395 lines
13 KiB
Plaintext
395 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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"# Enkel Bildklassificerare\n",
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"\n",
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"Den här notebooken visar hur du kan klassificera bilder med hjälp av ett förtränat neuralt nätverk.\n",
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"\n",
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"**Vad du kommer att lära dig:**\n",
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"- Hur man laddar och använder en förtränad modell\n",
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"- Bildförbehandling\n",
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"- Att göra förutsägelser på bilder\n",
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"- Förståelse för tillförlitlighetspoäng\n",
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"\n",
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"**Användningsområde:** Identifiera objekt i bilder (som \"katt\", \"hund\", \"bil\", 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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"## Steg 1: Importera nödvändiga bibliotek\n",
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"\n",
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"Låt oss importera de verktyg vi behöver. Oroa dig inte om du inte förstår alla dessa än!\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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"## Steg 2: Ladda förtränad modell\n",
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"\n",
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"Vi kommer att använda **MobileNetV2**, ett neuralt nätverk som redan är tränat på miljontals bilder.\n",
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"\n",
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"Detta kallas för **Transfer Learning** – att använda en modell som någon annan har tränat!\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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"## Steg 3: Hjälpfunktioner\n",
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"\n",
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"Låt oss skapa funktioner för att ladda och förbereda bilder för vår modell.\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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"## Steg 4: Testa på exempelbilder\n",
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"\n",
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"Låt oss prova att klassificera några bilder från 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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"### Klassificera Varje Bild\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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"## Steg 5: Testa med dina egna bilder!\n",
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"\n",
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"Byt ut URL:en nedan mot valfri bild-URL som du vill klassificera.\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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"## 💡 Vad hände precis?\n",
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"\n",
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"1. **Vi laddade en förtränad modell** - MobileNetV2 tränades på miljontals bilder \n",
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"2. **Vi förbehandlade bilder** - Ändrade storlek och formaterade dem för modellen \n",
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"3. **Modellen gjorde förutsägelser** - Den gav sannolikheter för 1000 objektklasser \n",
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"4. **Vi avkodade resultaten** - Omvandlade siffror till läsbara etiketter \n",
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"\n",
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"### Förståelse för tillförlitlighetspoäng\n",
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"\n",
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"- **90-100%**: Mycket säker (nästan garanterat korrekt) \n",
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"- **70-90%**: Säker (troligen korrekt) \n",
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"- **50-70%**: Någorlunda säker (kan vara korrekt) \n",
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"- **Under 50%**: Inte särskilt säker (osäker) \n",
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"\n",
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"### Varför kan förutsägelser vara fel?\n",
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"\n",
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"- **Ovanlig vinkel eller belysning** - Modellen tränades på typiska foton \n",
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"- **Flera objekt** - Modellen förväntar sig ett huvudobjekt \n",
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"- **Sällsynta objekt** - Modellen känner bara till 1000 kategorier \n",
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"- **Lågkvalitativ bild** - Suddiga eller pixliga bilder är svårare \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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"## 🚀 Nästa steg\n",
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"\n",
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"1. **Prova olika bilder:**\n",
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" - Hitta bilder på [Unsplash](https://unsplash.com)\n",
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" - Högerklicka → \"Kopiera bildadress\" för att få URL\n",
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"\n",
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"2. **Experimentera:**\n",
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" - Vad händer med abstrakt konst?\n",
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" - Kan den känna igen objekt från olika vinklar?\n",
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" - Hur hanterar den flera objekt?\n",
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"\n",
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"3. **Lär dig mer:**\n",
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" - Utforska [lektioner om datorseende](../lessons/4-ComputerVision/README.md)\n",
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" - Lär dig att träna din egen bildklassificerare\n",
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" - Förstå hur CNNs (Convolutional Neural Networks) fungerar\n",
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"\n",
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"---\n",
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"\n",
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"## 🎉 Grattis!\n",
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"\n",
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"Du har just byggt en bildklassificerare med hjälp av ett toppmodernt neuralt nätverk!\n",
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"\n",
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"Samma teknik används för:\n",
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"- Google Photos (organisera dina foton)\n",
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"- Självkörande bilar (känna igen objekt)\n",
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"- Medicinsk diagnos (analysera röntgenbilder)\n",
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"- Kvalitetskontroll (upptäcka defekter)\n",
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"\n",
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"Fortsätt utforska och lära dig! 🚀\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**Ansvarsfriskrivning**: \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, bör det noteras att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
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]
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