397 lines
13 KiB
Plaintext
397 lines
13 KiB
Plaintext
{
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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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"source": [
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"# Classificateur d'images simple\n",
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"\n",
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"Ce notebook vous montre comment classifier des images en utilisant un réseau neuronal pré-entraîné.\n",
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"\n",
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"**Ce que vous allez apprendre :**\n",
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"- Comment charger et utiliser un modèle pré-entraîné\n",
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"- Prétraitement des images\n",
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"- Faire des prédictions sur des images\n",
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"- Comprendre les scores de confiance\n",
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"\n",
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"**Cas d'utilisation :** Identifier des objets dans des images (comme \"chat\", \"chien\", \"voiture\", etc.)\n",
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"\n",
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"---\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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"source": [
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"## Étape 1 : Importer les bibliothèques nécessaires\n",
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"\n",
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"Importons les outils dont nous avons besoin. Ne vous inquiétez pas si vous ne comprenez pas encore tout cela !\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Core libraries\n",
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"import numpy as np\n",
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"from PIL import Image\n",
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"import requests\n",
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"from io import BytesIO\n",
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"\n",
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"# TensorFlow for deep learning\n",
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"try:\n",
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" import tensorflow as tf\n",
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" from tensorflow.keras.applications import MobileNetV2\n",
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" from tensorflow.keras.applications.mobilenet_v2 import preprocess_input, decode_predictions\n",
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" print(\"✅ TensorFlow loaded successfully!\")\n",
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" print(f\" Version: {tf.__version__}\")\n",
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"except ImportError:\n",
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" print(\"❌ Please install TensorFlow: pip install tensorflow\")"
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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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"source": [
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"## Étape 2 : Charger le modèle pré-entraîné\n",
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"\n",
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"Nous allons utiliser **MobileNetV2**, un réseau neuronal déjà entraîné sur des millions d'images.\n",
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"\n",
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"C'est ce qu'on appelle **l'apprentissage par transfert** - utiliser un modèle entraîné par quelqu'un d'autre !\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"📦 Loading pre-trained MobileNetV2 model...\")\n",
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"print(\" This may take a minute on first run (downloading weights)...\")\n",
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"\n",
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"# Load the model\n",
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"# include_top=True means we use the classification layer\n",
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"# weights='imagenet' means it was trained on ImageNet dataset\n",
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"model = MobileNetV2(weights='imagenet', include_top=True)\n",
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"\n",
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"print(\"✅ Model loaded!\")\n",
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"print(f\" The model can recognize 1000 different object categories\")"
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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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"source": [
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"## Étape 3 : Fonctions utilitaires\n",
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"\n",
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"Créons des fonctions pour charger et préparer les images pour notre modèle.\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def load_image_from_url(url):\n",
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" \"\"\"\n",
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" Load an image from a URL.\n",
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" \n",
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" Args:\n",
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" url: Web address of the image\n",
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" \n",
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" Returns:\n",
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" PIL Image object\n",
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" \"\"\"\n",
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" response = requests.get(url)\n",
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" img = Image.open(BytesIO(response.content))\n",
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" return img\n",
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"\n",
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"\n",
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"def prepare_image(img):\n",
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" \"\"\"\n",
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" Prepare an image for the model.\n",
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" \n",
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" Steps:\n",
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" 1. Resize to 224x224 (model's expected size)\n",
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" 2. Convert to array\n",
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" 3. Add batch dimension\n",
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" 4. Preprocess for MobileNetV2\n",
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" \n",
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" Args:\n",
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" img: PIL Image\n",
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" \n",
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" Returns:\n",
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" Preprocessed image array\n",
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" \"\"\"\n",
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" # Resize to 224x224 pixels\n",
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" img = img.resize((224, 224))\n",
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" \n",
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" # Convert to numpy array\n",
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" img_array = np.array(img)\n",
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" \n",
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" # Add batch dimension (model expects multiple images)\n",
