How to classify images using MobileNet V2 ? Want to turn any JPG into a set of top-5 predictions in under 5 minutes?
In this hands-on tutorial I’ll walk you line-by-line through loading MobileNetV2, prepping an image with OpenCV, and decoding the results—all in pure Python.
Perfect for beginners who need a lightweight model or anyone looking to add instant AI super-powers to an app.
What You’ll Learn 🔍:
- Loading MobileNetV2 pretrained on ImageNet (1000 classes)
- Reading images with OpenCV and converting BGR → RGB
- Resizing to 224×224 & batching with
np.expand_dims - Using
preprocess_input(scales pixels to -1…1) - Running inference on CPU/GPU (
model.predict) - Grabbing the single highest class with
np.argmax - Getting human-readable labels & probabilities via
decode_predictions
Check out our tutorial here :
https://youtu.be/Nhe7WrkXnpM&list=UULFTiWJJhaH6BviSWKLJUM9sg
Link for the code :
https://ko-fi.com/s/f8e806c381
If you are interested in learning modern Computer Vision course with deep dive with TensorFlow , Keras and Pytorch , you can find it here : http://bit.ly/3HeDy1V
Perfect course for every computer vision enthusiastic
Before we continue , I actually recommend this book for deep learning based on Tensorflow and Keras : https://amzn.to/3STWZ2N
You can find more tutorials, and join my newsletter here : https://eranfeit.net/
Enjoy
Eran