
|
|
" # bas droite R1\n",
|
|||
|
|
" x2 = cx1 + w1/2\n",
|
|||
|
|
" y2 = cy1 + h1/2\n",
|
|||
|
|
"\n",
|
|||
|
|
" # haut gauche R2\n",
|
|||
|
|
" x3 = cx2 - w2/2\n",
|
|||
|
|
" y3 = cy2 - h2/2\n",
|
|||
|
|
" \n",
|
|||
|
|
" # bas droite R2\n",
|
|||
|
|
" x4 = cx2 + w2/2\n",
|
|||
|
|
" y4 = cy2 + 2/2\n",
|
|||
|
|
"\n",
|
|||
|
|
" # haut gauche intersection\n",
|
|||
|
|
" xi1 = max(x1, x3)\n",
|
|||
|
|
" yi1 = max(y1, y3)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # bas droite intersection\n",
|
|||
|
|
" xi2 = min(x2, x4)\n",
|
|||
|
|
" yi2 = min(y2, y4)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # dimension de l'intersection\n",
|
|||
|
|
" wi = abs(xi2 - xi1)\n",
|
|||
|
|
" hi = abs(yi2 - yi1)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # calcul des aires\n",
|
|||
|
|
" aR1iR2 = wi * hi\n",
|
|||
|
|
" aR1 = w1 * h1\n",
|
|||
|
|
" aR2 = w2 * h2\n",
|
|||
|
|
"\n",
|
|||
|
|
" return aR1iR2 / (aR1 + aR2 - aR1iR2)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 32,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "SdqAFWFxRx8l"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"0.2\n",
|
|||
|
|
"0.0\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"print(intersection_sur_union([2.5, 2, 1, 4], [2, 3, 4, 2])) # Résultat attendu : 0.2\n",
|
|||
|
|
"print(intersection_sur_union([2.5, 2, 1, 4], [5, 3, 4, 2])) # Résultat attendu : 0.0"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "RLpMJGi3QC9i"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"Calcul des différentes métriques : \n",
|
|||
|
|
"\n",
|
|||
|
|
"$$ P = \\frac{TP}{TP + FP} $$\n",
|
|||
|
|
"\n",
|
|||
|
|
"$$ R = \\frac{TP}{TP + FN} $$\n",
|
|||
|
|
"\n",
|
|||
|
|
"$$ F1 = \\frac{2}{\\frac{1}{P} + \\frac{1}{R}} $$\n",
|
|||
|
|
"\n",
|
|||
|
|
"où $TP$ désigne le nombre de vrais positifs, $FP$ le nombre de faux positifs, $FN$ le nombre de faux négatifs, $P$ la précision, $R$ le rappel et $F1$ le F1-score.\n",
|
|||
|
|
"\n",
|
|||
|
|
"On considère souvent qu'une détection est correcte si la classification est valide et que l'intersection sur union entre vérité terrain et prédiction est supérieure à 0.5 (on utilisera un seuil modifiable *iou_thres*)."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 60,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "5e_aIvjXLq32"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# A COMPLETER\n",
|
|||
|
|
"def global_accuracy(y_true, y_pred, iou_thres=0.5):\n",
|
|||
|
|
" \"\"\"\n",
|
|||
|
|
" Calcul, pour chaque classe de la précision, du rappel et du F1-score ainsi \n",
|
|||
|
|
" que du pourcentage global de bonnes détections.\n",
|
|||
|
|
"\n",
|
|||
|
|
" Arguments:\n",
|
|||
|
|
" y_true -- les labels de la vérité terrain, de dimension (M, 1+4+N) où M désigne\n",
|
|||
|
|
" le nombre d'éléments du dataset et N le nombre de classes (11 dans notre cas)\n",
|
|||
|
|
" y_pred -- les labels prédits par un modèle, de dimension (M, 1+4+N) \n",
|
|||
|
|
" iou_thres -- seuil d'intersection sur union entre une boîte \"vérité-terrain\" et \n",
|
|||
|
|
" une boite prédite au-dessus duquel on considère que la prédiction est correcte \n",
|
|||
|
|
"\n",
|
|||
|
|
" Retourne :\n",
|
|||
|
|
" class_res -- liste de longueur N contenant des dictionnaires sous la forme \n",
|
|||
|
|
" {\"Précision\": p, \"Rappel\": r, \"F-score\": f} résumant les métriques\n",
|
|||
|
|
" précision, rappel et F1-score pour chacune des classes.\n",
|
|||
|
|
" accuracy -- pourcentage global de bonnes détections\n",
|
|||
|
|
" \"\"\"\n",
|
|||
|
|
" # Initialisation des métriques : nombre de vrais positifs (TP), faux positifs (FP)\n",
|
|||
|
|
" # et faux négatifs (FN) pour chaque classe\n",
|
|||
|
|
" class_metrics = []\n",
|
|||
|
|
" for i in range(num_classes):\n",
|
|||
|
|
" class_metrics.append({'TP': 0, 'FP': 0, 'FN': 0})\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Nombres de détections correctes et de détections incorrectes\n",
|
|||
|
|
" total_correct_detections = 0\n",
|
|||
|
|
" total_incorrect_detections = 0\n",
|
|||
|
|
" for i in range(y_true.shape[0]):\n",
|
|||
|
|
" # Labels vérité-terrain et prédits\n",
|
|||
|
|
" groundtruth_label = np.argmax(y_true[i,5:])\n",
|
|||
|
|
" predicted_label = np.argmax(y_pred[i,5:])\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Coordonnées de boîtes englobantes réelles et prédites\n",
|
|||
|
|
" bx_true = (y_true[i,1]*y_std[1] + y_mean[1])\n",
|
|||
|
|
" by_true = (y_true[i,2]*y_std[2] + y_mean[2])\n",
|
|||
|
|
" bw_true = (y_true[i,3]*y_std[3] + y_mean[3])\n",
|
|||
|
|
" bh_true = (y_true[i,4]*y_std[4] + y_mean[4]) \n",
|
|||
|
|
" bx_pred = (y_pred[i,1]*y_std[1] + y_mean[1])\n",
|
|||
|
|
" by_pred = (y_pred[i,2]*y_std[2] + y_mean[2])\n",
|
|||
|
|
" bw_pred = (y_pred[i,3]*y_std[3] + y_mean[3])\n",
|
|||
|
|
" bh_pred = (y_pred[i,4]*y_std[4] + y_mean[4]) \n",
|
|||
|
|
"\n",
|
|||
|
|
" # Calcul de l'intersection sur union\n",
|
|||
|
|
" iou = intersection_sur_union([bx_true, by_true, bw_true, bh_true], [bx_pred, by_pred, bw_pred, bh_pred])\n",
|
|||
|
|
" \n",
|
|||
|
|
" # Si la détection est correcte : \n",
|
|||
|
|
" if groundtruth_label == predicted_label and iou > iou_thres:\n",
|
|||
|
|
" total_correct_detections += 1\n",
|
|||
|
|
" class_metrics[predicted_label][\"TP\"] += 1\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" total_incorrect_detections += 1\n",
|
|||
|
|
" class_metrics[predicted_label][\"FP\"] += 1\n",
|
|||
|
|
" class_metrics[groundtruth_label][\"FN\"] += 1\n",
|
|||
|
|
"\n",
|
|||
|
|
"# for i in range(num_classes):\n",
|
|||
|
|
"# print(class_metrics[i])\n",
|
|||
|
|
"\n",
|
|||
|
|
" class_res = []\n",
|
|||
|
|
" for i in range(num_classes):\n",
|
|||
|
|
" TP = class_metrics[i][\"TP\"]\n",
|
|||
|
|
" FP = class_metrics[i][\"FP\"]\n",
|
|||
|
|
" FN = class_metrics[i][\"FN\"]\n",
|
|||
|
|
" if (TP or FP) and (TP or FN): \n",
|
|||
|
|
" P = TP / (TP + FP)\n",
|
|||
|
|
" R = TP / (TP + FN)\n",
|
|||
|
|
" F_score = 2 / ( 1/P + 1/R )\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" P = 0\n",
|
|||
|
|
" R = 0\n",
|
|||
|
|
" F_score = 0\n",
|
|||
|
|
" class_res.append({'Precision': P, 'Rappel': R, 'F-score': F_score})\n",
|
|||
|
|
"\n",
|
|||
|
|
" accuracy = total_correct_detections / (total_correct_detections + total_incorrect_detections)\n",
|
|||
|
|
" return class_res, accuracy"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 61,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "AOOWhuk4VBZE"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"La précision globale est de 66.7%\n",
|
|||
|
|
"\n",
|
|||
|
|
"--------------------------------------------\n",
|
|||
|
|
"| Classe | Précision | Rappel | F1-score |\n",
|
|||
|
|
"--------------------------------------------\n",
|
|||
|
|
"| Classe 1 | 1.00 | 1.00 | 1.00 |\n",
|
|||
|
|
"--------------------------------------------\n",
|
|||
|
|
"| Classe 2 | 0.00 | 0.00 | 0.00 |\n",
|
|||
|
|
"--------------------------------------------\n",
|
|||
|
|
"| Classe 3 | 0.50 | 1.00 | 0.67 |\n",
|
|||
|
|
"--------------------------------------------\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"num_class_test = 3\n",
|
|||
|
|
"class_labels_test = ['Classe 1', 'Classe 2', 'Classe 3']\n",
|
|||
|
|
"y_true_test = np.ones((num_class_test,8))\n",
|
|||
|
|
"y_true_test[0,:2] = [0.5, 0.5]\n",
|
|||
|
|
"y_true_test[0, 5:] = [1, 0, 0]\n",
|
|||
|
|
"y_true_test[1,:2] = [0.5, 0.5]\n",
|
|||
|
|
"y_true_test[1, 5:] = [0, 1, 0]\n",
|
|||
|
|
"y_true_test[2,:2] = [0.5, 0.5]\n",
|
|||
|
|
"y_true_test[2, 5:] = [0, 0, 1]\n",
|
|||
|
|
"y_pred_test = np.ones((num_class_test,8))\n",
|
|||
|
|
"y_pred_test[0,:2] = [0.6, 0.6]\n",
|
|||
|
|
"y_pred_test[0, 5:] = [1, 0, 0]\n",
|
|||
|
|
"y_pred_test[1,:2] = [2.5, 2.5]\n",
|
|||
|
|
"y_pred_test[1, 5:] = [0, 0, 1]\n",
|
|||
|
|
"y_pred_test[2,:2] = [0.6, 0.6]\n",
|
|||
|
|
"y_pred_test[2, 5:] = [0, 0, 1]\n",
|
|||
|
|
"\n",
|
|||
|
|
"class_res_test, acc_test = global_accuracy(y_true_test, y_pred_test)\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(f\"La précision globale est de {100 * acc_test:.1f}%\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"print()\n",
|
|||
|
|
"print(\"--------------------------------------------\")\n",
|
|||
|
|
"print(\"| Classe | Précision | Rappel | F1-score |\")\n",
|
|||
|
|
"print(\"--------------------------------------------\")\n",
|
|||
|
|
"for i in range(num_class_test):\n",
|
|||
|
|
" print(f\"| {class_labels_test[i]:9s}| {class_res_test[i]['Precision']:.2f} | {class_res_test[i]['Rappel']:.2f} | {class_res_test[i]['F-score']:.2f} |\")\n",
|
|||
|
|
" print(\"--------------------------------------------\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "Q1Tg4VFVVB-w"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"**Affichage attendu :**\n",
|
|||
|
|
"```\n",
|
|||
|
|
"La précision globale est de 66.7%\n",
|
|||
|
|
"\n",
|
|||
|
|
"--------------------------------------------\n",
|
|||
|
|
"| Classe | Précision | Rappel | F1-score |\n",
|
|||
|
|
"--------------------------------------------\n",
|
|||
|
|
"| Classe 1 | 1.00 | 1.00 | 1.00 |\n",
|
|||
|
|
"--------------------------------------------\n",
|
|||
|
|
"| Classe 2 | 0.00 | 0.00 | 0.00 |\n",
|
|||
|
|
"--------------------------------------------\n",
|
|||
|
|
"| Classe 3 | 0.50 | 1.00 | 0.67 |\n",
|
|||
|
|
"--------------------------------------------\n",
|
|||
|
|
"```"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "cMntCEgkANMg"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"La fonction ci-dessous permet de calculer l'intersection sur union sur des tenseurs (et non des tableaux numpy), elle sera donc utilisable comme métrique pendant l'entraînement."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 62,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "tVk9cB1WAMUK"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def compute_iou(y_true, y_pred):\n",
|
|||
|
|
" ### \"Dénormalisation\" des coordonnées des boîtes englobantes\n",
|
|||
|
|
" pred_box_xy = y_pred[..., 0:2]* y_std[0:2] + y_mean[0:2]\n",
|
|||
|
|
" true_box_xy = y_true[..., 0:2]* y_std[0:2] + y_mean[0:2]\n",
|
|||
|
|
"\n",
|
|||
|
|
" ### \"Dénormalisation\" des largeur et hauteur des boîtes englobantes\n",
|
|||
|
|
" pred_box_wh = y_pred[..., 2:4] * y_std[2:4] + y_mean[2:4]\n",
|
|||
|
|
" true_box_wh = y_true[..., 2:4] * y_std[2:4] + y_mean[2:4]\n",
|
|||
|
|
" \n",
|
|||
|
|
" # Calcul des coordonnées minimales et maximales des boiptes englobantes réelles\n",
|
|||
|
|
" true_wh_half = true_box_wh / 2.\n",
|
|||
|
|
" true_mins = true_box_xy - true_wh_half\n",
|
|||
|
|
" true_maxes = true_box_xy + true_wh_half\n",
|
|||
|
|
" \n",
|
|||
|
|
" # Calcul des coordonnées minimales et maximales des boiptes englobantes prédites\n",
|
|||
|
|
" pred_wh_half = pred_box_wh / 2.\n",
|
|||
|
|
" pred_mins = pred_box_xy - pred_wh_half\n",
|
|||
|
|
" pred_maxes = pred_box_xy + pred_wh_half \n",
|
|||
|
|
" \n",
|
|||
|
|
" # Détermination de l'intersection des boîtes englobantes\n",
|
|||
|
|
" intersect_mins = tf.maximum(pred_mins, true_mins)\n",
|
|||
|
|
" intersect_maxes = tf.minimum(pred_maxes, true_maxes)\n",
|
|||
|
|
" intersect_wh = tf.maximum(intersect_maxes - intersect_mins, 0.)\n",
|
|||
|
|
" intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]\n",
|
|||
|
|
" \n",
|
|||
|
|
" # Aire des boîtes englobantes prédites et réelles\n",
|
|||
|
|
" true_areas = true_box_wh[..., 0] * true_box_wh[..., 1]\n",
|
|||
|
|
" pred_areas = pred_box_wh[..., 0] * pred_box_wh[..., 1]\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Aire de l'union des boîtes prédites et réelles\n",
|
|||
|
|
" union_areas = pred_areas + true_areas - intersect_areas\n",
|
|||
|
|
"\n",
|
|||
|
|
" iou_scores = tf.truediv(intersect_areas, union_areas)\n",
|
|||
|
|
" return iou_scores\n",
|
|||
|
|
"\n",
|
|||
|
|
"def iou():\n",
|
|||
|
|
" def iou_metrics(y_true, y_pred):\n",
|
|||
|
|
