In this video I will show you methods to efficiently load a custom dataset with images in directories. Depending on how your dataset is structured the method that is the easiest could vary and the most common ways to load is either having structured subfolders, a csv with with annotations or simply all of the images in a single folder. We use the functions image_dataset_from_directory, ImageDataGenerator together with flow_from_directory for subfolders. Pandas togethor with from_tensor_slices when dealing with an annotation csv file and lastly list_files when having all inside a single image folder directory. Hope you find this tutorial useful!
❤️ Support the channel ❤️
https://www.youtube.com/channel/UCkzW5JSFwvKRjXABI-UTAkQ/join
Paid Courses I recommend for learning (affiliate links, no extra cost for you):
⭐ TensorFlow Specialization https://bit.ly/3FmEVK1
⭐ Machine Learning Specialization https://bit.ly/3hjTBBt
⭐ Deep Learning Specialization https://bit.ly/3YcUkoI
📘 MLOps Specialization http://bit.ly/3wibaWy
📘 GAN Specialization https://bit.ly/3FmnZDl
📘 NLP Specialization http://bit.ly/3GXoQuP
✨ Free Resources that are great:
NLP: https://web.stanford.edu/class/cs224n/
CV: http://cs231n.stanford.edu/
Deployment: https://fullstackdeeplearning.com/
FastAI: https://www.fast.ai/
💻 My Deep Learning Setup and Recording Setup:
https://www.amazon.com/shop/aladdinpersson
GitHub Repository:
https://github.com/aladdinpersson/Machine-Learning-Collection
✅ One-Time Donations:
Paypal: https://bit.ly/3buoRYH
▶️ You Can Connect with me on:
Twitter - https://twitter.com/aladdinpersson
LinkedIn - https://www.linkedin.com/in/aladdin-persson-a95384153/
Github - https://github.com/aladdinpersson
OUTLINE:
0:00 - Introduction
0:29 - Images in subfolders
14:03 - Folder with annotation csv
19:15 - All in single folder
24:53 - Ending