First, download the model checkpoint for ViT-L here

Use sample_imgs_and_to_csv.py to create a csv file for the image dataset:

python sample_imgs_and_to_csv.py <IMAGES DIRECTORY PATH>

Then generate embeddings for the images:

python main_sim.py --dataset artbreeder -a vit_large --pt_style csd --feattype normal --world-size 1 --dist-url tcp://localhost:6001 -b 128 -j 8 --embed_dir ./embeddings --data-dir <IMAGE DIRECTORY PATH> --model_path <PATH TO VIT CHECKPOINT>

Then, either use the kmeans model to assign cluster labels or fit a kmeans model using clustering.py which will save the model in a .pkl file. The embedding directory should be something like ./embeddings/csd_vit_large_artbreeder_normal/1/database/:

python predict_clusters.py <EMBEDDING DIRECTORY> kmeans_model.pkl

This will save the labels into a txt file titled predicted_cluster_labels.txt

To run recommendation, use the recommendation.py file and replace the path with the path to the embeddings you want to test.

python recommendation.py <TEST EMBEDDING DIRECTORY> kmeans_model.pkl