README.md

Datasets

We provide bash script for downloading the datasets. Run the following script from the project root:

# optionally specific the data directory (default: 'data/')
export DATA_DIR=data

# Download BigEarthNet (~66 Gb, ~1h)
sh datasets/bigearthnet_download.sh

# Download ForestNet (3 Gb, ~5 min)
sh datasets/forestnet_download.sh

Models

You can download the model weights for Prithvi-100M from Hugging Face with the following commands.

mkdir weights
cd weights && wget https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M/resolve/main/Prithvi_100M.pt

The weights are saved at weights/Prithvi_100M.pt but you can also update the path in the config file configs/prithvi_vit_us.yaml.

The weights for the vanilla ViT with RGB channels are downloaded automatically.

Run experiments

You can save the embeddings of a dataset with:

# Save embeddings
python inference.py -c configs/prithvi_vit.yaml --dataset ForestNet --split val
python inference.py -c configs/prithvi_vit.yaml --dataset ForestNet --split test

If you want to save the embeddings of all evaluated models and dataset versions, you can run:

bash inference.sh

Evaluate all saved embeddings with given

# Run experiments
python experiments.py --match any --distance_function hamming --hash_method trivial --hash_length 32
# You can also combine multiple methods
python experiments.py --match any --distance_function hamming --hash_method trivial,lsh,none --hash_length 32,768

Speed experiments

You need a running Milvus instance for these experiments.

With saved BigEarthNet embeddings, run the experiments with:

python speed_test_milvus.py

If you want to run the experiments on another machine, connect to Milvus via ssh.

ssh <server> -L19530:localhost:19530

Thanks

We are grateful to the Remote sensing image retrieval project.