Add task category, update license and add project links

Hi, I'm Niels from the Hugging Face community science team! This PR improves the dataset card for KubriCount by:
- Adding the `object-detection` task category.
- Updating the license to `apache-2.0` based on the GitHub repository.
- Adding links to the project page and code repository at the top of the README.
- Ensuring the paper is linked within the Markdown content.

README.md

@@ -1,6 +1,8 @@
 ---
-license: other
+license: apache-2.0
 pretty_name: KubriCount
+task_categories:
+- object-detection
 tags:
 - image
 - synthetic
@@ -13,6 +15,8 @@ tags:
 
 # KubriCount
 
+[Project Page](https://verg-avesta.github.io/KubriCount/) | [Paper](https://arxiv.org/abs/2605.10887) | [Code](https://github.com/Verg-Avesta/KubriCount)
+
 KubriCount is a large-scale synthetic benchmark for **multi-grained visual counting**, built for the research project **Count Anything at Any Granularity**.
 
 The dataset targets open-world counting settings where the intended counting granularity must be explicit. A query may ask for a specific identity, an attribute variant, a category, an instance type, or a broader concept. KubriCount provides controlled distractors and dense instance-level supervision for training and evaluation.
@@ -109,8 +113,6 @@ The tar shards in this release contain only scenes that passed the automatic qua
     └── extracted_metadata.json
 ```
 
-The release intentionally does **not** include `metadata/dataset_stats.json` or per-split `vlm_filter_results.json` files.
-
 ## Files Inside Each Scene
 
 The image folders are stored inside tar shards. Each tar preserves the split/level/timestamp/scene structure:
@@ -183,51 +185,6 @@ A typical annotation item is:
 }
 ```
 
-Field meanings:
-
-- `image_id`: relative path to the edited image after shard extraction.
-- `count`: number of target objects.
-- `category`: target category or target phrase.
-- `box_examples_coordinates`: target-object 2D boxes represented by four corner points.
-- `points`: target-object center points.
-- `H`, `W`: image height and width.
-- `metadata.level`: counting granularity level.
-- `metadata.split`: dataset split.
-- `negative_category`: distractor category or phrase, when applicable.
-- `negative_count`: number of distractor objects.
-- `negative_box_examples_coordinates`: distractor-object 2D boxes.
-- `negative_points`: distractor-object center points.
-
-## Manifest Format
-
-Each line in `metadata/all_pass_scenes.jsonl` describes one released scene and where it is stored:
-
-```json
-{
-  "split": "testA",
-  "scene": "level1/20260205_132725/scene_0001",
-  "path_in_dataset": "testA/level1/20260205_132725/scene_0001",
-  "shard": "shards/testA/testA-000000.tar",
-  "num_files": 4,
-  "files": [
-    {
-      "path": "testA/level1/20260205_132725/scene_0001/edited_00000.png",
-      "name": "edited_00000.png",
-      "size_bytes": 1562567
-    }
-  ]
-}
-```
-
-Important fields:
-
-- `split`: dataset split.
-- `scene`: scene path relative to the split folder.
-- `path_in_dataset`: scene path after extraction.
-- `shard`: tar shard containing this scene.
-- `num_files`: number of files in this scene.
-- `files`: files stored for this scene.
-
 ## Download
 
 ```python
@@ -292,23 +249,6 @@ for split in splits:
 print(f"Restored dataset to: {restore_dir}")
 ```
 
-After extraction:
-
-```text
-KubriCount_restored/
-├── train/
-│   ├── extracted_metadata.json
-│   └── level1/
-├── testA/
-│   ├── extracted_metadata.json
-│   └── level1/
-├── testB/
-│   ├── extracted_metadata.json
-│   └── level1/
-├── merged_train_metadata.json
-└── merged_test_metadata.json
-```
-
 ## Read Images Directly From Tar Shards
 
 ```python
@@ -327,18 +267,6 @@ for tar_path in sorted((repo_dir / "shards").glob("*/*.tar")):
                 break
 ```
 
-To find the shard for a specific scene, use `metadata/all_pass_scenes.jsonl`.
-
-## Companion Code
-
-The generation pipeline is released separately at [Verg-Avesta/KubriCount](https://github.com/Verg-Avesta/KubriCount).
-
-That repository contains the Kubric-based rendering code, asset preprocessing utilities, image-editing scripts, and VLM-filtering scripts used to construct KubriCount. It is only needed if you want to reproduce or extend the data generation pipeline.
-
-## Paper
-
-The paper is available at [arXiv](https://arxiv.org/abs/2605.10887).
-
 ## Citation
 
 If you find this dataset useful, please cite:
@@ -358,4 +286,4 @@ KubriCount builds on the [Kubric](https://github.com/google-research/kubric) dat
 
 ## Contact
 
-For questions, please contact liuchang666@sjtu.edu.cn.
+For questions, please contact liuchang666@sjtu.edu.cn.