---
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