{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Simple Image Classifier\n",
"\n",
"This notebook shows you how to classify images using a pre-trained neural network.\n",
"\n",
"**What you'll learn:**\n",
"- How to load and use a pre-trained model\n",
"- Image preprocessing\n",
"- Making predictions on images\n",
"- Understanding confidence scores\n",
"\n",
"**Use case:** Identify objects in images (like \"cat\", \"dog\", \"car\", etc.)\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1: Import Required Libraries\n",
"\n",
"Let's import the tools we need. Don't worry if you don't understand all of these yet!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Core libraries\n",
"import numpy as np\n",
"from PIL import Image\n",
"import requests\n",
"from io import BytesIO\n",
"\n",
"# TensorFlow for deep learning\n",
"try:\n",
" import tensorflow as tf\n",
" from tensorflow.keras.applications import MobileNetV2\n",
" from tensorflow.keras.applications.mobilenet_v2 import preprocess_input, decode_predictions\n",
" print(\"✅ TensorFlow loaded successfully!\")\n",
" print(f\" Version: {tf.__version__}\")\n",
"except ImportError:\n",
" print(\"❌ Please install TensorFlow: pip install tensorflow\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 2: Load Pre-trained Model\n",
"\n",
"We'll use **MobileNetV2**, a neural network already trained on millions of images.\n",
"\n",
"This is called **Transfer Learning** - using a model someone else trained!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"📦 Loading pre-trained MobileNetV2 model...\")\n",
"print(\" This may take a minute on first run (downloading weights)...\")\n",
"\n",
"# Load the model\n",
"# include_top=True means we use the classification layer\n",
"# weights='imagenet' means it was trained on ImageNet dataset\n",
"model = MobileNetV2(weights='imagenet', include_top=True)\n",
"\n",
"print(\"✅ Model loaded!\")\n",
"print(f\" The model can recognize 1000 different object categories\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3: Helper Functions\n",
"\n",
"Let's create functions to load and prepare images for our model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def load_image_from_url(url):\n",
" \"\"\"\n",
" Load an image from a URL.\n",
" \n",
" Args:\n",
" url: Web address of the image\n",
" \n",
" Returns:\n",
" PIL Image object\n",
" \"\"\"\n",
" response = requests.get(url)\n",
" img = Image.open(BytesIO(response.content))\n",
" return img\n",
"\n",
"\n",
"def prepare_image(img):\n",
" \"\"\"\n",
" Prepare an image for the model.\n",
" \n",
" Steps:\n",
" 1. Resize to 224x224 (model's expected size)\n",
" 2. Convert to array\n",
" 3. Add batch dimension\n",
" 4. Preprocess for MobileNetV2\n",
" \n",
" Args:\n",
" img: PIL Image\n",
" \n",
" Returns:\n",
" Preprocessed image array\n",
" \"\"\"\n",
" # Resize to 224x224 pixels\n",
" img = img.resize((224, 224))\n",
" \n",
" # Convert to numpy array\n",
" img_array = np.array(img)\n",
" \n",
" # Add batch dimension (model expects multiple images)\n",
" img_array = np.expand_dims(img_array, axis=0)\n",
" \n",
" # Preprocess for MobileNetV2\n",
" img_array = preprocess_input(img_array)\n",
" \n",
" return img_array\n",
"\n",
"\n",
"def classify_image(img):\n",
" \"\"\"\n",
" Classify an image and return top predictions.\n",
" \n",
" Args:\n",
" img: PIL Image\n",
" \n",
" Returns:\n",
" List of (class_name, confidence) tuples\n",
" \"\"\"\n",
" # Prepare the image\n",
" img_array = prepare_image(img)\n",
" \n",
" # Make prediction\n",
" predictions = model.predict(img_array, verbose=0)\n",
" \n",
" # Decode predictions to human-readable labels\n",
" # top=5 means we get the top 5 most likely classes\n",
" decoded = decode_predictions(predictions, top=5)[0]\n",
" \n",
" # Convert to simpler format\n",
" results = [(label, float(confidence)) for (_, label, confidence) in decoded]\n",
" \n",
" return results\n",
"\n",
"\n",
"print(\"✅ Helper functions ready!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 4: Test on Sample Images\n",
"\n",
"Let's try classifying some images from the internet!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Sample images to classify\n",
"# These are from Unsplash (free stock photos)\n",
"test_images = [\n",
" {\n",
" \"url\": \"https://images.unsplash.com/photo-1514888286974-6c03e2ca1dba?w=400\",\n",
" \"description\": \"A cat\"\n",
" },\n",
" {\n",
" \"url\": \"https://images.unsplash.com/photo-1552053831-71594a27632d?w=400\",\n",
" \"description\": \"A dog\"\n",
" },\n",
" {\n",