" return compute_iou(y_true, y_pred)\n",
|
|||
|
|
" iou_metrics.__name__= \"IoU\"\n",
|
|||
|
|
" return iou_metrics"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "vidE3XHlAkst"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"Visualisation des données et labels"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 63,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "8yotZHKgAiV1"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 1 Axes>"
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]
|
|||
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},
|
|||
|
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"metadata": {
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},
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|||
|
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"output_type": "display_data"
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},
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{
|
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"data": {
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"text/plain": [
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"text/plain": [
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|||
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
|
|||
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|
"<Figure size 432x288 with 1 Axes>"
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]
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},
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|||
|
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"metadata": {
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|
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"needs_background": "light"
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},
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|||
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAEICAYAAACZA4KlAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABW90lEQVR4nO29eZQlx1Un/Lv59vdqebVXd1dXL+rWLqsly5LlHcs2FgbsAX8egwHBiNEcBmY8HxywDfPNMMzwjVmOwRwGjMEMYhm8gcd8GmSQZRlvsuSWraWlVrd632t/VW9/LzPj+yOz3r03uqq7Wq2uEn7xO6dPR72IjIyMzMi8N+69v0vGGDg4OHz3w9voATg4OKwP3GJ3cOgSuMXu4NAlcIvdwaFL4Ba7g0OXwC12B4cuwT+rxU5Ev0hEf0FEq46biCaJqEJEiTX0N0xETxLRbS/tSC8fRPQgEd2z0eNw+C6CMWZD/wH4AoBfW+H3dwI4ByAZ/303gE8CSFxi/18G8NMr/J4C8H8AvOYCxz4LoBL/CwA0xN+/vNFzdxlzbgDs2uhxrGGc/w7AUQBLAPYCeN1l9PWrANrxvSsB+AaAO602RQB/GD93NQDPAPipi81d3Pdfir97AXwEwDEAVQAnAHwWwB2iDQH4RQAvAKjHbf47gIxo82cAWvGY5wE8BODaFzsHL4cv+/0AfoyIyPr9xwH8lTHGBwBjzIPGmPcaY4LVOiKi5FpPaoxpG2PeYYz5xgXa3GCM6THG9AD4KoCfW/7bGPP/ruU8K43pUsbZrSCiOwB8GMC7AfQD+ASAz61FYrsAPhXfy2EAjwD4jDhfGsAXAWwDcGd8zl8E8GEi+vlLGHcGwJcA3ATg+wH0AbgO0YfqbtH09wDcB+AnEL0c7gZwF4BPW13+ZjzmLQBOI5qHF4eXwds7B2ARwBvEbwOIvqI3I1I1PgjgMIC5eDIG43bbEb1p70X0ZvyK+C0J4Nehv8i/Hx93LaK35DyAAwDes4ZxfhlCQgDwrwDsB7AA4B8AbLPe/j+L6K19FMCbAJwC8AFEX42/iK/xAQAzcR8PAJhY6XwAfhLA1wD8dtz2KIC7L2POO18nRF+lzwD4SwBlRF+zqwF8CMA0gJMA3iaO/an4ussAjgD4N1bfvwTgLIAzAH7aOlcmvoYTAKYAfAxAbpUx/ksAj4u/C3Ffm17kNf8q9Nf3+ri/kfjve+PrLawwjgqAPnvuVuo7vuazdj9W+93xc3m79ftWAE0Abzb8Zf9vov77AFRf7H3f8C+7MaaOaAH/hPj5PQCeN8Y8hUiUexeANwLYjOhh/x9WN29E9Pb8XqvvX4H+Iv8cERUQLfT/BWAUwHsB/AERXb/WMRPROwH8MoAfAjASn+OvrWbvAnAHoocKAMYBDCL6ctyH6CX2P+O/JxGJcr9/gdPegejFNAzgNwF8YgVpaHl8f0BEf7DW6wHwA+AX0HcQvbw8RF+TXwPwR6LtNPiL9VMAfoeIbo3P+3YAPw/gLQB2IXrJSXwY0YtkT1y/BcB/WmVMDwJIENEd8df8XwF4EtHL8rIQf8V/AtHHYyH++a0AHjTGVK3mfwMgi+hrvxa8BcA/rNCPxF0AThljHpc/GmNOAvhmPBZ7zAUAPwLg0BrHcT6uxNf6Rbx1X4dIj8rGf38dwP8dl/cDuEu03YRI90qCv+I7Rf3yb8u6/pehv8j/EsBXrfP/EYD/fJExdvpB9CDeK+o8RDreNvH2f7OofxMi3St7gf73AFhY5Xw/CeCQqMvH5xh/kfNtf9kfEnU/gOhLloj/7o3bF1fp638DeH9c/lMA/13U7Vo+FyIdtQrgKlF/J4Cjq/RLiF6obQA+gFkAr7qMZ+xX43tQQvRVnQPwJlH/RQAfXuXYcwDeZ8+d1fdfrtRPfF9LiPYdDsS//UcA31zlXJ8E8Mdx+c8QSaUlACEiie4VL3YONvzLDgDGmK8hupnvIqKrANyO6MsLRF++zxFRiYhKiBZ/AGBMdHHyEk63DcAdy/3Ffb4P0Zf3Uvr4qDh+HtHDueUCY5oxxjSW/yCiPBH9EREdJ6IlRCpI8QI6aeeLZoypxcWeSxjzhTAlynUAs4b3RuryXER0NxF9k4jm42v/PkTSBhBJXvK6ZXkE0UvqCTFvX4h/Xwn3IpIcbgCQBvBjAB4gos12QyJ6fWyBqRDRsxe4zk8bY4qInp19AF4p6mYRfUjsvpPx9c3GPwWINnclUoheSkD0Eun0Y4x5Mj7nDyFSY1Y9V4xN4lwA8Nvx8dsR3YtrVjnuonhZLPYYf45ItPoxRGLQ8gN4EpF+WhT/ssaY0+LYC4Xu2XUnAfyT1V+PMeZnLmGsJxHpqrKPnNGbffZ57b9/AdGNu8MY0wfgDfHvK4rmLwfEm09/g0jvHosfwr8Hj/ksgAlxyFZRnkX0sN4g5qzfRJtPK2EPgAeMMQeNMaEx5gtx/6+xGxpjvmp44/SGi12HMWYWkSr1q0S0vOi+CODuWFyW+GFEevQ3479PIFp4EjsAHI/LDwN42wr9SHwJwFYiul3+SERbAbw67sMe8wkA70f0kcldoO9V8XJb7G8B8K8R7dAv42MAfp2ItgEAEY3EOvNaMQVgp/j7AQBXE9GPE1Eq/vcqIrruEvr8GIAPEdEN8Zj6iej/uoTjgUg8rgMoEdEggP98icdvBNKIvk4zAHwiuhvA20T9pwH8FBFdR0R5AP/PcoUxJgTwx4h0/FEAIKItRKT2WQS+BeAdRLSTIrwVkb6/76W4EGPMAUR7E78U//QXiDZRP0NE2+Pn4nsR7Zr/qjFmMW73KQD/kYgmiMgjorcgUn0+G9f/OaKX0ueI6EYiShBRFkDHl8MYcxDRM/RXRPTquM0NiF6kXzTGfHGVMT+EaOPzvhdzzS+bxW6MOYbI9lkA8Hei6qPx3/9IRGVEb9g7LqHrjwJ4NxEtENHvGWPKiB7Q9yKauHMAfgMsYq1lrJ+Lj/lkLILvgzarrAW/i8gSMYvomr5wicevCiL6GBF97KXqbxnx3P17RIt6AcCPQtwrY8yDiBbHI4g2kpa/hs34/w8s/x7P2xexulj654j01y8j0nd/D5E09fxLd0X4LQD3EdGoMaaJ6GNzEsBj8Tk/AuBXjDG/JY75NUTP6dcQzcFvItLn9wFArKp9D4DnEPlxLCHaWH0Voo3nZfwcgD9BZAWpILr/X0YkSVxszL8US1mXBIo3AhwcXnLE0tI+RI4i/kaPp9vxsvmyO3x3gIj+BRFliGgAkfTz/7mF/vKAW+wOLzX+DSJb/GFEO9eXsvHpcAXhxHgHhy7BZX3ZiejtRHSAiA4R0QdfqkE5ODi89HjRX/bY+eMgIte+U4hMJT9ijHlutWMymYzJ5/Px8ed12CmatK7q6e/rlKt+q1P2RRkAPPCBgR/qOuL4mWSG2430Dqp2SY/ff2Go+6gF9U45EFXNsKHaZZO8UWpa1oX6PA4KtCqbSHJ8jLwvXkK/k4MWH1crV3Qfoq0+s/7LkzfAGqIn5oA82U43lGM8zzlgZU/eFSCePztyWfRBCfF82GeT7S4wRoRyTnUsUijuxXkOEqLLyqK+14ERD4JwhwqbOl5L3kOTsO6MOI7afHZzXiS3qAv0s4kw6rNZr6Ldaqw4+ZcTfXU7IhfOIwBARJ9EFJa66mLP5/P4nrveDABI2MPJ8hWbCe1Educ72JT7+OyxTnlh/phql8cOrpvRiyCXKHfKg1dt65R/5k3vVe1Genmh1mo1VffEwv5OuSJu5qHyQdXuulE26zePamcrb47H4ZXnVN3A8ECn7IsFnevT/hmLp6c75Scf/qqq6xMvRi8hHu5Q3+pskseVSOmbUejhOUjlslyR0n20WvyyTXj6nsmHO5QvLk+fSy6WZNp6y6d5jFTgcZiknlOT5OM868EyLb5PYYPLPX1F1W5pcZ7bJfRyb4vV+NUHtYNeKWQX+EQv/149Vlbtsr3sB2N6rWeij+cqPcX33U/q+QgTPH5/Qbvee82ozye/troF93LE+C3Q7pCnoN1FAQBEdB8R7SWivc1m0652cHBYJ1zxuGpjzMcBfBwABgYGj
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# Si seuls x et y sont indiqués, on tire au hasard un numéro d'image et on affiche le label y associé à l'image\n",
|
|||
|
|
"# Si un 2e y, nommé y_pred, est indiqué, alors les deux labels sont affichés côte à côte, afin de pouvoir les comparer\n",
|
|||
|
|
"# Enfin on peut également indiquer l'id de l'image que l'on souhaite visualiser.\n",
|
|||
|
|
"def print_data_localisation(x, y, y_pred=[], id=None, image_size=64):\n",
|
|||
|
|
" if id==None:\n",
|
|||
|
|
" # Tirage aléatoire d'une image dans la base\n",
|
|||
|
|
" num_img = np.random.randint(x.shape[0]-1)\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" num_img = id\n",
|
|||
|
|
"\n",
|
|||
|
|
" img = x[num_img]\n",
|
|||
|
|
" lab = y[num_img]\n",
|
|||
|
|
"\n",
|
|||
|
|
" colors = [\"blue\", \"yellow\", \"red\", \"orange\", \"coral\", \"gold\", \"ivory\", \"fuchsia\", \"purple\", \"cyan\", \"navy\"] # Différentes couleurs pour les différentes classes\n",
|
|||
|
|
" classes = ['MESCHA', 'VEREUR', 'ECUROU', 'PIEBAV', 'SITTOR', 'PINARB', 'MESNOI', 'MESNON', 'MESBLE', 'ROUGOR', 'ACCMOU']\n",
|
|||
|
|
"\n",
|
|||
|
|
" if np.any(y_pred):\n",
|
|||
|
|
" plt.subplot(1, 2, 1)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Affichage de l'image\n",
|
|||
|
|
" plt.imshow(img)\n",
|
|||
|
|
" # Détermination de la classe\n",
|
|||
|
|
" class_id = np.argmax(lab[5:])\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Détermination des coordonnées de la boîte englobante dans le repère image\n",
|
|||
|
|
" ax = (lab[1]*y_std[1] + y_mean[1]) * image_size\n",
|
|||
|
|
" ay = (lab[2]*y_std[2] + y_mean[2]) * image_size\n",
|
|||
|
|
" width = (lab[3]*y_std[3] + y_mean[3]) * image_size\n",
|
|||
|
|
" height = (lab[4]*y_std[4] + y_mean[4]) * image_size\n",
|
|||
|
|
" #print(\"x: {}, y: {}, w: {}, h:{}\".format(ax,ay,width, height))\n",
|
|||
|
|
" # Détermination des extrema de la boîte englobante\n",
|
|||
|
|
" p_x = [ax-width/2, ax+width/2]\n",
|
|||
|
|
" p_y = [ay-height/2, ay+height/2]\n",
|
|||
|
|
" # Affichage de la boîte englobante, dans la bonne couleur\n",
|
|||
|
|
" plt.plot([p_x[0], p_x[0]],p_y,color=colors[class_id])\n",
|
|||
|
|
" plt.plot([p_x[1], p_x[1]],p_y,color=colors[class_id])\n",
|
|||
|
|
" plt.plot(p_x,[p_y[0],p_y[0]],color=colors[class_id])\n",
|
|||
|
|
" plt.plot(p_x,[p_y[1],p_y[1]],color=colors[class_id])\n",
|
|||
|
|
" plt.title(\"Vérité Terrain : Image {} - {}\".format(num_img, classes[class_id]))\n",
|
|||
|
|
"\n",
|
|||
|
|
" if np.any(y_pred):\n",
|
|||
|
|
" plt.subplot(1, 2, 2)\n",
|
|||
|
|
" # Affichage de l'image\n",
|
|||
|
|
" plt.imshow(img)\n",
|
|||
|
|
" lab = y_pred[num_img]\n",
|
|||
|
|
" # Détermination de la classe\n",
|
|||
|
|
" class_id = np.argmax(lab[5:])\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Détermination des coordonnées de la boîte englobante dans le repère image\n",
|
|||
|
|
" ax = (lab[1]*y_std[1] + y_mean[1]) * image_size\n",
|
|||
|
|
" ay = (lab[2]*y_std[2] + y_mean[2]) * image_size\n",
|
|||
|
|
" width = (lab[3]*y_std[3] + y_mean[3]) * image_size\n",
|
|||
|
|
" height = (lab[4]*y_std[4] + y_mean[4]) * image_size\n",
|
|||
|
|
" #print(\"x: {}, y: {}, w: {}, h:{}\".format(ax,ay,width, height))\n",
|
|||
|
|
" # Détermination des extrema de la boîte englobante\n",
|
|||
|
|
" p_x = [ax-width/2, ax+width/2]\n",
|
|||
|
|
" p_y = [ay-height/2, ay+height/2]\n",
|
|||
|
|
" # Affichage de la boîte englobante, dans la bonne couleur\n",
|
|||
|
|
" plt.plot([p_x[0], p_x[0]],p_y,color=colors[class_id])\n",
|
|||
|
|
" plt.plot([p_x[1], p_x[1]],p_y,color=colors[class_id])\n",
|
|||
|
|
" plt.plot(p_x,[p_y[0],p_y[0]],color=colors[class_id])\n",
|
|||
|
|
" plt.plot(p_x,[p_y[1],p_y[1]],color=colors[class_id])\n",
|
|||
|
|
" plt.title(\"Prédiction : Image {} - {}\".format(num_img, classes[class_id]))\n",
|
|||
|
|
"\n",
|
|||
|
|
" plt.show()\n",
|
|||
|
|
"\n",
|
|||
|
|
"for i in range(10):#x.shape[0]):\n",
|
|||
|
|
" print_data_localisation(x_train, y_train, image_size=IMAGE_SIZE, id=i)\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "mE9_B3yuR4OH"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"Fonction d'affichage des courbes d'apprentissage et de validation"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 64,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "-6O7R3qnCtlN"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def plot_training_analysis(history, metric='loss'): \n",
|
|||
|
|
"\n",
|
|||
|
|
" loss = history.history[metric]\n",
|
|||
|
|
" val_loss = history.history['val_' + metric]\n",
|
|||
|
|
"\n",
|
|||
|
|
" epochs = range(len(loss))\n",
|
|||
|
|
"\n",
|
|||
|
|
" plt.plot(epochs, loss, 'b', linestyle=\"--\",label='Training ' + metric)\n",
|
|||
|
|
" plt.plot(epochs, val_loss, 'g', label='Validation ' + metric)\n",
|
|||
|
|
" plt.title('Training and validation ' + metric)\n",
|
|||
|
|
" plt.legend()\n",
|
|||
|
|
"\n",
|
|||
|
|
" plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "8F_l_yNlDapa"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"## Travail à faire\n",
|
|||
|
|
"\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "E6D31NGPNyJp"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"<center> <img src=\"https://drive.google.com/uc?id=1YzCZe4pgnjJDVGklAaCHZ7HhlPJsadg9\" width=500></center>\n",
|
|||
|
|
"<caption><center> Figure 3: Illustration de l'architecture du réseau à construire. </center></caption>\n",
|
|||
|
|
"\n",
|
|||
|
|
"Complétez les codes qui vous sont fournis pour obtenir un algorithme de localisation. \n",
|
|||
|
|
"Vous pouvez utiliser n'importe quelle base convolutive de votre choix (**commencez par exemple par réutiliser celle que vous avez implémenté au TP3**), en revanche vous devrez porter une attention particulière à la couche de sortie.\n",
|
|||
|
|
"\n",
|
|||
|
|
"Vous allez en fait produire 3 sorties différentes : une caractérisant la présence d'un objet, une autre fournissant les coordonnées de la boîte englobante, et enfin une dernière effectuant la classification."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 82,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "vPOXiJ7hDcbr"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"def create_model_localisation(input_shape=(64, 64, 3)):\n",
