Comments (5)
Close this issue if you have no further concern.
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Actually I never test MNIST, let me know if any issue exists. The transform for MNIST is a simple ToTensor
And note that the normalization parameters are embedded into model as a layer rather than dataset transform. So that all image tensors range in [0, 1]
trojanzoo/trojanvision/datasets/imageset.py
Lines 56 to 61 in e38f90b
trojanzoo/trojanvision/datasets/imageset.py
Lines 141 to 156 in e38f90b
from trojanzoo.
CUDA_VISIBLE_DEVICES=0 python examples/train.py --verbose 1 --color --epoch 600 --batch_size 96 --cutout --grad_clip 5.0 --lr 0.025 --lr_scheduler --save --dataset cifar10 --model resnet18_comp
And you will get ResNet18 (first convolutional layer compressed version) with 96.5% accuracy.
from trojanzoo.
ya so I was testing these models manually and the transform that worked for me is
transform.Compose([transforms.ToTensor(),transforms.Normalize([0.49139968, 0.48215827, 0.44653124],[0.24703233, 0.24348505, 0.26158768]))
To give the expected accuracy.
from trojanzoo.
I remembered that Without data augment such as random crop and cutout, the model accuracy won’t exceed 92%. But I could be wrong.
And if you use my model class and put normalization into the transform, you’d better set the model transform layer mean/std to be 0/1.
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