High-dimensional Convolutional Networks for Geometric Pattern Recognition
Image Correspondences
Download YFCC100M Dataset
bash scripts/download_yfcc.sh /path/to/yfcc100m
Preprocess YFCC100M Dataset
SIFT
python -m scripts.gen_2d \
--source /path/to/yfcc100m \
--target /path/to/save/processed/dataset \
UCN
python -m scripts.gen_2d \
--source /path/to/yfcc100m \
--target /path/to/save/processed/dataset \
--feature ucn \
--onthefly \
--ucn_weight /path/to/pretrained/ucn/weight
Training Network
Train an image correspondence network.
bash scripts/train_2d.sh "-experiment1" \
"--data_dir_raw /path/to/raw/yfcc \
--data_dir_processed /path/to/processed/yfcc"
Testing on YFCC100M Dataset
python -m scripts.benchmark_yfcc \
--data_dir_raw /path/to/yfcc100m \
--data_dir_processed /path/to/processed/dataset \
--weights /path/to/checkpoint \
--out_dir /path/to/save/outputs \
--do_extract
Demo on YFCC100M Dataset
Following demo_2d script will download UCN and our best model(PyramidNetSCNoBlock) weights and test it on few pairs of images. The visualization output will be saved on './visualize' directory.
python demo_2d.py
Model Zoo
| Model | Dataset | Link |
|---|---|---|
| PyramidNetSCNoBlock | YFCC100MDatasetUCN | download |
| ResNetSC | YFCC100MDatasetExtracted | download |
| ResUNetINBN2G | YFCC100MDatasetExtracted | download |
| OANet | YFCC100MDatasetExtracted | download |
| LFGCNet | YFCC100MDatasetExtracted | download |