analyze_model.py

# analyze_model.py
import torch
from torchvision import models, transforms
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder

model = models.mobilenet_v2(weights=models.MobileNet_V2_Weights.IMAGENET1K_V1)
model.classifier[1] = torch.nn.Linear(1280, 18)  # 18 classes
model.load_state_dict(torch.load("custom_image_model.pth"))
model.eval()

transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

dataset = ImageFolder(root="categorized_images", transform=transform)
val_loader = DataLoader(dataset, batch_size=16, shuffle=False)

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

correct = 0
total = 0
with torch.no_grad():
    for images, labels in val_loader:
        images, labels = images.to(device), labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

accuracy = 100 * correct / total
print(f"✅ Model Accuracy: {accuracy:.2f}% on {total} images")