AI — Computer Vision
What is Computer Vision?
Teaching computers to interpret visual information.
CV Tasks
| Task | Description |
|---|---|
| Classification | Identify objects |
| Detection | Locate objects |
| Segmentation | Pixel-level labeling |
| Generation | Create images |
Image Classification
import tensorflow as tf
# Load pre-trained model
model = tf.keras.applications.MobileNetV2()
# Predict
from tensorflow.keras.preprocessing import image
img = image.load_img('photo.jpg', target_size=(224, 224))
img_array = tf.keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0)
predictions = model.predict(img_array)
Object Detection
# Using pre-trained model
import tensorflow_hub as hub
detector = hub.load("https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2")
Mini Practice
- Classify images
- Detect objects
- Use pre-trained models
- Process video frames
Up Next
Continue with NLP — natural language processing.
Related Topics
Frequently Asked Questions about Computer Vision
What is Computer Vision in AI?
Computer Vision is a fundamental concept in AI. This lesson explains it step by step with clear examples, making it easy for beginners to understand.
How do I learn Computer Vision?
Start by reading the explanation above, then try the code examples. Practice by modifying the examples and experimenting with different values. Hands-on practice is the best way to learn Computer Vision.
Why is Computer Vision important in AI?
Computer Vision is essential for AI development. Understanding this concept will help you write better code and solve real-world problems more effectively.