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README.md

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+---
+license: cc-by-nc-4.0
+library_name: scikit-learn
+pipeline_tag: image-classification
+tags:
+- art
+- art-history
+- clip
+- embeddings
+- scikit-learn
+- image-classification
+---
+
+# Art Movement Classifier with CLIP Embeddings
+
+This model classifies paintings into eight art movements using frozen CLIP image embeddings and a trained scikit-learn classifier.
+
+## Lab Framing
+
+**Issue:** Art movement classification is difficult because visual movements overlap in subject matter, period, materials, and artist-specific style.
+
+**Challenge addressed with embeddings:** Instead of training a vision model from scratch on a small dataset, the project uses pretrained CLIP image embeddings as a compact visual representation, then trains a domain-specific classifier on top of those embeddings.
+
+**Embedding type:** `openai/clip-vit-base-patch32` image embeddings.
+
+**Classifier:** MLP classifier trained on CLIP embeddings.
+
+## Classes
+
+- Renaissance
+- Baroque
+- Impressionism
+- Expressionism
+- Cubism
+- Abstract
+- Surrealism
+- Pop Art
+
+## Results
+
+Held-out test accuracy: **0.8093**
+
+Cross-validation accuracy: **0.8138 ± 0.0173**
+
+Per-class F1:
+
+| Class | F1 |
+|---|---:|
+| Abstract | 0.727 |
+| Baroque | 0.860 |
+| Cubism | 0.774 |
+| Expressionism | 0.758 |
+| Impressionism | 0.906 |
+| Pop Art | 0.761 |
+| Renaissance | 0.904 |
+| Surrealism | 0.684 |
+
+## Files
+
+- `classifier.joblib`: trained scikit-learn classifier
+- `label_encoder.joblib`: label encoder for class ids
+- `config.json`: embedding/model configuration
+- `evaluation_metrics.json`: held-out and cross-validation metrics
+- `confusion_matrix.png`: normalized confusion matrix
+- `wolfflin_pca_clip.png`: Wölfflin-inspired PCA analysis
+- `arnheim_axes.npz`: Arnheim perceptual axis vectors used by the demo
+
+## Usage
+
+The model is intended to be used through the accompanying Gradio Space. The app loads CLIP, embeds an uploaded image, and applies the trained classifier.
+
+## Limitations
+
+- The model can confuse visually similar old-master religious paintings, especially Renaissance vs Baroque.
+- Predictions reflect visual similarity in the training distribution, not definitive art-historical attribution.
+- The Wölfflin tab in the demo is a theoretical profile of the predicted class, not a direct pixel-level measurement.
+- Arnheim perceptual scores are anchor-based projections in CLIP space and should be interpreted as exploratory.