|
|||
|
|
"\n",
|
|||
|
|
" input_layer = Input(shape=input_shape)\n",
|
|||
|
|
"\n",
|
|||
|
|
" conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(input_layer)\n",
|
|||
|
|
" conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)\n",
|
|||
|
|
" pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n",
|
|||
|
|
"\n",
|
|||
|
|
" conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)\n",
|
|||
|
|
" conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)\n",
|
|||
|
|
" pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n",
|
|||
|
|
"\n",
|
|||
|
|
" flat = Flatten()(pool2)\n",
|
|||
|
|
" dense = Dense(128, activation = 'relu')(flat)\n",
|
|||
|
|
"\n",
|
|||
|
|
" prev_layer = dense\n",
|
|||
|
|
"\n",
|
|||
|
|
" output_p = Dense(1, activation='sigmoid', name='p')(prev_layer) # Sortie caractérisant la présence d'un objet\n",
|
|||
|
|
" output_coord = Dense(4, activation='linear', name='coord')(prev_layer) # Sortie caractérisant les coordonnées de boîte englobante\n",
|
|||
|
|
" output_class = Dense(len(class_labels), activation='softmax', name='classes')(prev_layer) # Sortie caractérisant les probabilités de classe\n",
|
|||
|
|
" \n",
|
|||
|
|
" output= [output_p, output_coord, output_class]\n",
|
|||
|
|
" model = Model(input_layer, output)\n",
|
|||
|
|
"\n",
|
|||
|
|
" return model"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "MYoVYfQpotjy"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"<center> <img src=\"https://drive.google.com/uc?id=1bnh8zU7Os-w-5TT8hV4xDoThKQc-Ywc2\" width=500></center>\n",
|
|||
|
|
"<caption><center> Figure 4: Illustration des fonctions de coût à utiliser pour l'entraînement. </center></caption>\n",
|
|||
|
|
"\n",
|
|||
|
|
"Pour entraîner votre réseau, vous allez donc devoir associer une fonction de coût à chacune des sorties du réseau. La fonction de coût totale sera la somme des trois fonctions de coût précédemment définies, pondérées par des poids définis dans la variable *loss_weights*.\n",
|
|||
|
|
"\n",
|
|||
|
|
"**Prenez le temps de tester différentes valeurs de *loss_weights* en fonction de l'évolution des métriques que vous observerez pendant l'entraînement.**\n",
|
|||
|
|
"\n",
|
|||
|
|
"Vous évaluerez vos résultats de manière qualitative (en affichant les boîtes englobantes prédites et réelles) mais aussi quantitatives grâce aux fonctions définies dans la section précédente. **N'hésitez pas à modifier un peu le paramètre iou_threshold positionné par défaut à 0.5** (une valeur de 0.4 vous permettra d'obtenir de meilleurs résultats !)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 83,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "s_ewlCn5Rovm"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Model: \"model_6\"\n",
|
|||
|
|
"__________________________________________________________________________________________________\n",
|
|||
|
|
" Layer (type) Output Shape Param # Connected to \n",
|
|||
|
|
"==================================================================================================\n",
|
|||
|
|
" input_7 (InputLayer) [(None, 64, 64, 3)] 0 [] \n",
|
|||
|
|
" \n",
|
|||
|
|
" conv2d_22 (Conv2D) (None, 64, 64, 64) 1792 ['input_7[0][0]'] \n",
|
|||
|
|
" \n",
|
|||
|
|
" conv2d_23 (Conv2D) (None, 64, 64, 64) 36928 ['conv2d_22[0][0]'] \n",
|
|||
|
|
" \n",
|
|||
|
|
" max_pooling2d_11 (MaxPooling2D (None, 32, 32, 64) 0 ['conv2d_23[0][0]'] \n",
|
|||
|
|
" ) \n",
|
|||
|
|
" \n",
|
|||
|
|
" conv2d_24 (Conv2D) (None, 32, 32, 128) 73856 ['max_pooling2d_11[0][0]'] \n",
|
|||
|
|
" \n",
|
|||
|
|
" conv2d_25 (Conv2D) (None, 32, 32, 128) 147584 ['conv2d_24[0][0]'] \n",
|
|||
|
|
" \n",
|
|||
|
|
" max_pooling2d_12 (MaxPooling2D (None, 16, 16, 128) 0 ['conv2d_25[0][0]'] \n",
|
|||
|
|
" ) \n",
|
|||
|
|
" \n",
|
|||
|
|
" flatten_3 (Flatten) (None, 32768) 0 ['max_pooling2d_12[0][0]'] \n",
|
|||
|
|
" \n",
|
|||
|
|
" dense_5 (Dense) (None, 128) 4194432 ['flatten_3[0][0]'] \n",
|
|||
|
|
" \n",
|
|||
|
|
" p (Dense) (None, 1) 129 ['dense_5[0][0]'] \n",
|
|||
|
|
" \n",
|
|||
|
|
" coord (Dense) (None, 4) 516 ['dense_5[0][0]'] \n",
|
|||
|
|
" \n",
|
|||
|
|
" classes (Dense) (None, 11) 1419 ['dense_5[0][0]'] \n",
|
|||
|
|
" \n",
|
|||
|
|
"==================================================================================================\n",
|
|||
|
|
"Total params: 4,456,656\n",
|
|||
|
|
"Trainable params: 4,456,656\n",
|
|||
|
|
"Non-trainable params: 0\n",
|
|||
|
|
"__________________________________________________________________________________________________\n",
|
|||
|
|
"Epoch 1/50\n",
|
|||
|
|
"118/118 [==============================] - 15s 104ms/step - loss: 1.5293 - p_loss: 0.0235 - coord_loss: 1.1148 - classes_loss: 0.3911 - p_accuracy: 1.0000 - coord_IoU: 0.4255 - classes_accuracy: 0.1787 - val_loss: 0.8456 - val_p_loss: 0.0010 - val_coord_loss: 0.5451 - val_classes_loss: 0.2995 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5131 - val_classes_accuracy: 0.2810\n",
|
|||
|
|
"Epoch 2/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.7562 - p_loss: 0.0018 - coord_loss: 0.4862 - classes_loss: 0.2681 - p_accuracy: 1.0000 - coord_IoU: 0.5129 - classes_accuracy: 0.4040 - val_loss: 0.7039 - val_p_loss: 8.6872e-04 - val_coord_loss: 0.4485 - val_classes_loss: 0.2545 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5107 - val_classes_accuracy: 0.4048\n",
|
|||
|
|
"Epoch 3/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.6387 - p_loss: 6.8169e-04 - coord_loss: 0.4034 - classes_loss: 0.2346 - p_accuracy: 1.0000 - coord_IoU: 0.5332 - classes_accuracy: 0.5032 - val_loss: 0.6252 - val_p_loss: 0.0010 - val_coord_loss: 0.3901 - val_classes_loss: 0.2341 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5327 - val_classes_accuracy: 0.4762\n",
|
|||
|
|
"Epoch 4/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 87ms/step - loss: 0.5672 - p_loss: 5.0588e-04 - coord_loss: 0.3520 - classes_loss: 0.2147 - p_accuracy: 1.0000 - coord_IoU: 0.5354 - classes_accuracy: 0.5292 - val_loss: 0.5895 - val_p_loss: 2.2709e-04 - val_coord_loss: 0.3826 - val_classes_loss: 0.2066 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5358 - val_classes_accuracy: 0.5000\n",
|
|||
|
|
"Epoch 5/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.4793 - p_loss: 2.0704e-04 - coord_loss: 0.2845 - classes_loss: 0.1946 - p_accuracy: 1.0000 - coord_IoU: 0.5772 - classes_accuracy: 0.5610 - val_loss: 0.5538 - val_p_loss: 2.4165e-04 - val_coord_loss: 0.3575 - val_classes_loss: 0.1961 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5256 - val_classes_accuracy: 0.5286\n",
|
|||
|
|
"Epoch 6/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.4431 - p_loss: 1.6858e-04 - coord_loss: 0.2604 - classes_loss: 0.1825 - p_accuracy: 1.0000 - coord_IoU: 0.6001 - classes_accuracy: 0.5732 - val_loss: 0.5183 - val_p_loss: 1.1366e-04 - val_coord_loss: 0.3382 - val_classes_loss: 0.1800 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5732 - val_classes_accuracy: 0.5333\n",
|
|||
|
|
"Epoch 7/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.3859 - p_loss: 1.0205e-04 - coord_loss: 0.2163 - classes_loss: 0.1695 - p_accuracy: 1.0000 - coord_IoU: 0.6207 - classes_accuracy: 0.5944 - val_loss: 0.5467 - val_p_loss: 1.3684e-04 - val_coord_loss: 0.3726 - val_classes_loss: 0.1740 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5229 - val_classes_accuracy: 0.5762\n",
|
|||
|
|
"Epoch 8/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.3621 - p_loss: 9.5203e-05 - coord_loss: 0.1990 - classes_loss: 0.1630 - p_accuracy: 1.0000 - coord_IoU: 0.6330 - classes_accuracy: 0.6108 - val_loss: 0.4850 - val_p_loss: 6.6984e-05 - val_coord_loss: 0.3162 - val_classes_loss: 0.1688 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5435 - val_classes_accuracy: 0.5476\n",
|
|||
|
|
"Epoch 9/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.3252 - p_loss: 8.0633e-05 - coord_loss: 0.1702 - classes_loss: 0.1549 - p_accuracy: 1.0000 - coord_IoU: 0.6402 - classes_accuracy: 0.6257 - val_loss: 0.4909 - val_p_loss: 5.5699e-05 - val_coord_loss: 0.3324 - val_classes_loss: 0.1584 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5603 - val_classes_accuracy: 0.5762\n",
|
|||
|
|
"Epoch 10/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.2911 - p_loss: 5.4550e-05 - coord_loss: 0.1445 - classes_loss: 0.1466 - p_accuracy: 1.0000 - coord_IoU: 0.6725 - classes_accuracy: 0.6495 - val_loss: 0.4471 - val_p_loss: 4.2831e-05 - val_coord_loss: 0.2914 - val_classes_loss: 0.1556 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5763 - val_classes_accuracy: 0.5714\n",
|
|||
|
|
"Epoch 11/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.2748 - p_loss: 4.0582e-05 - coord_loss: 0.1337 - classes_loss: 0.1411 - p_accuracy: 1.0000 - coord_IoU: 0.6778 - classes_accuracy: 0.6617 - val_loss: 0.5055 - val_p_loss: 2.8487e-05 - val_coord_loss: 0.3523 - val_classes_loss: 0.1532 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5665 - val_classes_accuracy: 0.5571\n",
|
|||
|
|
"Epoch 12/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.2577 - p_loss: 3.7545e-05 - coord_loss: 0.1205 - classes_loss: 0.1371 - p_accuracy: 1.0000 - coord_IoU: 0.6899 - classes_accuracy: 0.6691 - val_loss: 0.4531 - val_p_loss: 1.9267e-05 - val_coord_loss: 0.3026 - val_classes_loss: 0.1505 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5872 - val_classes_accuracy: 0.6048\n",
|
|||
|
|
"Epoch 13/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.2374 - p_loss: 3.0101e-05 - coord_loss: 0.1064 - classes_loss: 0.1309 - p_accuracy: 1.0000 - coord_IoU: 0.7021 - classes_accuracy: 0.6909 - val_loss: 0.4378 - val_p_loss: 2.6943e-05 - val_coord_loss: 0.2922 - val_classes_loss: 0.1455 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5917 - val_classes_accuracy: 0.6238\n",
|
|||
|
|
"Epoch 14/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.2291 - p_loss: 3.2337e-05 - coord_loss: 0.1018 - classes_loss: 0.1273 - p_accuracy: 1.0000 - coord_IoU: 0.7118 - classes_accuracy: 0.7052 - val_loss: 0.4363 - val_p_loss: 2.6610e-05 - val_coord_loss: 0.2911 - val_classes_loss: 0.1452 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5864 - val_classes_accuracy: 0.6238\n",
|
|||
|
|
"Epoch 15/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.2152 - p_loss: 2.7226e-05 - coord_loss: 0.0912 - classes_loss: 0.1239 - p_accuracy: 1.0000 - coord_IoU: 0.7252 - classes_accuracy: 0.7169 - val_loss: 0.4317 - val_p_loss: 3.0600e-05 - val_coord_loss: 0.2906 - val_classes_loss: 0.1411 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5881 - val_classes_accuracy: 0.6381\n",
|
|||
|
|
"Epoch 16/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.2030 - p_loss: 2.6618e-05 - coord_loss: 0.0832 - classes_loss: 0.1198 - p_accuracy: 1.0000 - coord_IoU: 0.7442 - classes_accuracy: 0.7397 - val_loss: 0.4165 - val_p_loss: 1.5708e-05 - val_coord_loss: 0.2784 - val_classes_loss: 0.1381 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5916 - val_classes_accuracy: 0.6238\n",
|
|||
|
|
"Epoch 17/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.2058 - p_loss: 3.1942e-05 - coord_loss: 0.0886 - classes_loss: 0.1172 - p_accuracy: 1.0000 - coord_IoU: 0.7303 - classes_accuracy: 0.7386 - val_loss: 0.4309 - val_p_loss: 6.8455e-05 - val_coord_loss: 0.2904 - val_classes_loss: 0.1405 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5980 - val_classes_accuracy: 0.6190\n",
|
|||
|
|
"Epoch 18/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.1907 - p_loss: 2.9955e-05 - coord_loss: 0.0776 - classes_loss: 0.1130 - p_accuracy: 1.0000 - coord_IoU: 0.7450 - classes_accuracy: 0.7609 - val_loss: 0.4112 - val_p_loss: 1.6949e-05 - val_coord_loss: 0.2780 - val_classes_loss: 0.1332 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5937 - val_classes_accuracy: 0.6571\n",
