Add files using upload-large-folder tool

.gitattributes

@@ -58,3 +58,10 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+merged_train_metadata.json filter=lfs diff=lfs merge=lfs -text
+merged_test_metadata.json filter=lfs diff=lfs merge=lfs -text
+metadata/train_pass_scenes.jsonl filter=lfs diff=lfs merge=lfs -text
+metadata/all_pass_scenes.jsonl filter=lfs diff=lfs merge=lfs -text
+testB/extracted_metadata.json filter=lfs diff=lfs merge=lfs -text
+train/extracted_metadata.json filter=lfs diff=lfs merge=lfs -text
+testA/extracted_metadata.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,368 @@
+---
+license: other
+pretty_name: KubriCount
+tags:
+- image
+- synthetic
+- object-counting
+- visual-counting
+- tar
+- shards
+---
+
+# KubriCount
+
+This dataset is a synthetic visual counting dataset.
+
+Each annotation item describes one image, the target category to count, positive instance boxes/points, and a negative category with corresponding negative boxes/points. The dataset has been filtered by `vlm_filter_results.json`; only scenes marked as `PASS` are included in the released tar shards.
+
+## Dataset structure
+
+```text
+.
+├── README.md
+├── metadata/
+│   ├── all_pass_scenes.jsonl
+│   ├── train_pass_scenes.jsonl
+│   ├── testA_pass_scenes.jsonl
+│   ├── testB_pass_scenes.jsonl
+│   ├── shards.jsonl
+│   └── dataset_stats.json
+├── shards/
+│   ├── train/
+│   │   ├── train-000000.tar
+│   │   ├── train-000001.tar
+│   │   └── ...
+│   ├── testA/
+│   │   ├── testA-000000.tar
+│   │   └── ...
+│   └── testB/
+│       ├── testB-000000.tar
+│       └── ...
+├── train/
+│   ├── vlm_filter_results.json
+│   └── other train-level json files
+├── testA/
+│   ├── vlm_filter_results.json
+│   └── other testA-level json files
+├── testB/
+│   ├── vlm_filter_results.json
+│   └── other testB-level json files
+└── other dataset-level json files
+```
+
+The scene folders are stored inside tar shards. Each tar file preserves the original split/level/timestamp/scene structure, for example:
+
+```text
+train/level5/20260205_135900/scene_0431/edited_00000.png
+train/level5/20260205_135900/scene_0431/...
+testB/level1/20260205_132729/scene_0046/...
+```
+
+## Splits
+
+```text
+train, testA, testB
+```
+
+## Sharding information
+
+- Scenes per shard: `5000`
+- Total PASS scenes: `110507`
+- Total files inside scenes: `442028`
+- Total shards: `24`
+
+Shard metadata is available in:
+
+```text
+metadata/shards.jsonl
+```
+
+Scene-to-shard metadata is available in:
+
+```text
+metadata/all_pass_scenes.jsonl
+metadata/train_pass_scenes.jsonl
+metadata/testA_pass_scenes.jsonl
+metadata/testB_pass_scenes.jsonl
+```
+
+## Annotation format
+
+A typical annotation item is:
+
+```json
+{
+  "image_id": "/mnt/vision_user/changliu/kubric/count_data_best/train/level5/20260205_135900/scene_0431/edited_00000.png",
+  "count": 6,
+  "box_examples_coordinates": [
+    [[[142, 656], [142, 1024], [494, 1024], [494, 656]]],
+    [[[230, 610], [230, 945], [632, 945], [632, 610]]]
+  ],
+  "points": [
+    [331.9403381347656, 895.3883056640625],
+    [438.8674011230469, 792.9484252929688]
+  ],
+  "H": 1024,
+  "W": 1024,
+  "category": "trousers",
+  "metadata": {
+    "level": 5,
+    "split": "train",
+    "config_file": "/kubric/config_gpt.json"
+  },
+  "negative_count": 12,
+  "negative_category": "tie",
+  "negative_box_examples_coordinates": [
+    [[[591, 897], [591, 1008], [723, 1008], [723, 897]]]
+  ],
+  "negative_points": [
+    [655.068603515625, 951.5546875]
+  ]
+}
+```
+
+Field meanings:
+
+- `image_id`: original image path used when generating the annotation.
+- `count`: number of target-category objects.
+- `category`: target category.
+- `box_examples_coordinates`: bounding boxes for target-category objects. Each box is represented by four corner points.
+- `points`: point annotations for target-category objects.
+- `H`, `W`: image height and width.
+- `metadata.level`: difficulty or generation level.
+- `metadata.split`: data split.
+- `negative_category`: category used as negative reference.
+- `negative_count`: number of negative-category objects.
+- `negative_box_examples_coordinates`: boxes for negative-category objects.
+- `negative_points`: point annotations for negative-category objects.
+
+The released image files are stored inside tar shards. To locate an image after extraction, use the split-relative part of `image_id`.
+
+For example, this original path:
+
+```text
+/mnt/vision_user/changliu/kubric/count_data_best/train/level5/20260205_135900/scene_0431/edited_00000.png
+```
+
+corresponds to the extracted path:
+
+```text
+train/level5/20260205_135900/scene_0431/edited_00000.png
+```
+
+## PASS filtering
+
+The original dataset was filtered using `vlm_filter_results.json`.
+
+Only scenes whose annotation value is `PASS` are included in the tar shards. For example:
+
+```json
+{
