Glue Factory is CVG's library for training and evaluating deep neural network that extract and match local visual feature. It enables you to:
- Reproduce the training of state-of-the-art models for point and line matching, like LightGlue and GlueStick (ICCV 2023)
- Train these models on multiple datasets using your own local features or lines
- Evaluate feature extractors or matchers on standard benchmarks like HPatches or MegaDepth-1500
Point and line matching with LightGlue and GlueStick.
Glue Factory runs with Python 3 and PyTorch. The following installs the library and its basic dependencies:
git clone https://github.com/cvg/glue-factory
cd glue-factory
python3 -m pip install -e . # editable modeSome advanced features might require installing the full set of dependencies:
python3 -m pip install -e .[extra]All models and datasets in gluefactory have auto-downloaders, so you can get started right away!
The code and trained models in Glue Factory are released with an Apache-2.0 license. This includes LightGlue trained with an open version of SuperPoint. Third-party models that are not compatible with this license, such as SuperPoint (original) and SuperGlue, are provided in gluefactory_nonfree, where each model might follow its own, restrictive license.
Running the evaluation commands automatically downloads the dataset, by default to the directory data/. You will need about 1.8 GB of free disk space.
[Evaluating LightGlue]
To evaluate the pre-trained SuperPoint+LightGlue model on HPatches, run:
python -m gluefactory.eval.hpatches --conf superpoint+lightglue-official --overwriteYou should expect the following results
{'H_error_dlt@1px': 0.3515,
'H_error_dlt@3px': 0.6723,
'H_error_dlt@5px': 0.7756,
'H_error_ransac@1px': 0.3428,
'H_error_ransac@3px': 0.5763,
'H_error_ransac@5px': 0.6943,
'mnum_keypoints': 1024.0,
'mnum_matches': 560.756,
'mprec@1px': 0.337,
'mprec@3px': 0.89,
'mransac_inl': 130.081,
'mransac_inl%': 0.217,
'ransac_mAA': 0.5378}
The default robust estimator is opencv, but we strongly recommend to use poselib instead:
python -m gluefactory.eval.hpatches --conf superpoint+lightglue-official --overwrite \
eval.estimator=poselib eval.ransac_th=-1Setting eval.ransac_th=-1 auto-tunes the RANSAC inlier threshold by running the evaluation with a range of thresholds and reports results for the optimal value.
Here are the results as Area Under the Curve (AUC) of the homography error at 1/3/5 pixels:
| Methods | DLT | OpenCV | PoseLib |
|---|---|---|---|
| SuperPoint + SuperGlue | 32.1 / 65.0 / 75.7 | 32.9 / 55.7 / 68.0 | 37.0 / 68.2 / 78.7 |
| SuperPoint + LightGlue | 35.1 / 67.2 / 77.6 | 34.2 / 57.9 / 69.9 | 37.1 / 67.4 / 77.8 |
[Evaluating GlueStick]
To evaluate GlueStick on HPatches, run:
python -m gluefactory.eval.hpatches --conf gluefactory/configs/superpoint+lsd+gluestick.yaml --overwriteYou should expect the following results
{"mprec@1px": 0.245,
"mprec@3px": 0.838,
"mnum_matches": 1290.5,
"mnum_keypoints": 2287.5,
"mH_error_dlt": null,
"H_error_dlt@1px": 0.3355,
"H_error_dlt@3px": 0.6637,
"H_error_dlt@5px": 0.7713,
"H_error_ransac@1px": 0.3915,
"H_error_ransac@3px": 0.6972,
"H_error_ransac@5px": 0.7955,
"H_error_ransac_mAA": 0.62806,
"mH_error_ransac": null}
Since we use points and lines to solve for the homography, we use a different robust estimator here: Hest. Here are the results as Area Under the Curve (AUC) of the homography error at 1/3/5 pixels:
| Methods | DLT | Hest |
|---|---|---|
| SP + LSD + GlueStick | 33.6 / 66.4 / 77.1 | 39.2 / 69.7 / 79.6 |
Running the evaluation commands automatically downloads the dataset, which takes about 1.5 GB of disk space.