|
|||
|
|
"Epoch 19/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.1740 - p_loss: 2.3655e-05 - coord_loss: 0.0654 - classes_loss: 0.1086 - p_accuracy: 1.0000 - coord_IoU: 0.7620 - classes_accuracy: 0.7662 - val_loss: 0.4295 - val_p_loss: 7.7834e-06 - val_coord_loss: 0.2988 - val_classes_loss: 0.1308 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5796 - val_classes_accuracy: 0.6905\n",
|
|||
|
|
"Epoch 20/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.1670 - p_loss: 2.2241e-05 - coord_loss: 0.0623 - classes_loss: 0.1047 - p_accuracy: 1.0000 - coord_IoU: 0.7619 - classes_accuracy: 0.7821 - val_loss: 0.4024 - val_p_loss: 2.5984e-05 - val_coord_loss: 0.2725 - val_classes_loss: 0.1298 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6069 - val_classes_accuracy: 0.6810\n",
|
|||
|
|
"Epoch 21/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.1687 - p_loss: 2.1902e-05 - coord_loss: 0.0664 - classes_loss: 0.1023 - p_accuracy: 1.0000 - coord_IoU: 0.7641 - classes_accuracy: 0.7884 - val_loss: 0.4067 - val_p_loss: 9.9703e-06 - val_coord_loss: 0.2822 - val_classes_loss: 0.1245 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5956 - val_classes_accuracy: 0.7000\n",
|
|||
|
|
"Epoch 22/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.1523 - p_loss: 1.5818e-05 - coord_loss: 0.0550 - classes_loss: 0.0973 - p_accuracy: 1.0000 - coord_IoU: 0.7689 - classes_accuracy: 0.7990 - val_loss: 0.4198 - val_p_loss: 1.6219e-05 - val_coord_loss: 0.2917 - val_classes_loss: 0.1281 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5777 - val_classes_accuracy: 0.7095\n",
|
|||
|
|
"Epoch 23/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.1517 - p_loss: 1.6976e-05 - coord_loss: 0.0574 - classes_loss: 0.0944 - p_accuracy: 1.0000 - coord_IoU: 0.7685 - classes_accuracy: 0.8070 - val_loss: 0.3936 - val_p_loss: 1.5322e-05 - val_coord_loss: 0.2671 - val_classes_loss: 0.1265 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5896 - val_classes_accuracy: 0.7048\n",
|
|||
|
|
"Epoch 24/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.1395 - p_loss: 1.3186e-05 - coord_loss: 0.0486 - classes_loss: 0.0909 - p_accuracy: 1.0000 - coord_IoU: 0.7819 - classes_accuracy: 0.8261 - val_loss: 0.4031 - val_p_loss: 1.2730e-05 - val_coord_loss: 0.2800 - val_classes_loss: 0.1230 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6027 - val_classes_accuracy: 0.7524\n",
|
|||
|
|
"Epoch 25/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.1389 - p_loss: 1.2866e-05 - coord_loss: 0.0517 - classes_loss: 0.0872 - p_accuracy: 1.0000 - coord_IoU: 0.7672 - classes_accuracy: 0.8399 - val_loss: 0.4047 - val_p_loss: 2.1689e-05 - val_coord_loss: 0.2824 - val_classes_loss: 0.1223 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5614 - val_classes_accuracy: 0.7286\n",
|
|||
|
|
"Epoch 26/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.1326 - p_loss: 1.3932e-05 - coord_loss: 0.0485 - classes_loss: 0.0841 - p_accuracy: 1.0000 - coord_IoU: 0.7796 - classes_accuracy: 0.8478 - val_loss: 0.4003 - val_p_loss: 9.8319e-06 - val_coord_loss: 0.2825 - val_classes_loss: 0.1178 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6046 - val_classes_accuracy: 0.7524\n",
|
|||
|
|
"Epoch 27/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.1250 - p_loss: 1.0477e-05 - coord_loss: 0.0449 - classes_loss: 0.0801 - p_accuracy: 1.0000 - coord_IoU: 0.7908 - classes_accuracy: 0.8579 - val_loss: 0.3921 - val_p_loss: 6.6715e-06 - val_coord_loss: 0.2763 - val_classes_loss: 0.1158 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5834 - val_classes_accuracy: 0.7476\n",
|
|||
|
|
"Epoch 28/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.1239 - p_loss: 1.2673e-05 - coord_loss: 0.0470 - classes_loss: 0.0769 - p_accuracy: 1.0000 - coord_IoU: 0.7836 - classes_accuracy: 0.8680 - val_loss: 0.3843 - val_p_loss: 1.1446e-05 - val_coord_loss: 0.2687 - val_classes_loss: 0.1156 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5992 - val_classes_accuracy: 0.7524\n",
|
|||
|
|
"Epoch 29/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.1191 - p_loss: 1.0588e-05 - coord_loss: 0.0452 - classes_loss: 0.0739 - p_accuracy: 1.0000 - coord_IoU: 0.7808 - classes_accuracy: 0.8818 - val_loss: 0.3907 - val_p_loss: 1.1466e-05 - val_coord_loss: 0.2760 - val_classes_loss: 0.1148 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6082 - val_classes_accuracy: 0.7429\n",
|
|||
|
|
"Epoch 30/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.1193 - p_loss: 1.0438e-05 - coord_loss: 0.0486 - classes_loss: 0.0707 - p_accuracy: 1.0000 - coord_IoU: 0.7825 - classes_accuracy: 0.8881 - val_loss: 0.3991 - val_p_loss: 1.1293e-05 - val_coord_loss: 0.2860 - val_classes_loss: 0.1131 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5731 - val_classes_accuracy: 0.7762\n",
|
|||
|
|
"Epoch 31/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.1125 - p_loss: 8.7969e-06 - coord_loss: 0.0445 - classes_loss: 0.0680 - p_accuracy: 1.0000 - coord_IoU: 0.7844 - classes_accuracy: 0.8940 - val_loss: 0.3903 - val_p_loss: 5.9613e-06 - val_coord_loss: 0.2771 - val_classes_loss: 0.1132 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5902 - val_classes_accuracy: 0.7714\n",
|
|||
|
|
"Epoch 32/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.1079 - p_loss: 8.5401e-06 - coord_loss: 0.0434 - classes_loss: 0.0644 - p_accuracy: 1.0000 - coord_IoU: 0.7930 - classes_accuracy: 0.9062 - val_loss: 0.3907 - val_p_loss: 3.9751e-06 - val_coord_loss: 0.2794 - val_classes_loss: 0.1113 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6132 - val_classes_accuracy: 0.7810\n",
|
|||
|
|
"Epoch 33/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.1041 - p_loss: 6.3921e-06 - coord_loss: 0.0421 - classes_loss: 0.0619 - p_accuracy: 1.0000 - coord_IoU: 0.7967 - classes_accuracy: 0.9168 - val_loss: 0.3941 - val_p_loss: 9.8752e-06 - val_coord_loss: 0.2780 - val_classes_loss: 0.1160 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5973 - val_classes_accuracy: 0.7333\n",
|
|||
|
|
"Epoch 34/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.0981 - p_loss: 8.5369e-06 - coord_loss: 0.0395 - classes_loss: 0.0585 - p_accuracy: 1.0000 - coord_IoU: 0.8023 - classes_accuracy: 0.9226 - val_loss: 0.3971 - val_p_loss: 3.0896e-06 - val_coord_loss: 0.2855 - val_classes_loss: 0.1116 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5973 - val_classes_accuracy: 0.7524\n",
|
|||
|
|
"Epoch 35/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.0926 - p_loss: 5.3509e-06 - coord_loss: 0.0377 - classes_loss: 0.0549 - p_accuracy: 1.0000 - coord_IoU: 0.8007 - classes_accuracy: 0.9348 - val_loss: 0.3745 - val_p_loss: 6.3115e-06 - val_coord_loss: 0.2608 - val_classes_loss: 0.1137 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6280 - val_classes_accuracy: 0.7619\n",
|
|||
|
|
"Epoch 36/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.0863 - p_loss: 4.5172e-06 - coord_loss: 0.0340 - classes_loss: 0.0523 - p_accuracy: 1.0000 - coord_IoU: 0.8094 - classes_accuracy: 0.9380 - val_loss: 0.3905 - val_p_loss: 1.8461e-06 - val_coord_loss: 0.2769 - val_classes_loss: 0.1136 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5988 - val_classes_accuracy: 0.7476\n",
|
|||
|
|
"Epoch 37/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.0808 - p_loss: 3.7010e-06 - coord_loss: 0.0318 - classes_loss: 0.0489 - p_accuracy: 1.0000 - coord_IoU: 0.8073 - classes_accuracy: 0.9507 - val_loss: 0.3798 - val_p_loss: 4.9560e-06 - val_coord_loss: 0.2730 - val_classes_loss: 0.1068 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5770 - val_classes_accuracy: 0.8000\n",
|
|||
|
|
"Epoch 38/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.0803 - p_loss: 3.8230e-06 - coord_loss: 0.0338 - classes_loss: 0.0465 - p_accuracy: 1.0000 - coord_IoU: 0.8084 - classes_accuracy: 0.9560 - val_loss: 0.3729 - val_p_loss: 2.7241e-06 - val_coord_loss: 0.2624 - val_classes_loss: 0.1105 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6056 - val_classes_accuracy: 0.7857\n",
|
|||
|
|
"Epoch 39/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.0754 - p_loss: 3.1915e-06 - coord_loss: 0.0323 - classes_loss: 0.0431 - p_accuracy: 1.0000 - coord_IoU: 0.8181 - classes_accuracy: 0.9581 - val_loss: 0.3831 - val_p_loss: 4.8808e-06 - val_coord_loss: 0.2767 - val_classes_loss: 0.1064 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6058 - val_classes_accuracy: 0.7952\n",
|
|||
|
|
"Epoch 40/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.0741 - p_loss: 3.3675e-06 - coord_loss: 0.0332 - classes_loss: 0.0409 - p_accuracy: 1.0000 - coord_IoU: 0.8164 - classes_accuracy: 0.9661 - val_loss: 0.3762 - val_p_loss: 2.6131e-06 - val_coord_loss: 0.2668 - val_classes_loss: 0.1094 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6179 - val_classes_accuracy: 0.7952\n",
|
|||
|
|
"Epoch 41/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.0717 - p_loss: 2.9842e-06 - coord_loss: 0.0330 - classes_loss: 0.0388 - p_accuracy: 1.0000 - coord_IoU: 0.8165 - classes_accuracy: 0.9708 - val_loss: 0.3781 - val_p_loss: 2.0115e-06 - val_coord_loss: 0.2716 - val_classes_loss: 0.1064 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6109 - val_classes_accuracy: 0.8048\n",
|
|||
|
|
"Epoch 42/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.0667 - p_loss: 2.6782e-06 - coord_loss: 0.0311 - classes_loss: 0.0356 - p_accuracy: 1.0000 - coord_IoU: 0.8156 - classes_accuracy: 0.9719 - val_loss: 0.3922 - val_p_loss: 2.0544e-06 - val_coord_loss: 0.2870 - val_classes_loss: 0.1051 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5667 - val_classes_accuracy: 0.8000\n",
|
|||
|
|
"Epoch 43/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.0719 - p_loss: 2.8606e-06 - coord_loss: 0.0370 - classes_loss: 0.0349 - p_accuracy: 1.0000 - coord_IoU: 0.7998 - classes_accuracy: 0.9756 - val_loss: 0.3827 - val_p_loss: 3.2613e-06 - val_coord_loss: 0.2734 - val_classes_loss: 0.1093 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6156 - val_classes_accuracy: 0.8048\n",
|
|||
|
|
"Epoch 44/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.0692 - p_loss: 2.8178e-06 - coord_loss: 0.0368 - classes_loss: 0.0324 - p_accuracy: 1.0000 - coord_IoU: 0.8033 - classes_accuracy: 0.9814 - val_loss: 0.3922 - val_p_loss: 1.6154e-06 - val_coord_loss: 0.2802 - val_classes_loss: 0.1120 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5931 - val_classes_accuracy: 0.7952\n",
|
|||
|
|
"Epoch 45/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.0639 - p_loss: 2.7500e-06 - coord_loss: 0.0331 - classes_loss: 0.0308 - p_accuracy: 1.0000 - coord_IoU: 0.8090 - classes_accuracy: 0.9783 - val_loss: 0.3965 - val_p_loss: 1.2561e-06 - val_coord_loss: 0.2813 - val_classes_loss: 0.1152 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5977 - val_classes_accuracy: 0.7810\n",
|
|||
|
|
"Epoch 46/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.0593 - p_loss: 2.0790e-06 - coord_loss: 0.0309 - classes_loss: 0.0284 - p_accuracy: 1.0000 - coord_IoU: 0.8117 - classes_accuracy: 0.9852 - val_loss: 0.3921 - val_p_loss: 9.9823e-07 - val_coord_loss: 0.2795 - val_classes_loss: 0.1125 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5989 - val_classes_accuracy: 0.8048\n",
|
|||
|
|
"Epoch 47/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.0582 - p_loss: 2.2083e-06 - coord_loss: 0.0316 - classes_loss: 0.0266 - p_accuracy: 1.0000 - coord_IoU: 0.8052 - classes_accuracy: 0.9857 - val_loss: 0.4020 - val_p_loss: 1.3179e-06 - val_coord_loss: 0.2929 - val_classes_loss: 0.1091 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6036 - val_classes_accuracy: 0.8048\n",
|
|||
|
|
"Epoch 48/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.0585 - p_loss: 2.2486e-06 - coord_loss: 0.0332 - classes_loss: 0.0253 - p_accuracy: 1.0000 - coord_IoU: 0.8175 - classes_accuracy: 0.9878 - val_loss: 0.3783 - val_p_loss: 8.4552e-07 - val_coord_loss: 0.2659 - val_classes_loss: 0.1125 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6222 - val_classes_accuracy: 0.7905\n",
