Add pipeline tag, library name and link to code (#1)

- Add pipeline tag, library name and link to code (bd478b4b030264b55fac7d323a11c034cca0a733)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,10 +1,15 @@
 ---
 license: apache-2.0
+library_name: pytorch
+pipeline_tag: image-feature-extraction
 ---
 
 ## Checkpoints of ScaleLSD 
 
-There are two checkpoints of our model, [scalelsd-vitbase-v1-train-sa1b.pt](https://huggingface.co/cherubicxn/scalelsd/blob/main/scalelsd-vitbase-v1-train-sa1b.pt) and [scalelsd-vitbase-v2-train-sa1b.pt](https://huggingface.co/cherubicxn/scalelsd/blob/main/scalelsd-vitbase-v2-train-sa1b.pt).
+This repository contains the checkpoints as described in the paper [ScaleLSD: Scalable Deep Line Segment Detection Streamlined](https://huggingface.co/papers/2506.09369).
+For code, see https://github.com/ant-research/scalelsd.
+
+There are two checkpoints of our model, [scalelsd-vitbase-v1-train-sa1b.pt](https://huggingface.co/cherubicxn/scalelsd/blob/main/scalelsd-vitbase-v1-train-sa1b.pt) and [scalelsd-vitbase-v2-train-sa1b.pt](https://huggingface.co/cherubicxn/scalelsd/blob/main/scalelsd-vitbase-v2-train-sa1b.pt).\
 
 The main differences between v1 and v2 models are summarized as follow
 
@@ -13,4 +18,4 @@ The main differences between v1 and v2 models are summarized as follow
 | [scalelsd-vitbase-v1-train-sa1b.pt](https://huggingface.co/cherubicxn/scalelsd/blob/main/scalelsd-vitbase-v1-train-sa1b.pt)  | No    | ADAM |
 | [scalelsd-vitbase-v2-train-sa1b.pt](https://huggingface.co/cherubicxn/scalelsd/blob/main/scalelsd-vitbase-v2-train-sa1b.pt)  | Yes    | ADAMW |
 
-We recommend the users use the v2 model by default because it works much better than v1 (the model evaluated in our paper)
+We recommend the users use the v2 model by default because it works much better than v1 (the model evaluated in our paper)