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README.md

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-license: mit
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+---
+license: mit
+library_name: keras
+tags:
+- image-classification
+- multi-task-learning
+- art
+- painting-classification
+- mobilenet-v2
+datasets:
+- huggan/wikiart
+metrics:
+- accuracy
+- top-5-accuracy
+---
+
+# WikiArt Multi-Task Painting Classifier
+
+A multi-task deep learning model for classifying paintings by **artist**, **genre**, and **style** simultaneously.
+
+## Model Description
+
+This model performs three classification tasks on painting images:
+- **Artist Classification**: 129 artists (Claude Monet, Van Gogh, Picasso, Da Vinci, etc.)
+- **Genre Classification**: 11 genres (portrait, landscape, abstract painting, etc.)
+- **Style Classification**: 27 art styles (Impressionism, Cubism, Renaissance, Baroque, etc.)
+
+## Model Architecture
+
+- **Base Model**: MobileNetV2 (pre-trained on ImageNet)
+- **Framework**: TensorFlow/Keras
+- **Input**: 224×224 RGB images
+- **Approach**: Multi-head architecture with shared convolutional base
+- **Total Parameters**: ~3.5M (approximate)
+
+## Training Details
+
+### Dataset
+- **Source**: [WikiArt dataset](https://huggingface.co/datasets/huggan/wikiart)
+- **Total Images**: 84,440 paintings
+- **Split**: 75% training, 25% validation
+
+### Training Procedure
+- **Preprocessing**: MobileNetV2 preprocessing (normalization)
+- **Augmentation**: Random horizontal flip, rotation (±5°), zoom (±10%)
+- **Optimizer**: Adam (1e-3 for frozen, 2e-4 for fine-tuning)
+- **Loss**: Sparse categorical cross-entropy (for all three tasks)
+- **Training Stages**:
+  1. Frozen backbone (2 epochs)
+  2. Full fine-tuning (10 epochs)
+
+### Evaluation Metrics
+- Top-1 Accuracy (all tasks)
+- Top-5 Accuracy (artist and style)
+
+## How to Use
+
+### Load Model
+