painting-classifier-keras-v1

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
• 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

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