+  "level1/20260205_132729/scene_0046": "PASS"
+}
+```
+
+Scenes marked as `FAIL` are not included.
+
+## Manifest format
+
+Each line in `metadata/all_pass_scenes.jsonl` describes one released scene. Example:
+
+```json
+{
+  "split": "train",
+  "scene": "level1/20260205_132641/scene_0001",
+  "path_in_dataset": "train/level1/20260205_132641/scene_0001",
+  "shard": "shards/train/train-000000.tar",
+  "num_files": 4,
+  "files": [
+    {
+      "path": "train/level1/20260205_132641/scene_0001/edited_00000.png",
+      "name": "edited_00000.png",
+      "size_bytes": 2168861
+    },
+    {
+      "path": "train/level1/20260205_132641/scene_0001/metadata.json",
+      "name": "metadata.json",
+      "size_bytes": 459308
+    },
+    {
+      "path": "train/level1/20260205_132641/scene_0001/rgba_00000.png",
+      "name": "rgba_00000.png",
+      "size_bytes": 1660110
+    },
+    {
+      "path": "train/level1/20260205_132641/scene_0001/segmentation_00000.png",
+      "name": "segmentation_00000.png",
+      "size_bytes": 61694
+    }
+  ]
+}
+```
+
+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
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="YOUR_USERNAME_OR_ORG/KubriCount",
+    repo_type="dataset",
+    local_dir="./KubriCount",
+)
+```
+
+Command line:
+
+```bash
+huggingface-cli download YOUR_USERNAME_OR_ORG/KubriCount \
+  --repo-type dataset \
+  --local-dir ./KubriCount
+```
+
+## Restore the original folder structure
+
+Use the following script to extract all tar shards and copy the JSON files to the restored dataset directory.
+
+```python
+from pathlib import Path
+import tarfile
+import shutil
+
+
+repo_dir = Path("./KubriCount")
+restore_dir = Path("./KubriCount_restored")
+splits = ["train", "testA", "testB"]
+
+restore_dir.mkdir(parents=True, exist_ok=True)
+
+
+def safe_extract(tar, path):
+    path = Path(path).resolve()
+
+    for member in tar.getmembers():
+        target = (path / member.name).resolve()
+        if not str(target).startswith(str(path)):
+            raise RuntimeError(f"Unsafe path in tar: {member.name}")
+
+    tar.extractall(path)
+
+
+# Extract scene folders.
+for tar_path in sorted((repo_dir / "shards").glob("*/*.tar")):
+    print(f"Extracting {tar_path}")
+    with tarfile.open(tar_path, "r") as tar:
+        safe_extract(tar, restore_dir)
+
+
+# Copy dataset-level JSON files.
+for p in repo_dir.glob("*.json"):
+    shutil.copy2(p, restore_dir / p.name)
+
+
+# Copy split-level JSON files.
+for split in splits:
+    src_split_dir = repo_dir / split
+    dst_split_dir = restore_dir / split
+    dst_split_dir.mkdir(parents=True, exist_ok=True)
+
+    if src_split_dir.exists():
+        for p in src_split_dir.glob("*.json"):
+            shutil.copy2(p, dst_split_dir / p.name)
+
+print(f"Restored dataset to: {restore_dir}")
+```
+
+After extraction:
+
+```text
+KubriCount_restored/
+├── train/
+│   ├── vlm_filter_results.json
+│   └── level1/
+│   └── level2/
+│   └── ...
+├── testA/
+│   ├── vlm_filter_results.json
+│   └── level1/
+│   └── ...
+├── testB/
+│   ├── vlm_filter_results.json
+│   └── level1/
+│   └── ...
+└── dataset-level json files
+```
+
+## Resolve `image_id` to a local extracted image path
+
+Some annotation files may contain absolute original paths in `image_id`. After extraction, convert them to local paths as follows:
+
+```python
+from pathlib import Path
+
+
+def resolve_image_id(image_id, restored_root):
+    restored_root = Path(restored_root)
+
+    parts = Path(image_id).parts
+
+    for split in ["train", "testA", "testB"]:
+        if split in parts:
+            idx = parts.index(split)
+            rel_path = Path(*parts[idx:])
+            return restored_root / rel_path
+
+    raise ValueError(f"Cannot find split name in image_id: {image_id}")
+
+
+image_id = "/mnt/vision_user/changliu/kubric/count_data_best/train/level5/20260205_135900/scene_0431/edited_00000.png"
+local_path = resolve_image_id(image_id, "./KubriCount_restored")
+
+print(local_path)
+```
+
+Output:
+
+```text
+KubriCount_restored/train/level5/20260205_135900/scene_0431/edited_00000.png
+```
+
+## Read images directly from tar shards
+
+If you do not want to extract the full dataset, you can read files directly from tar shards.
+
+```python
+from pathlib import Path
+import tarfile
+
+
+repo_dir = Path("./KubriCount")
+
+for tar_path in sorted((repo_dir / "shards").glob("*/*.tar")):
+    with tarfile.open(tar_path, "r") as tar:
+        for member in tar:
+            if not member.isfile():
+                continue
+
+            if member.name.endswith(".png") or member.name.endswith(".jpg") or member.name.endswith(".jpeg"):
+                f = tar.extractfile(member)
+                data = f.read()
+                print(member.name, len(data))
+                break
+```
+
+To find which shard contains a specific scene, use:
+
+```text
+metadata/all_pass_scenes.jsonl
+```

