painting-style-classification

EfficientNet-B0 Fine-Tuned on Painting Style Classification 🎨

This is a fine-tuned image classification model based on torchvision.models.efficientnet_b0 initialized with pretrained weights from IMAGENET1K_V1. It has been fine-tuned to classify artworks into 9 different painting styles.

🧠 Model Details

• Base model: EfficientNet-B0
• Pretrained weights: IMAGENET1K_V1
• Fine-tuned on: keremberke/painting-style-classification
• Number of classes: 9
• Best validation accuracy: 60.15%
• Training epochs: 50

⚙️ Training Details

• Loss function: CrossEntropyLoss with label smoothing (label_smoothing=0.1)
• Optimizer: AdamW (lr=1e-4, weight_decay=0.01)
• Scheduler: ReduceLROnPlateau (mode='max', patience=5, factor=0.5)
• Layer freezing: All layers up to layer 100 were frozen during training

🚀 Usage

from torchvision import transforms
from PIL import Image

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],   
    ),
])

# Example usage:
img = Image.open("your_image.jpg").convert("RGB")
input_tensor = transform(img).unsqueeze(0) 
output = model(input_tensor)
pred = torch.argmax(output, dim=1)