Resnet50 Tutorial: Classifying Car Images with Transfer Learning and Tensorflow

In this comprehensive tutorial, you'll learn how to classify car images using the power of computer vision and deep learning. We'll be using Tensorflow and Keras to build a powerful Resnet50 model that can accurately classify car brands with transfer learning.

First, we'll install the necessary Python libraries and set up our environment for training and testing our model. Next, we'll dive into the details of transfer learning and how we can leverage the power of Resnet50 to train our model quickly and efficiently.

Throughout the tutorial, we'll cover topics such as image preprocessing, Configure the Resnet50 model , and model fine-tuning, to ensure that our car classifier performs as accurately as possible. By the end of the tutorial, you'll have the skills and knowledge necessary to build your own image classification models using Tensorflow and Keras, and to apply them to a variety of real-world problems.

You can find the code and instructions in this link : https://github.com/feitgemel/TensorFlowProjects/tree/master/transfer-learning/Resnet50-CarDetection

More relevant content in this playlist : https://www.youtube.com/playlist?list=PLdkryDe59y4bxVvpexwR6PMTHH6_vFXjA

Chapters : 00:00 Intro 00:18 Installation - Python libraries 02:15 Download the cars images dataset 03:42 Configure the Resnet50 model and run the training 17:32 Predict classes in images 23:02 Show the images with the prediction

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