pawlo2013/EarthLoc2

EarthLoc2 model

This is the EarthLoc2 model = DINOv2 base with SALAD aggregator out dim = 3072.

Trained on the original EarthLoc dataset (zooms 9,10,11) , in range -60,60 latitude, polar regions not supported.

Training included additional queries which were not part of the test/val sets

Achieves average R@10 = 90.6 on the original EarthLoc test and val sets (when retrieving against whole db as is).

5000 iterations with a batch size of 96, lr = 0.0001, only last block of Dinov2 + aggregator trainable.

To use the prediction of the model, see the FAISS index https://huggingface.co/datasets/pawlo2013/EarthLoc2_FAISS, 2021 database https://huggingface.co/datasets/pawlo2013/EarthLoc_2021_Database, and the inference space https://huggingface.co/spaces/pawlo2013/EarthLoc2.

See EarthLoc for more details about the training, data and use cases https://earthloc-and-earthmatch.github.io/

Model	Average R@1	Average R@10	Average R@100
EarthLoc	50.8	65.9	80.5
EarthLoc2	79.6	90.0	95.5

Wide world search. Results across evaluation sets when all of the images in the database from 2021 are encoded.
EarthLoc = (ResNet + MixVPR), EarthLoc2 = (DINOv2-B + SALAD-B + Query Data)

Loading and Inspecting the DINOv2 Feature Extractor Model

from model import DINOv2FeatureExtractor
import torch

# Set device
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'

# Path to the pretrained weights
MODEL_CHECKPOINT_PATH = './weights/best_model_95.6.torch'

# Initialize the model
model = DINOv2FeatureExtractor(
    model_type="vit_base_patch14_reg4_dinov2.lvd142m",
    num_of_layers_to_unfreeze=0,
    desc_dim=768,
    aggregator_type="SALAD",
)

print('Loading model ...')
# Load weights
model_state_dict = torch.load(MODEL_CHECKPOINT_PATH, map_location=DEVICE)
model.load_state_dict(model_state_dict)

# Move model to device and set to evaluation mode
model = model.to(DEVICE)
model.eval()
print('Model loaded.')

# Print model parameters info
num_params = sum(p.numel() for p in model.parameters())
num_trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f"Model total parameters: {num_params:,}")
print(f"Model trainable parameters: {num_trainable:,}")

# Print aggregator type
print(f"Aggregator type: {model.aggregator_type}")