Transfer Learning For Dog Breed Classification With Tensorflow Mobilenetv2 Model
In this machine learning project, we dive deep into Transfer Learning for Dog Breed Classification with TensorFlow MobileNetV2 Model! We’ll guide you through the entire process of using a pre-trained MobileNetV2 model dog breed classification machine learning project example code. This will help you to sharpen your skills on transfer learning projects for your future work reference.
This tutorial is perfect for those looking to expand their knowledge in data science, deep learning, and machine learning. We start by walking you through the training process where we prepared the model, saved it, and then tested it using a new notebook. You'll learn how to connect your Google Drive, manage your datasets, and work with image files for accurate predictions.
We also cover how to handle the test images, resize them, and use the model to predict dog breeds with a step-by-step explanation. By the end, you’ll see how the trained model achieves around 50% accuracy across 120 dog breeds and how you can further improve it.
Moreover, we'll show you how to convert this model to a TensorFlow Lite version, making it ready for mobile application deployment. Whether you're just starting with machine learning or looking to implement advanced techniques, this video has something for everyone!
Video Chapters:
00:00 Introduction
00:23 Connect GDrive to Google Colab
01:03 upload and uzip images to drive
03:22 pick images from test folder and display
06:07 Predicting the Dog Breed
08:32 Explaining Accuracy
09:43 Convert to TFLite model
10:46 Overview of Transfer learning Training
