cifar10
Name: Sheng Cao (Scott)
EID: SC52753
E-Mail: scottcao (at) outlook.com
CIFAR-10 is a standard object recognition dataset with:
- 60,000 32x32 color images
- 10 object classes - 6000 images per class.
- 50,000 training images and 10,000 testing images.
Current state-of-the-art test accuracy on CIFAR-10 is above 95%.
This is my submission for Kaggle's CIFAR-10 Competition.
I modified a ResNet-34 architecture from PyTorch and trained for around 200 epochs.
The model achieved a test accuracy of 94.49% on Kaggle.