[Evaluating LightGlue]
To evaluate the pre-trained SuperPoint+LightGlue model on MegaDepth-1500, run:
python -m gluefactory.eval.megadepth1500 --conf superpoint+lightglue-official
# or the adaptive variant
python -m gluefactory.eval.megadepth1500 --conf superpoint+lightglue-official \
model.matcher.{depth_confidence=0.95,width_confidence=0.95}The first command should print the following results
{'mepi_prec@1e-3': 0.795,
'mepi_prec@1e-4': 0.15,
'mepi_prec@5e-4': 0.567,
'mnum_keypoints': 2048.0,
'mnum_matches': 613.287,
'mransac_inl': 280.518,
'mransac_inl%': 0.442,
'rel_pose_error@10°': 0.681,
'rel_pose_error@20°': 0.8065,
'rel_pose_error@5°': 0.5102,
'ransac_mAA': 0.6659}
To use the PoseLib estimator:
python -m gluefactory.eval.megadepth1500 --conf superpoint+lightglue-official \
eval.estimator=poselib eval.ransac_th=2.0[Evaluating GlueStick]
To evaluate the pre-trained SuperPoint+GlueStick model on MegaDepth-1500, run:
python -m gluefactory.eval.megadepth1500 --conf gluefactory/configs/superpoint+lsd+gluestick.yamlHere are the results as Area Under the Curve (AUC) of the pose error at 5/10/20 degrees:
| Methods | pycolmap | OpenCV | PoseLib |
|---|---|---|---|
| SuperPoint + SuperGlue | 54.4 / 70.4 / 82.4 | 48.7 / 65.6 / 79.0 | 64.8 / 77.9 / 87.0 |
| SuperPoint + LightGlue | 56.7 / 72.4 / 83.7 | 51.0 / 68.1 / 80.7 | 66.8 / 79.3 / 87.9 |
| SuperPoint + GlueStick | 53.2 / 69.8 / 81.9 | 46.3 / 64.2 / 78.1 | 64.4 / 77.5 / 86.5 |
The dataset will be auto-downloaded if it is not found on disk, and will need about 6 GB of free disk space.
[Evaluating GlueStick]
To evaluate GlueStick on ETH3D, run:
python -m gluefactory.eval.eth3d --conf gluefactory/configs/superpoint+lsd+gluestick.yamlYou should expect the following results
AP: 77.92
AP_lines: 69.22
Coming soon!
Coming soon!
python -m gluefactory.eval.inspect hpatches superpoint+lightglue-officialClick on a point to visualize matches on this pair.
To compare multiple methods on a dataset:
python -m gluefactory.eval.inspect hpatches superpoint+lightglue-official superpoint+superglue-officialAll current benchmarks are supported by the viewer.
Detailed evaluation instructions can be found here.
We generally follow a two-stage training:
- Pre-train on a large dataset of synthetic homographies applied to internet images. We use the 1M-image distractor set of the Oxford-Paris retrieval dataset. It requires about 450 GB of disk space.
- Fine-tune on the MegaDepth dataset, which is based on PhotoTourism pictures of popular landmarks around the world. It exhibits more complex and realistic appearance and viewpoint changes. It requires about 420 GB of disk space.
All training commands automatically download the datasets.
[Training LightGlue]
We show how to train LightGlue with SuperPoint open. We first pre-train LightGlue on the homography dataset:
python -m gluefactory.train sp+lg_homography \ # experiment name
--conf gluefactory/configs/superpoint-open+lightglue_homography.yamlFeel free to use any other experiment name. By default the checkpoints are written to outputs/training/. The default batch size of 128 corresponds to the results reported in the paper and requires 2x 3090 GPUs with 24GB of VRAM each as well as PyTorch >= 2.0 (FlashAttention).