|
|||
|
|
"Epoch 49/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.0535 - p_loss: 1.7950e-06 - coord_loss: 0.0302 - classes_loss: 0.0233 - p_accuracy: 1.0000 - coord_IoU: 0.8218 - classes_accuracy: 0.9894 - val_loss: 0.3774 - val_p_loss: 2.5507e-06 - val_coord_loss: 0.2659 - val_classes_loss: 0.1115 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6127 - val_classes_accuracy: 0.7857\n",
|
|||
|
|
"Epoch 50/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.0472 - p_loss: 1.3065e-06 - coord_loss: 0.0260 - classes_loss: 0.0212 - p_accuracy: 1.0000 - coord_IoU: 0.8263 - classes_accuracy: 0.9931 - val_loss: 0.3771 - val_p_loss: 1.0610e-06 - val_coord_loss: 0.2671 - val_classes_loss: 0.1100 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6085 - val_classes_accuracy: 0.8095\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"batch_size=16\n",
|
|||
|
|
"model = create_model_localisation()\n",
|
|||
|
|
"model.summary()\n",
|
|||
|
|
"opt = Adam(learning_rate=3e-4) \n",
|
|||
|
|
"\n",
|
|||
|
|
"# Ici mettre, dans l'ordre, les fonctions de coût associées à chacune des sorties\n",
|
|||
|
|
"loss=['binary_crossentropy', 'mean_squared_error', 'binary_crossentropy']\n",
|
|||
|
|
"# On va associer une métrique à chaque sortie : l'accuracy pour les deux classifications, \n",
|
|||
|
|
"# et l'IoU définie plus tôt pour la qualité des boîtes englobantes. \n",
|
|||
|
|
"metrics=[ ['accuracy'], [iou()], ['accuracy']]\n",
|
|||
|
|
"loss_weights = [1, 1, 1]\n",
|
|||
|
|
"\n",
|
|||
|
|
"model.compile(loss=loss,\n",
|
|||
|
|
" optimizer=opt,\n",
|
|||
|
|
" metrics=metrics,\n",
|
|||
|
|
" loss_weights=loss_weights\n",
|
|||
|
|
")\n",
|
|||
|
|
"history = model.fit(\n",
|
|||
|
|
" x_train,\n",
|
|||
|
|
" [y_train[:,0], y_train[:,1:5], y_train[:,5:]],\n",
|
|||
|
|
" epochs=50,\n",
|
|||
|
|
" batch_size=batch_size, \n",
|
|||
|
|
" validation_data=(x_val, [y_val[:,0], y_val[:,1:5], y_val[:,5:]])\n",
|
|||
|
|
")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 84,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "Jc-sGe8LNzA-"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
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{
|
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|
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAADHCAYAAAD2xE+bAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAACzTUlEQVR4nOz9edxty3rXhX6fqhpjzvk2q939PufsE9JAGjBEk4gJEEEFJJLgxRAETDAY9MoFr1xpP15QAYGP9wIaMcQLEhoTEhGIkYgKBDiE3gQIJCc5JznN7rvVvN2cY4yq5/7xPDXGmO9ea++11tlnv2sls/Zn7vXOOboaVb+n6ukfUVV2bdd2bdd2bdcAwkV3YNd2bdd2bdcenrbbFHZt13Zt13ZtbLtNYdd2bdd2bdfGttsUdm3Xdm3Xdm1su01h13Zt13Zt18a22xR2bdd2bdd2bWzv2qYgIv+JiPwpEbnrPUXkAyJyLCLxHu73mIj8gIj8C+9WH9+tJiLfIyJfd9H92LWHt4nIl4nI3xeRa29zzp8Qkd/tf/9sEfnwAz7rm0XkP33Qvu7arm01Vb3rB/jfgP/8Dr9/FfAykPz7LwK+HYhvd7873Od7gV97h98b4H8F/qW3ufafAsf+ycB69v23308/HqYPoMBnXXQ/3qGPTwPfBbzo/f3gp3i/P+H3+apzv/9B//3r/fvX+1wfn/s848e/HPg+4BbwJvC3gC++y7U/BvwHs2d90J+VzvXhY0Dvx4r/fQzcfJv3eT/w14En7uG9f/d9jtXXAx+6aAw8aP8vqJ//BfBPgAH4XZ/ivb7CsfDnz/3+z/nv3zv7TYGTc1j9zX7sCvDHfR09An4E+K2zawX4DcAP+j2eB74T+Ol3G/u3wfDv8t+/9F7e8Z0khW8FfpWIyLnffzXwZ1R1AFDV71HVr1XVfLcbiUh6h2eNTVV7Vf3Fqvp9b3PO56vqgaoeAH8T+PX1u6r+3nt5zp36dD/9/EncCsYw/N/exXv+CPDv1C8+D18DfPTceX97Ns/186KIXAK+G/hvgGvAs8B/BmzudK33/Q+IyM+8h759G/BnsMX+w8A3qeqV+Qlz3KjqJ1X156rqq/f05rv26W4fAX4zxmi+G+014GeJyPXZb1+HYfh8++fOYfUP+O9/EDgAPhe4DPwS72dtfxj4jdjGcA34HOAvAL/4fjrqa/e/gzFJ/847nA68s/roLwDXgZ89e8hV4CuBPykiQUR+q4h8VETeEJHvqOKyiHxQRFREvkFEPgH81dlvSUR+j9/3m1yl9E1+3U8Tkf9DRN4UkQ+LyNfczyD4Pf5dEfkhEbkhIn9ZRJ6bHVMR+Q9F5EeBHxWRrxCR50Xkt4jIy8D/ICJXReS7ReQ1v8d3i8j7Zvf4XhH5tf7314vIh0Tkv/Jzf1xEftH99vku7/G7ROQ7ReRPi8iRiPwTEfkcEfltIvKqiHxSRP612fm/xt/7SER+TER+3bn7/WYReUlEXhSRX+tj8Vl+bOHv8AkRecVVEqs79UtVX1HVPwL8/XfjPb39L8CXO74AfiHwjzFO6l7a53jfvk1Vs6qeqer/rqr/+E4nq+r3Az+EEeU9NVV9Afge4AvgrVjy377S1Z43ReT7RORn1OtF5GeKyP/l8/NngeXs2FeIyPOz7+8Xkf/ZMfiGiHyTiHwu8M3YgnQsIjf93FEN5d//PRH5iNPQd4nIM7NjKiL/voj8qPfxv70D03ffbUbbv8ZxecOf88Ui8o/9Wd80O/8zReSv+ru9LiJ/RkSuzI5/kYh8v4/Vd4rInz33jncd5/NNVb9VVb8H48jfjdZha+PXel8i8MsxxuFe2xcD/6Oq3lDVoqo/rKr/k9/vs4H/EPgVqvpXVXWjqqeq+mdU9ffdZ19/NibZ/wbga0WkfacL3nZTUNUz4DvY3mG+BvhhVf1HwP8D+Grg5wLPADeA//bcbX4uRni/4Ny9fwfbHP6vF5F94P8A/kfgCWzQ/4iIfN47vUhtIvJVwG8H/k3gcX/Gt5077auBLwXqfZ/CduPngG/ExuV/8O8fAM6Ab+Lu7UsxDvIx4A8Af+xuhCYif0RE/si9vg/wbwB/CrgKfD/wl71/zwL/OfBHZ+e+im3Yl4BfA/xBEfkif+4vBP5j4F8BPgsTg+ft92EL6xf68WeB//d99PNTbWvgL+KEhmHuT97H9T8CZBH5VhH5RbPN5Y5NRL4Ye99/cK8PEJH3A/86Ng+1fTWOJTGp448Dvw5jpv4o8F2+4bbYQvKnMKx9J3eRtHyR+W7g45hK4Fng21X1h4B/n0niuXKHa38e8F9idPq03+Pbz532ldii9DP8vF/AHZqIfHndeO6jfSnw2dgi+YeA34Fh7vOBrxGRn1tv7/18Blsf3o+pOfCx+vOYiuQaRr+/dNavu47zffb1U2l/kmld/AWYmufF+7j+7wC/xzfRzz537OcDz6vq3/vUu8nXYQzXd/j3f+Mdr7gHHdqXAzeBpX//W8D/0//+IeDnz859GtO5Jib91k+5m86LczYFDEh/89zz/yjwO9+hj+N9ME7uG2bHAnAKPDfT8/28czrCrr7fXe7/hcCNuzzv64GPzI7t+TOeuhf93R2eNdoUMCL5P2bH/g1MLxn9+6Gff+Uu9/oLwG/0v/848F/Ojn1WfRZGoCfAZ86O/yzgx9+hr4l3z6bwux1rfxvTt74CrIAPsW1TGByP9fPR2X0+1+/1vJ/3XcCTd7j2yPv93wByJ2zO7vkxx0e1KWz8Hn/tDlj674D/4tz1H8YYo5+DLRoyO/Z9uF7Ycfj8bOxfO9+X2Xt86E7j53//MeAPzI4dYDT5wRm+vnx2/DuY6bIfZN7Ojd+zs+NvAL989v3PAf/RXe711cD3+98/B3jh3Fh9aPasu47zO/T3T/Pu2BTqPP0o8FOxTfdXAr+Wt9oUbrON11/gx1YY8/oPfX4+AvwiP/Y7gL9zD2O/Pnfv22yvr3v+21f79z8K/MV3esd39D5S1Q8BrwNfLSKfCXwJxsmDcdJ/3kW4m9gmkYEnZ7f45Ds9Y9aeA7603s/v+SsxTv5+7vGHZ9e/iS16z75Nn15T1XX9IiJ7IvJHReTjInIb+BvAFbm719So4lDVU//z4D76/HbtldnfZ8DrOtluzubPcg7577ja4CbG1T7m5zzD9nvP/34cA9A/nI3b/+a/f0pNRH6lqzqOReR73u5cx9rjGFF8t5qker79HVW9Mvt85uz6H1LVr1fV92EqnmcwbvX8tYcYpj4fuBf707djNrSgqgu/x7/sx+bj+Bzwm87h9/3ej2eAF9Sp09vH7/K89wMfV7fZ3Wd7Zn5fVT3GFuc5/ucquVPePazCW/F6/nvF6pMi8u0i8oLT2J9mG6vnx+pex/lTajOsHovIB97h9D8F/HrgX8Ykmzu1LzqH178MpoVR1d+rqv88Ju18B/CdYur3NzAG+53afzW/Nyb5zdsvxRihv+Tf/wzwi0Tkben6Xl1Sq6j0q4C/rKp1oj+J7W7zl16q6V5r0/M3e5tjnwT++rn7Hajqf3CP/az3+HXn7rHSbaP1+eee//6bMA7gS1X1Esa5gG0uD2Vz0fnPAf8Vxh1fwcBQ+/wS8L7ZJe+f/f06RrCfPxuzy2oG2U+pqelBq5HtXmwtfxob//tRHd3puT+McVNfcJfjr2Dj9c7i9Ds8avb3J4Hfcw57e6r6bdj4P3tOrXi3ReeTwAfkzk4Pb0dPYNLI3Ia2jy06L9z1iotpvxd7l5/uNPar2Mbq+bGa4/XtxvlTarptFP7EO5z+p4D/O/CXZszggzzzNjYe+8BnAH8FeJ986u74X4dtwp8Qs5d+J+bZ+W+/3UX3syn8K8C/h3kk1fbNmF7sOQARedx1+vfaXgF+yuz7dwOfIyK/WkQa/3yxmIHtXts3A79NRD7f+3RZRP6t+7geTC1zBtz0nft33uf1F9FaYIGpHQYxY/e/Njv+HcCvEZHPFZE9YPRrV9UC/PeYDeIJABF5VkTuqGv240t/HsDCv78b7b8G/lVMOrvnJuag8JvEHQJc//8rMN3tnc6/jnFS//TcoYWILOvnP
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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"text/plain": [
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|||
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"<Figure size 432x288 with 2 Axes>"
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]
|
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},
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"metadata": {
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},
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"output_type": "display_data"
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{
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"data": {
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
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|||
|
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},
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|
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{
|
|||
|
|
"data": {
|
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"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 2 Axes>"
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]
|
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},
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"metadata": {
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|||
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"needs_background": "light"
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},
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|||
|
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
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"text/plain": [
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|||
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"<Figure size 432x288 with 2 Axes>"
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]
|
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
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|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# Analyse des résultats : courbes d'évolution de la fonction de perte, et de l'IoU des boîtes englobantes ainsi que de la précision des classes prédites\n",
|
|||
|
|
"plot_training_analysis(history, metric='loss')\n",
|
|||
|
|
"plot_training_analysis(history, metric='coord_IoU')\n",
|
|||
|
|
"plot_training_analysis(history, metric='classes_accuracy')\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Prédiction des données de test\n",
|
|||
|
|
"y_pred_presence, y_pred_coords, y_pred_classes = model.predict(x_test)\n",
|
|||
|
|
"y_pred = np.zeros(y_test.shape)\n",
|
|||
|
|
"for i in range(y_pred.shape[0]):\n",
|
|||
|
|
" y_pred[i, 0] = y_pred_presence[i]\n",
|
|||
|
|
" y_pred[i, 1:5] = y_pred_coords[i]\n",
|
|||
|
|
" y_pred[i, 5:] = y_pred_classes[i]\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Affichage des résultats sur plusieurs images\n",
|
|||
|
|
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=1, image_size=IMAGE_SIZE)\n",
|
|||
|
|
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=2, image_size=IMAGE_SIZE)\n",
|
|||
|
|
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=3, image_size=IMAGE_SIZE)\n",
|
|||
|
|
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=4, image_size=IMAGE_SIZE)\n",
|
|||
|
|
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=5, image_size=IMAGE_SIZE)\n",
|
|||
|
|
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=6, image_size=IMAGE_SIZE)\n",
|
|||
|
|
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=7, image_size=IMAGE_SIZE)\n",
|
|||
|
|
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=8, image_size=IMAGE_SIZE)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 85,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "UVU1bR_YEIMs"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"La précision globale est de 56.2%\n",
|
|||
|
|
"\n",
|
|||
|
|
"------------------------------------------\n",
|
|||
|
|
"| Classe | Précision | Rappel | F1-score |\n",
|
|||
|
|
"------------------------------------------\n",
|
|||
|
|
"| MESCHA | 0.14 | 0.09 | 0.11 |\n",
|
|||
|
|
"------------------------------------------\n",
|
|||
|
|
"| VEREUR | 0.52 | 0.59 | 0.55 |\n",
|
|||
|
|
"------------------------------------------\n",
|
|||
|
|
"| ECUROU | 0.86 | 0.86 | 0.86 |\n",
|
|||
|
|
"------------------------------------------\n",
|
|||
|
|
"| PIEBAV | 0.91 | 0.91 | 0.91 |\n",
|
|||
|
|
"------------------------------------------\n",
|
|||
|
|
"| SITTOR | 0.55 | 0.27 | 0.36 |\n",
|
|||
|
|
"------------------------------------------\n",
|
|||
|
|
"| PINARB | 0.71 | 0.77 | 0.74 |\n",
|
|||
|
|
"------------------------------------------\n",
|
|||
|
|
"| MESNOI | 0.36 | 0.57 | 0.44 |\n",
|
|||
|
|
"------------------------------------------\n",
|
|||
|
|
"| MESNON | 0.29 | 0.36 | 0.32 |\n",
|
|||
|
|
"------------------------------------------\n",
|
|||
|
|
"| MESBLE | 0.30 | 0.27 | 0.29 |\n",
|
|||
|
|
"------------------------------------------\n",
|
|||
|
|
"| ROUGOR | 0.71 | 0.71 | 0.71 |\n",
|
|||
|
|
"------------------------------------------\n",
|
|||
|
|
"| ACCMOU | 0.71 | 0.77 | 0.74 |\n",
|
|||
|
|
"------------------------------------------\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"class_res, accuracy = global_accuracy(y_test, y_pred)\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(f\"La précision globale est de {100 * accuracy:.1f}%\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"print()\n",
|
|||
|
|
"print(\"------------------------------------------\")\n",
|
|||
|
|
"print(\"| Classe | Précision | Rappel | F1-score |\")\n",