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" img_array = np.expand_dims(img_array, axis=0)\n",
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" \n",
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" # Preprocess for MobileNetV2\n",
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" img_array = preprocess_input(img_array)\n",
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" \n",
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" return img_array\n",
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"\n",
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"\n",
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"def classify_image(img):\n",
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" \"\"\"\n",
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" Classify an image and return top predictions.\n",
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" \n",
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" Args:\n",
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" img: PIL Image\n",
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" \n",
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" Returns:\n",
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" List of (class_name, confidence) tuples\n",
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" \"\"\"\n",
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" # Prepare the image\n",
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" img_array = prepare_image(img)\n",
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" \n",
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" # Make prediction\n",
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" predictions = model.predict(img_array, verbose=0)\n",
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" \n",
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" # Decode predictions to human-readable labels\n",
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" # top=5 means we get the top 5 most likely classes\n",
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" decoded = decode_predictions(predictions, top=5)[0]\n",
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" \n",
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" # Convert to simpler format\n",
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" results = [(label, float(confidence)) for (_, label, confidence) in decoded]\n",
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" \n",
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" return results\n",
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"\n",
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"\n",
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"print(\"✅ Helper functions ready!\")"
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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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"source": [
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"## Étape 4 : Tester sur des images d'exemple\n",
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"\n",
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"Essayons de classifier quelques images trouvées sur Internet !\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Sample images to classify\n",
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"# These are from Unsplash (free stock photos)\n",
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"test_images = [\n",
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" {\n",
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" \"url\": \"https://images.unsplash.com/photo-1514888286974-6c03e2ca1dba?w=400\",\n",
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" \"description\": \"A cat\"\n",
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" },\n",
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" {\n",
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" \"url\": \"https://images.unsplash.com/photo-1552053831-71594a27632d?w=400\",\n",
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" \"description\": \"A dog\"\n",
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" },\n",
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" {\n",
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" \"url\": \"https://images.unsplash.com/photo-1511919884226-fd3cad34687c?w=400\",\n",
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" \"description\": \"A car\"\n",
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" },\n",
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"]\n",
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"\n",
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"print(f\"🧪 Testing on {len(test_images)} images...\")\n",
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"print(\"=\" * 70)"
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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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"source": [
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"### Classifier chaque image\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"for i, img_data in enumerate(test_images, 1):\n",
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" print(f\"\\n📸 Image {i}: {img_data['description']}\")\n",
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" print(\"-\" * 70)\n",
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" \n",
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" try:\n",
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" # Load image\n",
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" img = load_image_from_url(img_data['url'])\n",
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" \n",
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" # Display image\n",
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" display(img.resize((200, 200))) # Show smaller version\n",
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" \n",
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" # Classify\n",
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" results = classify_image(img)\n",
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" \n",
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" # Show predictions\n",
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" print(\"\\n🎯 Top 5 Predictions:\")\n",
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" for rank, (label, confidence) in enumerate(results, 1):\n",
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" # Create a visual bar\n",
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" bar_length = int(confidence * 50)\n",
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" bar = \"█\" * bar_length\n",
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" \n",
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" print(f\" {rank}. {label:20s} {confidence*100:5.2f}% {bar}\")\n",
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" \n",
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" except Exception as e:\n",
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" print(f\"❌ Error: {e}\")\n",
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"\n",
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"print(\"\\n\" + \"=\" * 70)"
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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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"source": [
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"## Étape 5 : Essayez avec vos propres images !\n",
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"\n",
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"Remplacez l'URL ci-dessous par n'importe quelle URL d'image que vous souhaitez classifier.\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Try your own image!\n",
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"# Replace this URL with any image URL\n",
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"custom_image_url = \"https://images.unsplash.com/photo-1472491235688-bdc81a63246e?w=400\" # A flower\n",
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"\n",
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"print(\"🖼️ Classifying your custom image...\")\n",
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"print(\"=\" * 70)\n",
