Pytorch ResNet18 low performance with CIFAR100 Dataset
When using PyTorch’s provided ResNet18, the performance on the CIFAR-100 dataset is significantly lower than expected—around 30-40% accuracy, which is much lower than its reported benchmark. Why does this happen? PyTorch’s ResNet18 is originally designed for the ImageNet dataset, where images are typically resized to 224×224 or 256×256. The model architecture is optimized for these larger images, particularly in the first convolutional layer, which uses a large kernel size: Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) Since CIFAR-100…