merged_test_metadata.json

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metadata/all_pass_scenes.jsonl

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metadata/dataset_stats.json

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+{
+  "created_at": "2026-04-27T05:34:33",
+  "staging_root": "/remote-home/changliu/kubric/count_data_best_pass_staging",
+  "out_root": "/remote-home/changliu/kubric/count_data_best_hf",
+  "splits": [
+    "train",
+    "testA",
+    "testB"
+  ],
+  "scenes_per_shard": 5000,
+  "total_scenes": 110507,
+  "total_missing_scenes": 0,
+  "total_files": 442028,
+  "total_shards": 24,
+  "copied_root_jsons": [
+    "/remote-home/changliu/kubric/count_data_best_hf/merged_test_metadata.json",
+    "/remote-home/changliu/kubric/count_data_best_hf/merged_train_metadata.json"
+  ],
+  "copied_split_jsons": {
+    "train": [
+      "/remote-home/changliu/kubric/count_data_best_hf/train/extracted_metadata.json",
+      "/remote-home/changliu/kubric/count_data_best_hf/train/vlm_filter_results.json"
+    ],
+    "testA": [
+      "/remote-home/changliu/kubric/count_data_best_hf/testA/extracted_metadata.json",
+      "/remote-home/changliu/kubric/count_data_best_hf/testA/vlm_filter_results.json"
+    ],
+    "testB": [
+      "/remote-home/changliu/kubric/count_data_best_hf/testB/extracted_metadata.json",
+      "/remote-home/changliu/kubric/count_data_best_hf/testB/vlm_filter_results.json"
+    ]
+  }
+}

metadata/shards.jsonl

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