
Image Recognition using MobileNetV2
Actual notebook-based tutorial for loading images in Python, preprocessing for MobileNetV2 and decoding ImageNet predictions.
AI Code School brings together YouTube lessons, written tutorials, code snippets, and project-ready guidance so you can learn and teach with confidence.

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Actual notebook-based tutorial for loading images in Python, preprocessing for MobileNetV2 and decoding ImageNet predictions.

Actual notebook-based tutorial for detecting faces, analyzing emotions with DeepFace and drawing emotion labels using OpenCV.

Driver identification pipeline using deep learning for recognizing driver identity from image/video input.

Realtime face emotion recognition using PyTorch, OpenCV and webcam inference.

Notebook-based emotion recognition tutorial using TensorFlow, MobileNetV2 transfer learning, seven emotion classes, Haar cascade face detection and realtime prediction logic.

OCR tutorial for extracting text from images using Python preprocessing and recognition logic.
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Object detection, OCR, pose estimation, drowsiness detection, driver identification and more.
PyTorch, TensorFlow, CNNs, model pipelines and practical classification projects.
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Actual notebook-based tutorial for loading images in Python, preprocessing for MobileNetV2 and decoding ImageNet predictions.

Actual notebook-based tutorial for detecting faces, analyzing emotions with DeepFace and drawing emotion labels using OpenCV.

Driver identification pipeline using deep learning for recognizing driver identity from image/video input.

Realtime face emotion recognition using PyTorch, OpenCV and webcam inference.

Notebook-based emotion recognition tutorial using TensorFlow, MobileNetV2 transfer learning, seven emotion classes, Haar cascade face detection and realtime prediction logic.

OCR tutorial for extracting text from images using Python preprocessing and recognition logic.
This layout gives you a clear place to explain code line-by-line in the same visual style as the rest of the website.
import cv2
image = cv2.imread("sample.jpg")
classifier = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = classifier.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow("Result", image)
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