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# creating model.py
########## imports ############
import torch
import torch.nn as nn
from torchvision import models, transforms
###############################
def create_model():
weights = models.Swin_B_Weights.DEFAULT
transform = weights.transforms()
model = models.swin_b(weights = weights)
for param in model.parameters():
param.requires_grad = False
model.head = nn.Sequential(
# nn.Linear(in_features = 1024, out_features = 512, bias = True),
# nn.ReLU(),
# nn.Dropout(p = 0.3, inplace = True),
nn.Linear(in_features = 1024, out_features = 101, bias = True)
)
return model, transform
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