Upload README.md with huggingface_hub

README.md

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+---
+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},
+}
+```