8# Xception | Image Classification | Real-Time Industrial AI Vision using ImageNet

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In this video, we demonstrate image classification using a pre-trained Xception model with ImageNet, implemented in Python using TensorFlow.

The goal of this project is to introduce the fundamentals of computer vision by showing how a lightweight convolutional neural network can classify images without training a custom model.

You will learn how to :
- Load a pre-trained Xception model
- Perform image classification using ImageNet classes
- Run inference on an image.
- Understand the strengths and limitations.

This video is ideal for :
- Beginners in computer vision
- Python developers
- AI & machine learning enthusiasts
- Automation and industrial engineers
- Students learning deep learning fundamentals

The demonstration is done using :
- Python
- TensorFlow / Keras
- Xception
- ImageNet pre-trained weights

ImageNet Dataset used in this video:
https://deeplearning.cms.waikato.ac.nz/user-guide/class-maps/IMAGENET/

This content is part of the Industrial AI Series, where we explore practical and real-world computer vision models step by step.

In the next video, we will use InceptionV3 to compare results with Xception.

If you’re interested in artificial intelligence, computer vision, and industrial AI applications, don’t forget to like, subscribe, and share!

#ia #deeplearning #classification

Chapters :
00:00 Introduction
00:08 Xception

Видео 8# Xception | Image Classification | Real-Time Industrial AI Vision using ImageNet канала IndusCore Tech