|
|||
|
|
"print(\"------------------------------------------\")\n",
|
|||
|
|
"for i in range(num_classes):\n",
|
|||
|
|
" print(f\"| {class_labels[i]:7s}| {class_res[i]['Precision']:.2f} | {class_res[i]['Rappel']:.2f} | {class_res[i]['F-score']:.2f} |\")\n",
|
|||
|
|
" print(\"------------------------------------------\")\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "eiFO6fBns-OI"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"En pratique, il est délicat de trouver une bonne combinaison des fonctions de perte tel que vous l'avez fait sur les cellules précédentes. L'entropie croisée et l'erreur quadratique moyenne donnent des valeurs trop différentes pour être combinables efficacement.\n",
|
|||
|
|
"\n",
|
|||
|
|
"Une variante, peut-être plus simple à faire fonctionner, est d'utiliser uniquement l'erreur quadratique moyenne comme perte pour toutes les sorties. C'est cette variante qui est implémentée dans l'algorithme YOLO, dont nous implémenterons une version simplifiée dans le prochain TP.\n",
|
|||
|
|
"Testez cette solution ci-dessous. Comme sur l'exercice précédent, n'hésitez pas à faire varier le poids des différents éléments de la fonction de coût."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 86,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "ttGCeqz0Dc3y"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Epoch 1/50\n",
|
|||
|
|
"118/118 [==============================] - 11s 88ms/step - loss: 1.4310 - p_loss: 0.0151 - coord_loss: 1.3283 - classes_loss: 0.0876 - p_accuracy: 0.9989 - coord_IoU: 0.4143 - classes_accuracy: 0.0859 - val_loss: 0.6519 - val_p_loss: 5.9735e-04 - val_coord_loss: 0.5653 - val_classes_loss: 0.0860 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.4729 - val_classes_accuracy: 0.1286\n",
|
|||
|
|
"Epoch 2/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.5781 - p_loss: 0.0011 - coord_loss: 0.4942 - classes_loss: 0.0828 - p_accuracy: 1.0000 - coord_IoU: 0.5069 - classes_accuracy: 0.1729 - val_loss: 0.5192 - val_p_loss: 5.8288e-04 - val_coord_loss: 0.4374 - val_classes_loss: 0.0812 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5086 - val_classes_accuracy: 0.2143\n",
|
|||
|
|
"Epoch 3/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.4808 - p_loss: 7.0716e-04 - coord_loss: 0.4026 - classes_loss: 0.0774 - p_accuracy: 1.0000 - coord_IoU: 0.5380 - classes_accuracy: 0.2964 - val_loss: 0.4908 - val_p_loss: 9.9283e-04 - val_coord_loss: 0.4128 - val_classes_loss: 0.0770 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5183 - val_classes_accuracy: 0.3143\n",
|
|||
|
|
"Epoch 4/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.4210 - p_loss: 4.7277e-04 - coord_loss: 0.3479 - classes_loss: 0.0726 - p_accuracy: 1.0000 - coord_IoU: 0.5572 - classes_accuracy: 0.3881 - val_loss: 0.4603 - val_p_loss: 2.6880e-04 - val_coord_loss: 0.3883 - val_classes_loss: 0.0718 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.4756 - val_classes_accuracy: 0.3476\n",
|
|||
|
|
"Epoch 5/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.3553 - p_loss: 2.5311e-04 - coord_loss: 0.2874 - classes_loss: 0.0676 - p_accuracy: 1.0000 - coord_IoU: 0.5813 - classes_accuracy: 0.4316 - val_loss: 0.3913 - val_p_loss: 1.5167e-04 - val_coord_loss: 0.3239 - val_classes_loss: 0.0672 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5942 - val_classes_accuracy: 0.3952\n",
|
|||
|
|
"Epoch 6/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.3121 - p_loss: 2.1208e-04 - coord_loss: 0.2483 - classes_loss: 0.0636 - p_accuracy: 1.0000 - coord_IoU: 0.6031 - classes_accuracy: 0.4708 - val_loss: 0.3978 - val_p_loss: 1.8132e-04 - val_coord_loss: 0.3330 - val_classes_loss: 0.0647 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5796 - val_classes_accuracy: 0.4429\n",
|
|||
|
|
"Epoch 7/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.2712 - p_loss: 1.4645e-04 - coord_loss: 0.2110 - classes_loss: 0.0600 - p_accuracy: 1.0000 - coord_IoU: 0.6217 - classes_accuracy: 0.5048 - val_loss: 0.3656 - val_p_loss: 9.9904e-05 - val_coord_loss: 0.3042 - val_classes_loss: 0.0613 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6001 - val_classes_accuracy: 0.4762\n",
|
|||
|
|
"Epoch 8/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.2488 - p_loss: 1.8330e-04 - coord_loss: 0.1911 - classes_loss: 0.0576 - p_accuracy: 1.0000 - coord_IoU: 0.6303 - classes_accuracy: 0.5223 - val_loss: 0.3516 - val_p_loss: 2.8725e-04 - val_coord_loss: 0.2916 - val_classes_loss: 0.0597 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6061 - val_classes_accuracy: 0.4952\n",
|
|||
|
|
"Epoch 9/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 86ms/step - loss: 0.2226 - p_loss: 1.1546e-04 - coord_loss: 0.1673 - classes_loss: 0.0552 - p_accuracy: 1.0000 - coord_IoU: 0.6481 - classes_accuracy: 0.5451 - val_loss: 0.3517 - val_p_loss: 9.4986e-05 - val_coord_loss: 0.2937 - val_classes_loss: 0.0579 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5699 - val_classes_accuracy: 0.5048\n",
|
|||
|
|
"Epoch 10/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.2028 - p_loss: 1.1011e-04 - coord_loss: 0.1493 - classes_loss: 0.0533 - p_accuracy: 1.0000 - coord_IoU: 0.6572 - classes_accuracy: 0.5551 - val_loss: 0.3346 - val_p_loss: 6.5773e-05 - val_coord_loss: 0.2787 - val_classes_loss: 0.0558 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6171 - val_classes_accuracy: 0.5571\n",
|
|||
|
|
"Epoch 11/50\n",
|
|||
|
|
"118/118 [==============================] - 10s 85ms/step - loss: 0.1809 - p_loss: 7.7123e-05 - coord_loss: 0.1293 - classes_loss: 0.0515 - p_accuracy: 1.0000 - coord_IoU: 0.6801 - classes_accuracy: 0.5870 - val_loss: 0.3483 - val_p_loss: 3.8490e-05 - val_coord_loss: 0.2935 - val_classes_loss: 0.0547 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.5973 - val_classes_accuracy: 0.5333\n",
|
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|
"Epoch 12/50\n",
|
|||
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|
"118/118 [==============================] - 10s 86ms/step - loss: 0.1652 - p_loss: 7.5254e-05 - coord_loss: 0.1155 - classes_loss: 0.0496 - p_accuracy: 1.0000 - coord_IoU: 0.6897 - classes_accuracy: 0.5960 - val_loss: 0.3261 - val_p_loss: 7.5853e-05 - val_coord_loss: 0.2731 - val_classes_loss: 0.0529 - val_p_accuracy: 1.0000 - val_coord_IoU: 0.6050 - val_classes_accuracy: 0.5619\n",
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"Epoch 13/50\n",
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" 86/118 [====================>.........] - ETA: 2s - loss: 0.1487 - p_loss: 5.7952e-05 - coord_loss: 0.0999 - classes_loss: 0.0488 - p_accuracy: 1.0000 - coord_IoU: 0.6981 - classes_accuracy: 0.6054"
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]
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},
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m/home/laurent/Documents/Cours/ENSEEIHT/S8 - Réseau Profond/TP6.ipynb Cell 33'\u001b[0m in \u001b[0;36m<cell line: 14>\u001b[0;34m()\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S8%20-%20R%C3%A9seau%20Profond/TP6.ipynb#ch0000032?line=6'>7</a>\u001b[0m loss_weights \u001b[39m=\u001b[39m [\u001b[39m1\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m1\u001b[39m]\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S8%20-%20R%C3%A9seau%20Profond/TP6.ipynb#ch0000032?line=8'>9</a>\u001b[0m model\u001b[39m.\u001b[39mcompile(loss\u001b[39m=\u001b[39mloss,\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S8%20-%20R%C3%A9seau%20Profond/TP6.ipynb#ch0000032?line=9'>10</a>\u001b[0m optimizer\u001b[39m=\u001b[39mopt,\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S8%20-%20R%C3%A9seau%20Profond/TP6.ipynb#ch0000032?line=10'>11</a>\u001b[0m metrics\u001b[39m=\u001b[39mmetrics,\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S8%20-%20R%C3%A9seau%20Profond/TP6.ipynb#ch0000032?line=11'>12</a>\u001b[0m loss_weights\u001b[39m=\u001b[39mloss_weights\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S8%20-%20R%C3%A9seau%20Profond/TP6.ipynb#ch0000032?line=12'>13</a>\u001b[0m )\n\u001b[0;32m---> <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S8%20-%20R%C3%A9seau%20Profond/TP6.ipynb#ch0000032?line=13'>14</a>\u001b[0m history \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39;49mfit(x_train, [y_train[:,\u001b[39m0\u001b[39;49m], y_train[:,\u001b[39m1\u001b[39;49m:\u001b[39m5\u001b[39;49m], y_train[:,\u001b[39m5\u001b[39;49m:]],\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S8%20-%20R%C3%A9seau%20Profond/TP6.ipynb#ch0000032?line=14'>15</a>\u001b[0m epochs\u001b[39m=\u001b[39;49m\u001b[39m50\u001b[39;49m,\n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S8%20-%20R%C3%A9seau%20Profond/TP6.ipynb#ch0000032?line=15'>16</a>\u001b[0m batch_size\u001b[39m=\u001b[39;49mbatch_size, \n\u001b[1;32m <a href='vscode-notebook-cell:/home/laurent/Documents/Cours/ENSEEIHT/S8%20-%20R%C3%A9seau%20Profond/TP6.ipynb#ch0000032?line=16'>17</a>\u001b[0m validation_data\u001b[39m=\u001b[39;49m(x_val, [y_val[:,\u001b[39m0\u001b[39;49m], y_val[:,\u001b[39m1\u001b[39;49m:\u001b[39m5\u001b[39;49m], y_val[:,\u001b[39m5\u001b[39;49m:]]))\n",
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"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/traceback_utils.py:64\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/traceback_utils.py?line=61'>62</a>\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/traceback_utils.py?line=62'>63</a>\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/traceback_utils.py?line=63'>64</a>\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/traceback_utils.py?line=64'>65</a>\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/traceback_utils.py?line=65'>66</a>\u001b[0m filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
|
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"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/engine/training.py:1389\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/engine/training.py?line=1386'>1387</a>\u001b[0m logs \u001b[39m=\u001b[39m tmp_logs \u001b[39m# No error, now safe to assign to logs.\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/engine/training.py?line=1387'>1388</a>\u001b[0m end_step \u001b[39m=\u001b[39m step \u001b[39m+\u001b[39m data_handler\u001b[39m.\u001b[39mstep_increment\n\u001b[0;32m-> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/engine/training.py?line=1388'>1389</a>\u001b[0m callbacks\u001b[39m.\u001b[39;49mon_train_batch_end(end_step, logs)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/engine/training.py?line=1389'>1390</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstop_training:\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/engine/training.py?line=1390'>1391</a>\u001b[0m \u001b[39mbreak\u001b[39;00m\n",
|
|||
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|
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py:438\u001b[0m, in \u001b[0;36mCallbackList.on_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=430'>431</a>\u001b[0m \u001b[39m\"\"\"Calls the `on_train_batch_end` methods of its callbacks.\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=431'>432</a>\u001b[0m \n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=432'>433</a>\u001b[0m \u001b[39mArgs:\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=433'>434</a>\u001b[0m \u001b[39m batch: Integer, index of batch within the current epoch.\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=434'>435</a>\u001b[0m \u001b[39m logs: Dict. Aggregated metric results up until this batch.\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=435'>436</a>\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=436'>437</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_should_call_train_batch_hooks:\n\u001b[0;32m--> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=437'>438</a>\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_batch_hook(ModeKeys\u001b[39m.\u001b[39;49mTRAIN, \u001b[39m'\u001b[39;49m\u001b[39mend\u001b[39;49m\u001b[39m'\u001b[39;49m, batch, logs\u001b[39m=\u001b[39;49mlogs)\n",
