import torch import torchvision from torch import nn def create_effnetb2_model(num_classes: int = 3, seed: int = 42): effnetb2_weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT effnetb2_transforms = effnetb2_weights.transforms() effnetb2 = torchvision.models.efficientnet_b2(weights=effnetb2_weights) for param in effnetb2.parameters(): param.requires_grad = False torch.manual_seed(seed) effnetb2.classifier = nn.Sequential( nn.Dropout(p = 0.3, inplace = True), nn.Linear(in_features = 1408, out_features = num_classes) ) return effnetb2, effnetb2_transforms