Comments (6)
in train_meta.py
, add the following:
@@ -107,6 +107,7 @@ test_loader = torch.utils.data.DataLoader(
dataset.listDataset(testlist, shape=(init_width, init_height),
shuffle=False,
transform=transforms.Compose([
+ transforms.Resize(416),
transforms.ToTensor(),
]), train=False),
batch_size=batch_size, shuffle=False, **kwargs)
@@ -174,6 +175,7 @@ def train(epoch):
dataset.listDataset(trainlist, shape=(init_width, init_height),
shuffle=False,
transform=transforms.Compose([
+ transforms.Resize(416),
transforms.ToTensor(),
]),
train=True,
from fewshot_detection.
Thanks for the hint!
Does this affect performance significantly? Were you able to reproduce the results in the paper after resizing the input image? Also, why resize?
from fewshot_detection.
from fewshot_detection.
I see. Thank you!
Leaving this post open because the following questions have not been answered:
- Why do we need resizing?
- Were the results in the paper obtained with resizing to (416, 416)?
from fewshot_detection.
from fewshot_detection.
Thanks!
from fewshot_detection.
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