|
|||
|
|
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py:297\u001b[0m, in \u001b[0;36mCallbackList._call_batch_hook\u001b[0;34m(self, mode, hook, batch, logs)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=294'>295</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_call_batch_begin_hook(mode, batch, logs)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=295'>296</a>\u001b[0m \u001b[39melif\u001b[39;00m hook \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mend\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[0;32m--> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=296'>297</a>\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_batch_end_hook(mode, batch, logs)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=297'>298</a>\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=298'>299</a>\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=299'>300</a>\u001b[0m \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mUnrecognized hook: \u001b[39m\u001b[39m{\u001b[39;00mhook\u001b[39m}\u001b[39;00m\u001b[39m. Expected values are [\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mbegin\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m, \u001b[39m\u001b[39m\"\u001b[39m\u001b[39mend\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m]\u001b[39m\u001b[39m'\u001b[39m)\n",
|
|||
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|
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py:318\u001b[0m, in \u001b[0;36mCallbackList._call_batch_end_hook\u001b[0;34m(self, mode, batch, logs)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=314'>315</a>\u001b[0m batch_time \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime() \u001b[39m-\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_batch_start_time\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=315'>316</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_batch_times\u001b[39m.\u001b[39mappend(batch_time)\n\u001b[0;32m--> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=317'>318</a>\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_batch_hook_helper(hook_name, batch, logs)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=319'>320</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_batch_times) \u001b[39m>\u001b[39m\u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_batches_for_timing_check:\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=320'>321</a>\u001b[0m end_hook_name \u001b[39m=\u001b[39m hook_name\n",
|
|||
|
|
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py:356\u001b[0m, in \u001b[0;36mCallbackList._call_batch_hook_helper\u001b[0;34m(self, hook_name, batch, logs)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=353'>354</a>\u001b[0m \u001b[39mfor\u001b[39;00m callback \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallbacks:\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=354'>355</a>\u001b[0m hook \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(callback, hook_name)\n\u001b[0;32m--> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=355'>356</a>\u001b[0m hook(batch, logs)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=357'>358</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_timing:\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=358'>359</a>\u001b[0m \u001b[39mif\u001b[39;00m hook_name \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_hook_times:\n",
|
|||
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"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py:1034\u001b[0m, in \u001b[0;36mProgbarLogger.on_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=1032'>1033</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mon_train_batch_end\u001b[39m(\u001b[39mself\u001b[39m, batch, logs\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m-> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=1033'>1034</a>\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_batch_update_progbar(batch, logs)\n",
|
|||
|
|
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py:1106\u001b[0m, in \u001b[0;36mProgbarLogger._batch_update_progbar\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=1101'>1102</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mseen \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m add_seen\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=1103'>1104</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=1104'>1105</a>\u001b[0m \u001b[39m# Only block async when verbose = 1.\u001b[39;00m\n\u001b[0;32m-> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=1105'>1106</a>\u001b[0m logs \u001b[39m=\u001b[39m tf_utils\u001b[39m.\u001b[39;49msync_to_numpy_or_python_type(logs)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/callbacks.py?line=1106'>1107</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprogbar\u001b[39m.\u001b[39mupdate(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mseen, \u001b[39mlist\u001b[39m(logs\u001b[39m.\u001b[39mitems()), finalize\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n",
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|
|
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py:563\u001b[0m, in \u001b[0;36msync_to_numpy_or_python_type\u001b[0;34m(tensors)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py?line=559'>560</a>\u001b[0m \u001b[39mreturn\u001b[39;00m t\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py?line=560'>561</a>\u001b[0m \u001b[39mreturn\u001b[39;00m t\u001b[39m.\u001b[39mitem() \u001b[39mif\u001b[39;00m np\u001b[39m.\u001b[39mndim(t) \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m \u001b[39melse\u001b[39;00m t\n\u001b[0;32m--> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py?line=562'>563</a>\u001b[0m \u001b[39mreturn\u001b[39;00m tf\u001b[39m.\u001b[39;49mnest\u001b[39m.\u001b[39;49mmap_structure(_to_single_numpy_or_python_type, tensors)\n",
|
|||
|
|
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py:914\u001b[0m, in \u001b[0;36mmap_structure\u001b[0;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py?line=909'>910</a>\u001b[0m flat_structure \u001b[39m=\u001b[39m (flatten(s, expand_composites) \u001b[39mfor\u001b[39;00m s \u001b[39min\u001b[39;00m structure)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py?line=910'>911</a>\u001b[0m entries \u001b[39m=\u001b[39m \u001b[39mzip\u001b[39m(\u001b[39m*\u001b[39mflat_structure)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py?line=912'>913</a>\u001b[0m \u001b[39mreturn\u001b[39;00m pack_sequence_as(\n\u001b[0;32m--> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py?line=913'>914</a>\u001b[0m structure[\u001b[39m0\u001b[39m], [func(\u001b[39m*\u001b[39mx) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m entries],\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py?line=914'>915</a>\u001b[0m expand_composites\u001b[39m=\u001b[39mexpand_composites)\n",
|
|||
|
|
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py:914\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py?line=909'>910</a>\u001b[0m flat_structure \u001b[39m=\u001b[39m (flatten(s, expand_composites) \u001b[39mfor\u001b[39;00m s \u001b[39min\u001b[39;00m structure)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py?line=910'>911</a>\u001b[0m entries \u001b[39m=\u001b[39m \u001b[39mzip\u001b[39m(\u001b[39m*\u001b[39mflat_structure)\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py?line=912'>913</a>\u001b[0m \u001b[39mreturn\u001b[39;00m pack_sequence_as(\n\u001b[0;32m--> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py?line=913'>914</a>\u001b[0m structure[\u001b[39m0\u001b[39m], [func(\u001b[39m*\u001b[39;49mx) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m entries],\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/util/nest.py?line=914'>915</a>\u001b[0m expand_composites\u001b[39m=\u001b[39mexpand_composites)\n",
|
|||
|
|
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py:557\u001b[0m, in \u001b[0;36msync_to_numpy_or_python_type.<locals>._to_single_numpy_or_python_type\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py?line=553'>554</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_to_single_numpy_or_python_type\u001b[39m(t):\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py?line=554'>555</a>\u001b[0m \u001b[39m# Don't turn ragged or sparse tensors to NumPy.\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py?line=555'>556</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(t, tf\u001b[39m.\u001b[39mTensor):\n\u001b[0;32m--> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py?line=556'>557</a>\u001b[0m t \u001b[39m=\u001b[39m t\u001b[39m.\u001b[39;49mnumpy()\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py?line=557'>558</a>\u001b[0m \u001b[39m# Strings, ragged and sparse tensors don't have .item(). Return them as-is.\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/keras/utils/tf_utils.py?line=558'>559</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(t, (np\u001b[39m.\u001b[39mndarray, np\u001b[39m.\u001b[39mgeneric)):\n",
|
|||
|
|
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:1223\u001b[0m, in \u001b[0;36m_EagerTensorBase.numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1199'>1200</a>\u001b[0m \u001b[39m\"\"\"Copy of the contents of this Tensor into a NumPy array or scalar.\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1200'>1201</a>\u001b[0m \n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1201'>1202</a>\u001b[0m \u001b[39mUnlike NumPy arrays, Tensors are immutable, so this method has to copy\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1219'>1220</a>\u001b[0m \u001b[39m NumPy dtype.\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1220'>1221</a>\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1221'>1222</a>\u001b[0m \u001b[39m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[39;00m\n\u001b[0;32m-> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1222'>1223</a>\u001b[0m maybe_arr \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_numpy() \u001b[39m# pylint: disable=protected-access\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1223'>1224</a>\u001b[0m \u001b[39mreturn\u001b[39;00m maybe_arr\u001b[39m.\u001b[39mcopy() \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(maybe_arr, np\u001b[39m.\u001b[39mndarray) \u001b[39melse\u001b[39;00m maybe_arr\n",
|
|||
|
|
"File \u001b[0;32m/tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:1189\u001b[0m, in \u001b[0;36m_EagerTensorBase._numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1186'>1187</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_numpy\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1187'>1188</a>\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1188'>1189</a>\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_numpy_internal()\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1189'>1190</a>\u001b[0m \u001b[39mexcept\u001b[39;00m core\u001b[39m.\u001b[39m_NotOkStatusException \u001b[39mas\u001b[39;00m e: \u001b[39m# pylint: disable=protected-access\u001b[39;00m\n\u001b[1;32m <a href='file:///tmp/deepl/.env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py?line=1190'>1191</a>\u001b[0m \u001b[39mraise\u001b[39;00m core\u001b[39m.\u001b[39m_status_to_exception(e) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39m\n",
|
|||
|
|
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"batch_size=16\n",
|
|||
|
|
"model = create_model_localisation()\n",
|
|||
|
|
"opt = Adam(learning_rate=3e-4) \n",
|
|||
|
|
"\n",
|
|||
|
|
"loss = ['mean_squared_error', \"mean_squared_error\", 'mean_squared_error']\n",
|
|||
|
|
"metrics =[ ['accuracy'], [iou()], ['accuracy']]\n",
|
|||
|
|
"loss_weights = [1, 1, 1]\n",
|
|||
|
|
"\n",
|
|||
|
|
"model.compile(loss=loss,\n",
|
|||
|
|
" optimizer=opt,\n",
|
|||
|
|
" metrics=metrics,\n",
|
|||
|
|