Configurations are managed by OmegaConf so any entry can be overridden from the command line.
If you have PyTorch < 2.0 or weaker GPUs, you may thus need to reduce the batch size via:
python -m gluefactory.train sp+lg_homography \
--conf gluefactory/configs/superpoint-open+lightglue_homography.yaml \
data.batch_size=32 # for 1x 1080 GPUBe aware that this can impact the overall performance. You might need to adjust the learning rate accordingly.
We then fine-tune the model on the MegaDepth dataset:
python -m gluefactory.train sp+lg_megadepth \
--conf gluefactory/configs/superpoint-open+lightglue_megadepth.yaml \
train.load_experiment=sp+lg_homographyHere the default batch size is 32. To speed up training on MegaDepth, we suggest to cache the local features before training (requires around 150 GB of disk space):
# extract features
python -m gluefactory.scripts.export_megadepth --method sp_open --num_workers 8
# run training with cached features
python -m gluefactory.train sp+lg_megadepth \
--conf gluefactory/configs/superpoint-open+lightglue_megadepth.yaml \
train.load_experiment=sp+lg_homography \
data.load_features.do=TrueThe model can then be evaluated using its experiment name:
python -m gluefactory.eval.megadepth1500 --checkpoint sp+lg_megadepthYou can also run all benchmarks after each training epoch with the option --run_benchmarks.
[Training GlueStick]
We first pre-train GlueStick on the homography dataset:
python -m gluefactory.train gluestick_H --conf gluefactory/configs/superpoint+lsd+gluestick-homography.yaml --distributedFeel free to use any other experiment name. Configurations are managed by OmegaConf so any entry can be overridden from the command line.
We then fine-tune the model on the MegaDepth dataset:
python -m gluefactory.train gluestick_MD --conf gluefactory/configs/superpoint+lsd+gluestick-megadepth.yaml --distributedNote that we used the training splits train_scenes.txt and valid_scenes.txt to train the original model, which contains some overlap with the IMC challenge. The new default splits are now train_scenes_clean.txt and valid_scenes_clean.txt, without this overlap.
Glue Factory supports training and evaluating the following deep matchers:
| Model | Training? | Evaluation? |
|---|---|---|
| LightGlue | ✅ | ✅ |
| GlueStick | ✅ | ✅ |
| SuperGlue | ✅ | ✅ |
| LoFTR | ❌ | ✅ |
Using the following local feature extractors:
| Model | LightGlue config |
|---|---|
| SuperPoint (open) | superpoint-open+lightglue_{homography,megadepth}.yaml |
| SuperPoint (official) | ❌ TODO |
| SIFT (via pycolmap) | sift+lightglue_{homography,megadepth}.yaml |
| ALIKED | aliked+lightglue_{homography,megadepth}.yaml |
| DISK | ❌ TODO |
| Key.Net + HardNet | ❌ TODO |
- More baselines (LoFTR, ASpanFormer, MatchFormer, SGMNet, DKM, RoMa)
- Training deep detectors and descriptors like SuperPoint
- IMC evaluations
- Better documentation
Please consider citing the following papers if you found this library useful:
@InProceedings{lindenberger_2023_lightglue,
title = {{LightGlue: Local Feature Matching at Light Speed}},
author = {Philipp Lindenberger and
Paul-Edouard Sarlin and
Marc Pollefeys},
booktitle = {International Conference on Computer Vision (ICCV)},
year = {2023}
}@InProceedings{pautrat_suarez_2023_gluestick,
title = {{GlueStick: Robust Image Matching by Sticking Points and Lines Together}},
author = {R{\'e}mi Pautrat* and
Iago Su{\'a}rez* and
Yifan Yu and
Marc Pollefeys and
Viktor Larsson},
booktitle = {International Conference on Computer Vision (ICCV)},
year = {2023}
}