How to Train an Image Classification Model with CIFAR-10 Dataset using Ultralytics | Episode 65

In this video, we will explore the CIFAR-10 dataset. You will learn about its key features, how to use it for image classification, and view examples of training and prediction results using Google Colab. We will provide a comprehensive overview to enhance your understanding of this widely used dataset in the field of machine learning and computer vision.
Learn more ➡️ docs.ultralytics.com/datasets...
Key Moments ❤️
0:00 - Introduction
0:22 - CIFAR-10 Documentation Overview
1:41 - Dataset Sample Images and Annotations
2:24 - CIFAR-10 Image Classification Using Google Colab
6:18 - Training Results and Validation Metrics Overview
7:42 - CIFAR-10 Prediction on Test Set Using Google Colab
9:26 - Prediction Output Understanding for Image Classification Models
12:55 - Conclusion and Summary
Ultralytics ⚡ resources
- About Us - ultralytics.com/about
- Join Our Team - ultralytics.com/work
- Contact Us - ultralytics.com/contact
- Discord - / discord
- Ultralytics License - ultralytics.com/license
YOLOv8 🚀 resources
- GitHub - github.com/ultralytics/ultral...
- Docs - docs.ultralytics.com/

[@m033372]

It's crazy how the low res CIFAR images still work so well

[@Ultralytics]

Absolutely! The CIFAR datasets are indeed impressive, especially considering their low resolution. The diversity and structure of the CIFAR-100 dataset make it a fantastic resource for training robust image classification models. If you're interested in diving deeper, check out our documentation on CIFAR-100 here docs.ultralytics.com/datasets/classify/cifar100/. Happy experimenting! 🚀

[@Sasha-n2x]

Where can I find this "Untitled13.ipynb" notebook??

[@Ultralytics]

Hi! You can find the notebook used in the video on our GitHub repository. Check out the link in the video description or visit our GitHub page here: github.com/ultralytics/ultralytics. Happy coding! 🚀