" loss_weights=loss_weights\n",
|
|||
|
|
" )\n",
|
|||
|
|
"history = model.fit(x_train, [y_train[:,0], y_train[:,1:5], y_train[:,5:]],\n",
|
|||
|
|
" epochs=50,\n",
|
|||
|
|
" batch_size=batch_size, \n",
|
|||
|
|
" validation_data=(x_val, [y_val[:,0], y_val[:,1:5], y_val[:,5:]]))\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "gKlN9-clJMrI"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# Analyse des résultats : courbes d'évolution de la fonction de perte, et de l'IoU des boîtes englobantes ainsi que de la précision des classes prédites\n",
|
|||
|
|
"plot_training_analysis(history, metric='loss')\n",
|
|||
|
|
"plot_training_analysis(history, metric='coord_IoU')\n",
|
|||
|
|
"plot_training_analysis(history, metric='classes_accuracy')\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Prédiction des données de test\n",
|
|||
|
|
"y_pred_presence, y_pred_coords, y_pred_classes = model.predict(x_test)\n",
|
|||
|
|
"y_pred = np.zeros(y_test.shape)\n",
|
|||
|
|
"for i in range(y_pred.shape[0]):\n",
|
|||
|
|
" y_pred[i, 0] = y_pred_presence[i]\n",
|
|||
|
|
" y_pred[i, 1:5] = y_pred_coords[i]\n",
|
|||
|
|
" y_pred[i, 5:] = y_pred_classes[i]\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Affichage des résultats sur plusieurs images\n",
|
|||
|
|
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=1, image_size=IMAGE_SIZE)\n",
|
|||
|
|
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=2, image_size=IMAGE_SIZE)\n",
|
|||
|
|
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=3, image_size=IMAGE_SIZE)\n",
|
|||
|
|
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=4, image_size=IMAGE_SIZE)\n",
|
|||
|
|
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=5, image_size=IMAGE_SIZE)\n",
|
|||
|
|
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=6, image_size=IMAGE_SIZE)\n",
|
|||
|
|
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=7, image_size=IMAGE_SIZE)\n",
|
|||
|
|
"print_data_localisation(x_test, y_test, y_pred = y_pred, id=8, image_size=IMAGE_SIZE)\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"class_res, accuracy = global_accuracy(y_test, y_pred, iou_thres=0.4)\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(f\"La précision globale est de {100 * accuracy:.1f}%\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"print()\n",
|
|||
|
|
"print(\"------------------------------------------\")\n",
|
|||
|
|
"print(\"| Classe | Précision | Rappel | F1-score |\")\n",
|
|||
|
|
"print(\"------------------------------------------\")\n",
|
|||
|
|
"for i in range(num_classes):\n",
|
|||
|
|
" print(f\"| {class_labels[i]:7s}| {class_res[i]['Precision']:.2f} | {class_res[i]['Rappel']:.2f} | {class_res[i]['F-score']:.2f} |\")\n",
|
|||
|
|
" print(\"------------------------------------------\")\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "stQpmnmAt_bf"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"Compte-tenu de la taille réduite de la base de données, les résultats ne sont pas mal du tout ! On observe quelques confusions entre certaines classes mais les prédictions sont souvent intéressantes.\n",
|
|||
|
|
"\n",
|
|||
|
|
"Il devrait cependant subsister un fort surapprentissage à ce stade. Comme nous l'avons vu dans de précédents TPs, vous avez plusieurs possibilités qui s'offrent à vous pour le corriger : \n",
|
|||
|
|
"\n",
|
|||
|
|
"* Augmentation de la base de données. Vous pouvez pour cela vous appuyer sur l'exemple fourni en TP précédent, avec une classe *Sequence* et l'utilisation de la librairie *Albumentation*. Je vous fournis une Sequence qui fonctionne ci-dessous.\n",
|
|||
|
|
"* Utilisation de *transfer learning* : partant d'un réseau entraîné sur ImageNet (qui contient de nombreuses classes d'animaux), vous bénéficieriez de filtres très généraux qui aiderait à limiter le surapprentissage. \n",
|
|||
|
|
"\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "3Jp6jwZidJgx"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"# Code fourni pour vous aider à mettre en place l'augmentation de données"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "jKbK2AEkUF3M"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"Il y a des problèmes avec les versions d'openCV et d'Albumentation, voici comment les résoudre :"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "eTq-d6uadJgx"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"!pip uninstall opencv-python-headless==4.5.5.62\n",
|
|||
|
|
"!pip install opencv-python-headless==4.1.2.30\n",
|
|||
|
|
"!pip install -q -U albumentations\n",
|
|||
|
|
"!echo \"$(pip freeze | grep albumentations) is successfully installed\""
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "B5WuA43ZULxP"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"Définition des augmentations (uniquement colorimétriques ici)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "WS5wxAXvdJgx"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"from albumentations import (Compose, RandomBrightness, RandomContrast, RandomGamma, ShiftScaleRotate, RandomSizedBBoxSafeCrop)\n",
|
|||
|
|
"import albumentations as A\n",
|
|||
|
|
"\n",
|
|||
|
|
"AUGMENTATIONS_TRAIN = Compose([\n",
|
|||
|
|
" RandomContrast(limit=0.2, p=0.5),\n",
|
|||
|
|
" RandomGamma(gamma_limit=(80, 120), p=0.5),\n",
|
|||
|
|
" RandomBrightness(limit=0.2, p=0.5)\n",
|
|||
|
|
"], bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "sND_uaWPURfE"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"Une sequence qui permet d'implanter l'augmentation de données :"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "x9GXnEyYdJgy"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"from tensorflow.keras.utils import Sequence\n",
|
|||
|
|
"\n",
|
|||
|
|
"class MangeoireSequence(Sequence):\n",
|
|||
|
|
" # Initialisation de la séquence avec différents paramètres\n",
|
|||
|
|
" def __init__(self, x_set, y_set, batch_size,augmentations):\n",
|
|||
|
|
" self.x, self.y = x_set, y_set\n",
|
|||
|
|
" self.classes = ['MESCHA', 'VEREUR', 'ECUROU', 'PIEBAV', 'SITTOR', 'PINARB', 'MESNOI', 'MESNON', 'MESBLE', 'ROUGOR', 'ACCMOU']\n",
|
|||
|
|
" self.batch_size = batch_size\n",
|
|||
|
|
" self.augment = augmentations\n",
|
|||
|
|
" self.indices1 = np.arange(x_set.shape[0]) \n",
|
|||
|
|
" np.random.shuffle(self.indices1) # Les indices permettent d'accéder\n",
|
|||
|
|
" # aux données et sont randomisés à chaque epoch pour varier la composition\n",
|
|||
|
|
" # des batches au cours de l'entraînement\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Fonction calculant le nombre de pas de descente du gradient par epoch\n",
|
|||
|
|
" def __len__(self):\n",
|
|||
|
|
" return int(np.ceil(len(self.x) / float(self.batch_size)))\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Il y a des problèmes d'arrondi dans les conversions de boîtes englobantes \n",
|
|||
|
|
" # internes à la librairie Albumentations\n",
|
|||
|
|
" # Pour les contourner, si les boîtes sont trop proches des bords, on les érode un peu\n",
|
|||
|
|
" def erode_bounding_box(self, box):\n",
|
|||
|
|
" epsilon = 0.01\n",
|
|||
|
|
" \n",
|
|||
|
|
" xmin = max(box[0] - box[2]/2, epsilon)\n",
|
|||
|
|
" ymin = max(box[1] - box[3]/2, epsilon)\n",
|
|||
|
|
" xmax = min(box[0] + box[2]/2, 1-epsilon)\n",
|
|||
|
|
" ymax = min(box[1] + box[3]/2, 1-epsilon)\n",
|
|||
|
|
" \n",
|
|||
|
|
" cx = xmin + (xmax - xmin)/2\n",
|
|||
|
|
" cy = ymin + (ymax - ymin)/2\n",
|
|||
|
|
" width = xmax - xmin\n",
|
|||
|
|
" height = ymax - ymin\n",
|
|||
|
|
" \n",
|
|||
|
|
" return np.array([cx, cy, width, height])\n",
|
|||
|
|
" \n",
|
|||
|
|
" # Application de l'augmentation de données à chaque image du batch et aux\n",
|
|||
|
|
" # boîtes englobantes associées\n",
|
|||
|
|
" def apply_augmentation(self, bx, by):\n",
|
|||
|
|
"\n",
|
|||
|
|
" batch_x = np.zeros((bx.shape[0], IMAGE_SIZE, IMAGE_SIZE, 3))\n",
|
|||
|
|
" batch_y = by\n",
|
|||
|
|
" \n",
|
|||
|
|
" # Pour chaque image du batch\n",
|
|||
|
|
" for i in range(len(bx)):\n",
|
|||
|
|
" bboxes = []\n",
|
|||
|
|
" box = by[i,1:5]\n",
|
|||
|
|
" # Dénormalisation des coordonnées de boites englobantes\n",
|
|||
|
|
" box[0] = (box[0]*y_std[1] + y_mean[1])\n",
|
|||
|
|
" box[1] = (box[1]*y_std[2] + y_mean[2])\n",
|
|||
|
|
" box[2] = (box[2]*y_std[3] + y_mean[3])\n",
|
|||
|
|
" box[3] = (box[3]*y_std[4] + y_mean[4])\n",
|
|||
|
|
" box = self.erode_bounding_box(box)\n",
|
|||
|
|
" bboxes.append(box)\n",
|
|||
|
|
" \n",
|
|||
|
|
" class_labels = []\n",
|
|||
|
|
" class_id = np.argmax(by[i, 5:])\n",
|
|||
|
|
" class_labels.append(self.classes[class_id])\n",
|
|||
|
|
"\n",
|
|||
|
|
" img = bx[i]\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Application de l'augmentation à l'image et aux masques\n",
|
|||
|
|
" transformed = self.augment(image=img.astype('float32'), bboxes=bboxes, class_labels=class_labels)\n",
|
|||
|
|
" batch_x[i] = transformed['image']\n",
|
|||
|
|
" batch_y_transformed = transformed['bboxes']\n",
|
|||
|
|
" \n",
|
|||
|
|
" # Renormalisation des coordonnées de boîte englobante transformée\n",
|
|||
|
|
" batch_y[i, 1] = (batch_y_transformed[0][0] - y_mean[1])/y_std[1]\n",
|
|||
|
|
" batch_y[i, 2] = (batch_y_transformed[0][1] - y_mean[2])/y_std[2]\n",
|
|||
|
|
" batch_y[i, 3] = (batch_y_transformed[0][2] - y_mean[3])/y_std[3]\n",
|
|||
|
|
" batch_y[i, 4] = (batch_y_transformed[0][3] - y_mean[4])/y_std[4]\n",
|
|||
|
|
"\n",
|
|||
|
|
" return batch_x, batch_y\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Fonction appelée à chaque nouveau batch : sélection et augmentation des données\n",
|
|||
|
|
" def __getitem__(self, idx):\n",
|
|||
|
|
" batch_x = self.x[self.indices1[idx * self.batch_size:(idx + 1) * self.batch_size]]\n",
|
|||
|
|
" batch_y = self.y[self.indices1[idx * self.batch_size:(idx + 1) * self.batch_size]]\n",
|
|||
|
|
" batch_x, batch_y = self.apply_augmentation(batch_x, batch_y)\n",
|
|||
|
|
" \n",
|
|||
|
|
" batch_y = np.array(batch_y)\n",
|
|||
|
|
" return np.array(batch_x), [batch_y[:,0], batch_y[:,1:5], batch_y[:,5:]]\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Fonction appelée à la fin d'un epoch ; on randomise les indices d'accès aux données\n",
|
|||
|
|
" def on_epoch_end(self):\n",
|
|||
|
|
" np.random.shuffle(self.indices1)\n",
|
|||
|
|
" \n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "BUbm6gTMdJgy"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# Instanciation d'une Sequence\n",
|
|||
|
|
"train_gen = MangeoireSequence(x_train, y_train, 16, augmentations=AUGMENTATIONS_TRAIN)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Pour tester la séquence, nous sélectionnons les éléments du premier batch et les affichons\n",
|
|||
|
|
"batch_x, batch_y = train_gen.__getitem__(0)\n",
|
|||
|
|
"\n",
|
|||
|
|
"y_batch = np.zeros((batch_y[0].shape[0],1+4+num_classes))\n",
|
|||
|
|
"\n",
|
|||
|
|
"for i in range(batch_y[0].shape[0]):\n",
|
|||
|
|
" y_batch[i, 0] = batch_y[0][i]\n",
|
|||
|
|
" y_batch[i, 1:5] = batch_y[1][i]\n",
|
|||
|
|
" y_batch[i, 5:] = batch_y[2][i]\n",
|
|||
|
|
"\n",
|
|||
|
|
"print_data_localisation(batch_x, y_batch, image_size=IMAGE_SIZE)"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"accelerator": "GPU",
|
|||
|
|
"colab": {
|
|||
|
|
"collapsed_sections": [],
|
|||
|
|
"name": "TP6 - Localisation d'objet - Sujet.ipynb",
|
|||
|
|
"provenance": [],
|
|||
|
|
"toc_visible": true
|
|||
|
|
},
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3.10.6 64-bit",
|
|||
|
|
"language": "python",
|
|||
|
|
"name": "python3"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
|
|
"version": 3
|
|||
|
|
},
|
|||
|
|
"file_extension": ".py",
|
|||
|
|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython3",
|
|||
|
|
"version": "3.10.6"
|
|||
|
|
},
|
|||
|
|
"vscode": {
|
|||
|
|
"interpreter": {
|
|||
|
|
"hash": "767d51c1340bd893661ea55ea3124f6de3c7a262a8b4abca0554b478b1e2ff90"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 0
|
|||
|
|
}
|