Update README.md

README.md

@@ -1,3 +1,83 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- behAIvNET/TinyAestheticNet
+---
+
+# TinyAestheticNet
+
+## 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
+
+![Performance](TinyAestheticNet_performance.png)
+
+## EXPLAINABILITY
+
+![XAI](TinyAestheticNet_xai.png)