Hi guys. My code is simply a “variation” of this code here: https://github.com/leimao/PyTorch-Quantization-Aware-Training/blob/main/cifar.py 3 with the difference that I’m using MNIST dataset instead of CIFAR-10 and a different model (AlexNet). I don’t know why but sometimes accuracy is very high (starting from almost 91%) while sometimes it’s simply just very low (11%). If you’re asking i’ve changed the train_transform and test_transform according to the MNIST dataset and set everything accordingly to this. Please help me i’m freaking out