evaluate.py

import torch
from torchvision import datasets, transforms
import timm
from torch.utils.data import DataLoader
from sklearn.metrics import classification_report

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

test_data = datasets.ImageFolder("dataset/test", transform=transform)
test_loader = DataLoader(test_data, batch_size=32)

model = timm.create_model('efficientnet_b3', pretrained=False, num_classes=2)
model.load_state_dict(torch.load("model.pth", map_location=device, weights_only=True))
model = model.to(device)
model.eval()

y_true = []
y_pred = []

with torch.no_grad():
    for images, labels in test_loader:
        images = images.to(device)
        outputs = model(images)
        _, preds = torch.max(outputs, 1)

        y_true.extend(labels.numpy())
        y_pred.extend(preds.cpu().numpy())

print(classification_report(y_true, y_pred))