| # Aesthetic Scorer | |
| This model predicts 7 different aesthetic metrics for images: | |
| - Overall aesthetic score | |
| - Technical quality score | |
| - Composition score | |
| - Lighting score | |
| - Color harmony score | |
| - Depth of field score | |
| - Content score | |
| ## Model Details | |
| - Based on CLIP ViT-B/32 visual encoder | |
| - Fine-tuned on the PARA dataset | |
| - Returns scores between 0-5 for each aesthetic dimension | |
| ## Usage | |
| ```python | |
| from transformers import CLIPProcessor | |
| from aesthetic_scorer import AestheticScorer | |
| import torch | |
| from PIL import Image | |
| # Load the model | |
| processor = CLIPProcessor.from_pretrained("YOUR_USERNAME/aesthetic-scorer") | |
| model = torch.load("YOUR_USERNAME/aesthetic-scorer/model.pt") | |
| # Process an image | |
| image = Image.open("your_image.jpg") | |
| inputs = processor(images=image, return_tensors="pt")["pixel_values"] | |
| # Get scores | |
| with torch.no_grad(): | |
| scores = model(inputs) | |
| # Print results | |
| aesthetic_categories = ["Overall", "Quality", "Composition", "Lighting", "Color", "Depth of Field", "Content"] | |
| for category, score in zip(aesthetic_categories, scores): | |
| print(f"{category}: {score.item():.2f}/10") | |
| ``` | |