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"\n",
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"try:\n",
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" # Load and show image\n",
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" img = load_image_from_url(custom_image_url)\n",
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" display(img.resize((300, 300)))\n",
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" \n",
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" # Classify\n",
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" results = classify_image(img)\n",
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" \n",
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" # Show results\n",
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" print(\"\\n🎯 Top 5 Predictions:\")\n",
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" print(\"-\" * 70)\n",
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" for rank, (label, confidence) in enumerate(results, 1):\n",
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" bar_length = int(confidence * 50)\n",
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" bar = \"█\" * bar_length\n",
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" print(f\" {rank}. {label:20s} {confidence*100:5.2f}% {bar}\")\n",
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" \n",
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" # Highlight top prediction\n",
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" top_label, top_confidence = results[0]\n",
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" print(\"\\n\" + \"=\" * 70)\n",
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" print(f\"\\n🏆 Best guess: {top_label} ({top_confidence*100:.2f}% confident)\")\n",
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" \n",
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"except Exception as e:\n",
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" print(f\"❌ Error: {e}\")\n",
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" print(\" Make sure the URL points to a valid image!\")"
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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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"source": [
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"## 💡 Que s'est-il passé ?\n",
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"\n",
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"1. **Nous avons chargé un modèle pré-entraîné** - MobileNetV2 a été entraîné sur des millions d'images \n",
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"2. **Nous avons prétraité les images** - Redimensionnées et formatées pour le modèle \n",
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"3. **Le modèle a fait des prédictions** - Il a produit des probabilités pour 1000 classes d'objets \n",
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"4. **Nous avons décodé les résultats** - Converti les chiffres en étiquettes compréhensibles \n",
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"\n",
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"### Comprendre les scores de confiance\n",
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"\n",
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"- **90-100 %** : Très confiant (presque certainement correct) \n",
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"- **70-90 %** : Confiant (probablement correct) \n",
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"- **50-70 %** : Moyennement confiant (peut être correct) \n",
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"- **Moins de 50 %** : Peu confiant (incertain) \n",
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"\n",
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"### Pourquoi les prédictions pourraient être erronées ?\n",
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"\n",
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"- **Angle ou éclairage inhabituel** - Le modèle a été entraîné sur des photos classiques \n",
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"- **Objets multiples** - Le modèle s'attend à un objet principal \n",
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"- **Objets rares** - Le modèle ne connaît que 1000 catégories \n",
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"- **Image de mauvaise qualité** - Les images floues ou pixelisées sont plus difficiles à analyser \n",
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"\n",
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"---\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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"source": [
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"## 🚀 Prochaines étapes\n",
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"\n",
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"1. **Essayez différentes images :**\n",
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" - Trouvez des images sur [Unsplash](https://unsplash.com)\n",
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" - Clic droit → \"Copier l'adresse de l'image\" pour obtenir l'URL\n",
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"\n",
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"2. **Expérimentez :**\n",
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" - Que se passe-t-il avec l'art abstrait ?\n",
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" - Peut-il reconnaître des objets sous différents angles ?\n",
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" - Comment gère-t-il plusieurs objets ?\n",
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"\n",
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"3. **Apprenez-en davantage :**\n",
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" - Explorez les [leçons sur la vision par ordinateur](../lessons/4-ComputerVision/README.md)\n",
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" - Apprenez à entraîner votre propre classificateur d'images\n",
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" - Comprenez le fonctionnement des CNNs (Convolutional Neural Networks)\n",
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"\n",
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"---\n",
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"\n",
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"## 🎉 Félicitations !\n",
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"\n",
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"Vous venez de créer un classificateur d'images en utilisant un réseau neuronal de pointe !\n",
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"\n",
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"Cette même technique alimente :\n",
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"- Google Photos (organisation de vos photos)\n",
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"- Voitures autonomes (reconnaissance d'objets)\n",
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"- Diagnostic médical (analyse des radiographies)\n",
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"- Contrôle qualité (détection de défauts)\n",
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"\n",
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"Continuez à explorer et à apprendre ! 🚀\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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"source": [
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"\n---\n\n**Avertissement** : \nCe document a été traduit à l'aide du service de traduction automatique [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforcions d'assurer l'exactitude, veuillez noter que les traductions automatisées peuvent contenir des erreurs ou des inexactitudes. Le document original dans sa langue d'origine doit être considéré comme la source faisant autorité. Pour des informations critiques, il est recommandé de recourir à une traduction humaine professionnelle. Nous déclinons toute responsabilité en cas de malentendus ou d'interprétations erronées résultant de l'utilisation de cette traduction.\n"
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