| --- |
| license: mit |
| tags: |
| - line-segment-detection |
| - deeplsd |
| - image-features |
| --- |
| |
| # DeepLSD — weight mirror |
|
|
| Unmodified mirror of the two checkpoints published for |
| [DeepLSD](https://github.com/cvg/DeepLSD) (CVPR 2023, Pautrat et al.), a hybrid line segment |
| detector: a network predicts a line distance and angle field, and a modified LSD reads the |
| segments off those predicted fields instead of off the image gradient. |
|
|
| Upstream ships no Hugging Face repo and serves these files from a university web server. This |
| repo exists so they can be fetched with `huggingface_hub` and loaded offline afterwards. |
|
|
| **Nothing here is retrained, converted or quantised** — the files are byte-identical to the |
| upstream artefacts listed below. (They are `torch.save` archives despite the `.tar` extension, |
| which is upstream's naming.) |
|
|
| ## Contents |
|
|
| | File | Bytes | SHA-256 | |
| |------|------:|---------| |
| | `deeplsd_md.tar` | 102,898,193 | `56e9bae263977caa289ae49802987a67d4f00f376ae7e542176c8ae0a6cc2083` | |
| | `deeplsd_wireframe.tar` | 102,898,321 | `6d0c1bc78f79eb9e564404e4fef797d12ab18f30ba596208cca16116c2429406` | |
|
|
| ## Which one to use |
|
|
| | File | Trained on / suited to | |
| |------|------------------------| |
| | `deeplsd_md.tar` | Trained on MegaDepth. The generic checkpoint: outdoor scenes and harder images (night, fog, blur). Upstream's own demo default. | |
| | `deeplsd_wireframe.tar` | Trained on the Wireframe dataset. Clean man-made scenes -- documents, floorplans, CAD exports, indoor rooms. | |
|
|
| ## Provenance |
|
|
| | File | Upstream source | |
| |------|-----------------| |
| | `deeplsd_md.tar` | `https://cvg-data.inf.ethz.ch/DeepLSD/deeplsd_md.tar` | |
| | `deeplsd_wireframe.tar` | `https://cvg-data.inf.ethz.ch/DeepLSD/deeplsd_wireframe.tar` | |
|
|
| Each file was verified before upload by loading it into DeepLSD's own network and requiring an |
| exact fit — zero missing and zero unexpected `state_dict` keys. |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| from huggingface_hub import hf_hub_download |
| |
| # DeepLSD ships no pip package; see the upstream repo for the model definition. |
| from deeplsd.models.deeplsd_inference import DeepLSD |
| |
| path = hf_hub_download("xboix/deeplsd", "deeplsd_md.tar") |
| net = DeepLSD({"detect_lines": True}) |
| net.load_state_dict(torch.load(path, map_location="cpu")["model"]) |
| net = net.eval() |
| ``` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @InProceedings{pautrat_suarez_2023_deeplsd, |
| title={DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients}, |
| author={Pautrat, R{\'e}mi and Barath, Daniel and Larsson, Viktor and Oswald, Martin R. and Pollefeys, Marc}, |
| booktitle={Computer Vision and Pattern Recognition (CVPR)}, |
| year={2023}, |
| } |
| ``` |
|
|
