Welcome to the CIFAR 10 Image Classification repository! In this project, we aim to classify images from the CIFAR 10 dataset into 10 classes using deep learning techniques. The only file in this repository, CIFAR 10 Image Classification.ipynb, contains the code and explanations for the image classification process.
The CIFAR 10 dataset contains 60,000 32x32 color training images and 10,000 test images, with each image belonging to one of the following 10 classes:
- airplane
- automobile
- bird
- cat
- deer
- dog
- frog
- horse
- ship
- truck
In this project, we use a convolutional neural network (CNN) to classify the images in the CIFAR 10 dataset. The model is trained on the training set and evaluated on the test set. The results of the classification are reported in terms of accuracy and loss.
This project serves as an introduction to image classification and provides a foundation for further exploration and experimentation with deep learning techniques.
The code in the "CIFAR 10 Image Classification.ipynb" file is easy to follow and well-commented, making it a great starting point for anyone who is new to deep learning and wants to get started with image classification. So, dive in, play with the code, and get ready to classify some images!