Improve dataset card: add metadata, links, and usage snippet

Hi! I'm Niels, part of the community science team at Hugging Face. I've updated this dataset card to include:
- Metadata for `task_categories` and descriptive `tags`.
- Links to the project page, paper, and GitHub repository for better visibility.
- A sample usage section with a code snippet from the official repository.
- The BibTeX citation for researchers to cite your work.

README.md

@@ -1,6 +1,102 @@
 ---
 license: cc-by-4.0
+task_categories:
+- other
+tags:
+- 3d
+- computer-vision
+- orientation-estimation
 ---
-This Dataset contains the training data for the paper [Orient Anything V2: Unifying Orientation and Rotation Understanding](https://huggingface.co/papers/2601.05573).
 
-Code: https://github.com/SpatialVision/Orient-Anything-V2
+# Orient Anything V2 Dataset
+
+[**Project Page**](https://orient-anythingv2.github.io/) | [**Paper**](https://huggingface.co/papers/2601.05573) | [**GitHub**](https://github.com/SpatialVision/Orient-Anything-V2)
+
+**Orient Anything V2** is an enhanced foundation model for unified understanding of object 3D orientation and rotation from single or paired images. This repository contains the training data (final rendering data) used for the model.
+
+## Sample Usage
+
+Below is a snippet to run inference using the model and data logic, as found in the [official GitHub repository](https://github.com/SpatialVision/Orient-Anything-V2):
+
+```python
+import numpy as np
+from PIL import Image
+import torch
+import tempfile
+import os
+
+from paths import *
+from vision_tower import VGGT_OriAny_Ref
+from inference import *
+from app_utils import *
+
+mark_dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] >= 8 else torch.float16
+# device = 'cuda:0'
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+if os.path.exists(LOCAL_CKPT_PATH):
+    ckpt_path = LOCAL_CKPT_PATH
+else:
+    from huggingface_hub import hf_hub_download
+    ckpt_path = hf_hub_download(repo_id="Viglong/Orient-Anything-V2", filename=HF_CKPT_PATH, repo_type="model", cache_dir='./', resume_download=True)
+
+model = VGGT_OriAny_Ref(out_dim=900, dtype=mark_dtype, nopretrain=True)
+model.load_state_dict(torch.load(ckpt_path, map_location='cpu'))
+model.eval()
+model = model.to(device)
+print('Model loaded.')
+
+@torch.no_grad()
+def run_inference(pil_ref, pil_tgt=None, do_rm_bkg=True):
+    if pil_tgt is not None:
+        if do_rm_bkg:
+            pil_ref = background_preprocess(pil_ref, True)
+            pil_tgt = background_preprocess(pil_tgt, True)
+    else:
+        if do_rm_bkg:
+            pil_ref = background_preprocess(pil_ref, True)
+
+    try:
+        ans_dict = inf_single_case(model, pil_ref, pil_tgt)
+    except Exception as e:
+        print("Inference error:", e)
+        raise gr.Error(f"Inference failed: {str(e)}")
+
+    def safe_float(val, default=0.0):
+        try:
+            return float(val)
+        except:
+            return float(default)
+
+    az = safe_float(ans_dict.get('ref_az_pred', 0))
+    el = safe_float(ans_dict.get('ref_el_pred', 0))
+    ro = safe_float(ans_dict.get('ref_ro_pred', 0))
+    alpha = int(ans_dict.get('ref_alpha_pred', 1))
+
+    if pil_tgt is not None:
+      rel_az = safe_float(ans_dict.get('rel_az_pred', 0))
+      rel_el = safe_float(ans_dict.get('rel_el_pred', 0))
+      rel_ro = safe_float(ans_dict.get('rel_ro_pred', 0))
+
+      print("Relative Pose: Azi",rel_az,"Ele",rel_el,"Rot",rel_ro)
+
+image_ref_path = 'assets/examples/F35-0.jpg'
+image_tgt_path = 'assets/examples/F35-1.jpg' # optional
+
+image_ref = Image.open(image_ref_path).convert('RGB')
+image_tgt = Image.open(image_tgt_path).convert('RGB')
+
+run_inference(image_ref, image_tgt, True)
+```
+
+## Citation
+
+If you find this project useful, please consider citing:
+
+```bibtex
+@inproceedings{wangorient,
+  title={Orient Anything V2: Unifying Orientation and Rotation Understanding},
+  author={Wang, Zehan and Zhang, Ziang and Xu, Jiayang and Wang, Jialei and Pang, Tianyu and Du, Chao and Zhao, Hengshuang and Zhao, Zhou},
+  booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems}
+}
+```