Add image-segmentation task category and Github link (#3)

- Add image-segmentation task category and Github link (9a8e68df71dbdb192a9c892ae11f2c99f8e1c247)


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

README.md

@@ -1,21 +1,21 @@
 ---
-viewer: false
-license: bsd
 language:
 - en
+license: bsd
+size_categories:
+- 1K<n<10K
+pretty_name: arkit_labelmaker
+viewer: false
 tags:
 - 3D semantic segmentation
 - indoor 3D scene dataset
-pretty_name: arkit_labelmaker
-size_categories:
-- 1K<n<10K
+task_categories:
+- image-segmentation
 ---
 
 # ARKit Labelmaker: A New Scale for Indoor 3D Scene Understanding
 
-[[arxiv]](https://arxiv.org/abs/2410.13924)  [[website]](https://labelmaker.org/) [[checkpoints]](https://huggingface.co/labelmaker/PTv3-ARKit-LabelMaker)
+[[arxiv]](https://arxiv.org/abs/2410.13924)  [[website]](https://labelmaker.org/) [[checkpoints]](https://huggingface.co/labelmaker/PTv3-ARKit-LabelMaker) [[code]](https://github.com/cvg/LabelMaker)
 
 We complement ARKitScenes dataset with dense semantic annotations that are automatically generated at scale. This produces the first large-scale, real-world 3D dataset with dense semantic annotations.
-Training on this auto-generated data, we push forward the state-of-the-art performance on ScanNet and ScanNet200 with prevalent 3D semantic segmentation models.
-
-              
+Training on this auto-generated data, we push forward the state-of-the-art performance on ScanNet and ScanNet200 with prevalent 3D semantic segmentation models.