2022/01/05: Downloading links for the pre-trained models have been updated. Sorry for the wait.

Context Embeddings for Art Classification

Pytorch code for the classification part of our ICMR 2019 paper Context-Aware Embeddings for Automatic Art Analysis. For the retrieval part, check this other repository.

Setup

1. Download dataset from here.

2. Clone the repository:

   git clone https://github.com/noagarcia/context-art-classification.git

3. Install dependencies:

   • Python 2.7
   • pytorch (conda install pytorch=0.4.1 cuda90 -c pytorch)
   • torchvision (conda install torchvision)
   • visdom (check tutorial here)
   • pandas (conda install -c anaconda pandas)
   • gensim (conda install -c anaconda gensim)

4. For the KGM model, download the pre-computed graph embeddings from here, and save the file into the Data/ directory.

Train

• To train MTL multi-classifier run:

  python main.py --mode train --model mtl --dir_dataset $semart

• To train KGM classifier run:

  python main.py --mode train --model kgm --att $attribute --dir_dataset $semart

Where $semart is the path to SemArt dataset and $attribute is the classifier type (i.e. type, school, time, or author).

Test

• To test MTL multi-classifier run:

  python main.py --mode test --model mtl --dir_dataset $semart

• To test KGM classifier run:

  python main.py --mode test --model kgm --att $attribute --dir_dataset $semart --model_path $model-file

Where $semart is the path to SemArt dataset, $attribute is the classifier type (i.e. type, school, time, or author), and $model-file is the path to the trained model.

You can download our pre-trained models from:

• MTL
• KGM Type
• KGM School
• KGM Timeframe
• KGM Author

Results

Classification results on SemArt:

Model	Type	School	Timeframe	Author
VGG16 pre-trained	0.706	0.502	0.418	0.482
ResNet50 pre-trained	0.726	0.557	0.456	0.500
ResNet152 pre-trained	0.740	0.540	0.454	0.489
VGG16 fine-tuned	0.768	0.616	0.559	0.520
ResNet50 fine-tuned	0.765	0.655	0.604	0.515
ResNet152 fine-tuned	0.790	0.653	0.598	0.573
ResNet50+Attributes	0.785	0.667	0.599	0.561
ResNet50+Captions	0.799	0.649	0.598	0.607
MTL context-aware	0.791	0.691	0.632	0.603
KGM context-aware	0.815	0.671	0.613	0.615

Examples

Paintings with the highest scores for each class:

Citation

@InProceedings{Garcia2017Context,
   author    = {Noa Garcia and Benjamin Renoust and Yuta Nakashima},
   title     = {Context-Aware Embeddings for Automatic Art Analysis},
   booktitle = {Proceedings of the ACM International Conference on Multimedia Retrieval},
   year      = {2019},
}