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license: apache-2.0
datasets:
- behAIvNET/TinyAestheticNet
---
# TinyAestheticNet
A tiny aesthetic assessment model featuring only **16,581 trainable parameters** built on top of a frozen CLIP backbone, achieving an impressive **1.29 MAE** on a 1-10 scoring scale.
## Data
To measure creativity in paintings, this study utilizes a dataset consisting of 878 open-access modern art paintings. Each artwork was rigorously evaluated by 3 fine arts experts specializing in painting. The experts reached a consensus to score the artworks on a scale of 1 to 10 across 5 distinct criteria:
* **K1:** Originality
* **K2:** Aesthetics
* **K3:** Design Principles and Elements
* **K4:** Technique Used
* **K5:** Unity and Wholeness
The dataset features a well-balanced distribution, with an almost equal representation of scores across the 1 to 10 scale for each criterion.
## Model
```bash
pip install torch torchvision git+https://github.com/openai/CLIP.git huggingface_hub
```
```python
import torch
import torch.nn as nn
from PIL import Image
import clip
from huggingface_hub import hf_hub_download
class CLIPMLPScorer(nn.Module):
def __init__(self, clip_model, num_factors=5, dropout=0.4):
super().__init__()
self.clip_model = clip_model
for p in self.clip_model.parameters():
p.requires_grad = False
self.head = nn.Sequential(
nn.Linear(512, 32),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(32, num_factors),
nn.Sigmoid(),
)
def forward(self, x):
features = self.clip_model.encode_image(x).float()
x = self.head(features)
x = x * 9 + 1
return x
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
clip_model, preprocess = clip.load("ViT-B/32", device=device)
repo_id = "behAIvNET/TinyAestheticNet"
model_path = hf_hub_download(repo_id=repo_id, filename="TinyAestheticNet.pt")
model = CLIPMLPScorer(clip_model).to(device)
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()
image_path = "test_artwork.jpg"
img = preprocess(Image.open(image_path)).unsqueeze(0).to(device)
with torch.no_grad():
scores = model(img)[0].cpu().numpy()
criteria = ["K1", "K2", "K3", "K4", "K5"]
print("Artwork Scores:")
for c, s in zip(criteria, scores):
print(f"{c}: {s:.2f}/10")
```
## Performance
## Explainability
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