how are you doing?
I'm playing around with CNN in FastAI.
My model with 2 millions parameters only has around 80% accuracy. I also tried with Data normalization, Batch normalization, Label smoothing, Mixup but the results are still capped at 80-81% (they did converged faster with those techniques).
I assumed bigger model would make better predictions so I increase the parameters (from 2 millions to 182 millions). However, the result is still at 82% after 40 epochs
imagenette = DataBlock(blocks = (ImageBlock, CategoryBlock),
get_items = get_image_files,
get_y = parent_label,
splitter = GrandparentSplitter(train_name='train', valid_name='val'),
item_tfms = Resize(460),
batch_tfms = aug_transforms(size=244)
)
dls = imagenette.dataloaders(path)
def conv_manual(ni, nf, ks=3, act=True):
conv = nn.Conv2d(ni, nf, stride=2, kernel_size=ks, padding = ks//2)
if act: conv = nn.Sequential(
conv,
nn.ReLU(),
#nn.AvgPool2d(2,stride=2, padding=1),
BatchNorm(nf)
)
return conv
test_cnn = sequential(
conv_manual(3,8, ks=5), #122x122
conv_manual(8,64), #61x61
conv_manual(64,100), #31x31
conv_manual(100,250), #16x16
conv_manual(250,800), #8x8
conv_manual(800,3000), #4x4
conv_manual(3000, 6000), #2x2
conv_manual(6000,10, act=False), #1x1
Flatten(),
)
test_model.fit_one_cycle(10, 0.00015)
I hope you could give me some thoughts about it
Thank you