" \"url\": \"https://images.unsplash.com/photo-1511919884226-fd3cad34687c?w=400\",\n",
" \"description\": \"A car\"\n",
" },\n",
"]\n",
"\n",
"print(f\"🧪 Testing on {len(test_images)} images...\")\n",
"print(\"=\" * 70)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Classify Each Image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for i, img_data in enumerate(test_images, 1):\n",
" print(f\"\\n📸 Image {i}: {img_data['description']}\")\n",
" print(\"-\" * 70)\n",
" \n",
" try:\n",
" # Load image\n",
" img = load_image_from_url(img_data['url'])\n",
" \n",
" # Display image\n",
" display(img.resize((200, 200))) # Show smaller version\n",
" \n",
" # Classify\n",
" results = classify_image(img)\n",
" \n",
" # Show predictions\n",
" print(\"\\n🎯 Top 5 Predictions:\")\n",
" for rank, (label, confidence) in enumerate(results, 1):\n",
" # Create a visual bar\n",
" bar_length = int(confidence * 50)\n",
" bar = \"█\" * bar_length\n",
" \n",
" print(f\" {rank}. {label:20s} {confidence*100:5.2f}% {bar}\")\n",
" \n",
" except Exception as e:\n",
" print(f\"❌ Error: {e}\")\n",
"\n",
"print(\"\\n\" + \"=\" * 70)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 5: Try Your Own Images!\n",
"\n",
"Replace the URL below with any image URL you want to classify."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Try your own image!\n",
"# Replace this URL with any image URL\n",
"custom_image_url = \"https://images.unsplash.com/photo-1472491235688-bdc81a63246e?w=400\" # A flower\n",
"\n",
"print(\"🖼️ Classifying your custom image...\")\n",
"print(\"=\" * 70)\n",
"\n",
"try:\n",
" # Load and show image\n",
" img = load_image_from_url(custom_image_url)\n",
" display(img.resize((300, 300)))\n",
" \n",
" # Classify\n",
" results = classify_image(img)\n",
" \n",
" # Show results\n",
" print(\"\\n🎯 Top 5 Predictions:\")\n",
" print(\"-\" * 70)\n",
" for rank, (label, confidence) in enumerate(results, 1):\n",
" bar_length = int(confidence * 50)\n",
" bar = \"█\" * bar_length\n",
" print(f\" {rank}. {label:20s} {confidence*100:5.2f}% {bar}\")\n",
" \n",
" # Highlight top prediction\n",
" top_label, top_confidence = results[0]\n",
" print(\"\\n\" + \"=\" * 70)\n",
" print(f\"\\n🏆 Best guess: {top_label} ({top_confidence*100:.2f}% confident)\")\n",
" \n",
"except Exception as e:\n",
" print(f\"❌ Error: {e}\")\n",
" print(\" Make sure the URL points to a valid image!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 💡 What Just Happened?\n",
"\n",
"1. **We loaded a pre-trained model** - MobileNetV2 was trained on millions of images\n",
"2. **We preprocessed images** - Resized and formatted them for the model\n",
"3. **The model made predictions** - It output probabilities for 1000 object classes\n",
"4. **We decoded the results** - Converted numbers to human-readable labels\n",
"\n",
"### Understanding Confidence Scores\n",
"\n",
"- **90-100%**: Very confident (almost certainly correct)\n",
"- **70-90%**: Confident (probably correct)\n",
"- **50-70%**: Somewhat confident (might be correct)\n",
"- **Below 50%**: Not very confident (uncertain)\n",
"\n",
"### Why might predictions be wrong?\n",
"\n",
"- **Unusual angle or lighting** - Model was trained on typical photos\n",
"- **Multiple objects** - Model expects one main object\n",
"- **Rare objects** - Model only knows 1000 categories\n",
"- **Low quality image** - Blurry or pixelated images are harder\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🚀 Next Steps\n",
"\n",
"1. **Try different images:**\n",
" - Find images on [Unsplash](https://unsplash.com)\n",
" - Right-click → \"Copy image address\" to get URL\n",
"\n",
"2. **Experiment:**\n",
" - What happens with abstract art?\n",
" - Can it recognize objects from different angles?\n",
" - How does it handle multiple objects?\n",
"\n",
"3. **Learn more:**\n",
" - Explore [Computer Vision lessons](../lessons/4-ComputerVision/README.md)\n",
" - Learn to train your own image classifier\n",
" - Understand how CNNs (Convolutional Neural Networks) work\n",
"\n",
"---\n",
"\n",
"## 🎉 Congratulations!\n",
"\n",
"You just built an image classifier using a state-of-the-art neural network!\n",
"\n",
"This same technique powers:\n",
"- Google Photos (organizing your photos)\n",
"- Self-driving cars (recognizing objects)\n",
"- Medical diagnosis (analyzing X-rays)\n",
"- Quality control (detecting defects)\n",
"\n",
"Keep exploring and learning! 🚀"
]
}
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Publicmirrored fromhttps://github.com/microsoft/AI-For-BeginnersAvailable
examples/03-image-classifier.ipynb
384lines · modepreview