--- license: other task_categories: - image-classification language: - en size_categories: - 100K `3`, base folder -> `1`) - `scene_variant` (string): source folder name - `frame_id` (int): numeric frame id from filename - `image` (struct): embedded image bytes + relative path - `image_relpath` (string): relative source image path - `params_relpath` (string): relative source JSON path - `raw_params_json` (string): full original JSON text - `camera_json` (string): `camera` section - `actors_json` (string): `actors` section - `source_json` (string): `source` section - `actor_labels` (list[string]): unique labels found in `actors` - `has_label_mapping` (bool): whether `source.label_mapping` exists - `label_mapping_json` (string): full mapping JSON - `label_mapping_keys` (list[string]): mapping keys - `label_mapping_values` (list[string]): mapping values - `original_params_name` (string): `source.original_params` when present - `upload_batch_utc` (string): UTC timestamp of upload run ## Loading ```python from datasets import load_dataset, Image ds = load_dataset("turhancan97/SpaRRTa", split="train") # Convert struct column to datasets Image feature if needed: ds = ds.cast_column("image", Image()) ``` ## Download to Local Machine Download the dataset repo files (parquet shards, README, manifest): ```bash huggingface-cli download turhancan97/SpaRRTa --repo-type dataset --local-dir ./hf_SpaRRTa ``` ## Rebuild Original Folder Structure The script below reconstructs: `//img_XXXX.jpg` and `params_XXXX.json` ```python from pathlib import Path from datasets import load_dataset, Image repo_id = "turhancan97/SpaRRTa" output_root = Path("restored_position_between_objects") output_root.mkdir(parents=True, exist_ok=True) ds = load_dataset(repo_id, split="train") ds = ds.cast_column("image", Image(decode=False)) for row in ds: mid = output_root / row["scene_variant"] mid.mkdir(parents=True, exist_ok=True) image_name = Path(row["image_relpath"]).name params_name = Path(row["params_relpath"]).name image_bytes = row["image"]["bytes"] if image_bytes is None: raise RuntimeError(f"Missing embedded image bytes for {row['sample_id']}") (mid / image_name).write_bytes(image_bytes) (mid / params_name).write_text(row["raw_params_json"], encoding="utf-8") ```