Image Classification Using Densnet Model | Transfer learning Densnet

DenseNet, specifically DenseNet201, is a powerful convolutional neural network model renowned for its intricate connectivity patterns, facilitating efficient feature reuse and propagation. Leveraging frameworks like Keras and TensorFlow further streamlines DenseNet implementation, offering flexible and scalable solutions for deploying models and conducting transfer learning. Through DenseNet transfer learning in TensorFlow, practitioners can harness pre-trained models to expedite the process of training and fine-tuning, thereby enhancing efficiency and performance in diverse image classification tasks.

This tutorial has 3 parts :

🔍 Data Preparation: We'll start by preparing and loading the train test and validation images.

🛠️ Model Building with TensorFlow Keras: Next, we'll dive into the implementation of densnet201 architecture using TensorFlow's Keras API. You'll learn how to configure and fine-tune the pre-trained model for your specific task.

📊 Testing the Model: Once the model is trained, we'll show you how to test the model using a new and fresh image

You can download the code here : https://ko-fi.com/s/233f61d885

More relevant content in this playlist : https://www.youtube.com/playlist?list=PLdkryDe59y4aCcCN4ioFpdLVAGZ_dFeFr

A perfect course for learning modern Computer Vision with deep dive in TensorFlow , Keras and Pytorch . You can find it here : http://bit.ly/3HeDy1V 
I also recommend this book, https://amzn.to/44GnlLW : "Make Your Own Neural Network - An In-depth Visual Introduction For Beginners ".
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00:00 Introduction 
00:11 Prepare the data
08:10  Build the Model - Transfer learning 
20:50 Test The Model 
~~~~~~~~~~~~~~ Credits ~~~~~~~~~~~~~ 
Music by Vincent Rubinetti 
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