Datasets:
Tasks:
Image Classification
Formats:
parquet
Languages:
English
Size:
100K - 1M
ArXiv:
Tags:
spatial-reasoning
spatial_intelligence
vision-foundation-models
probing
benchmark
unreal-engine
License:
File size: 5,032 Bytes
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license: other
task_categories:
- image-classification
language:
- en
size_categories:
- 100K<n<1M
dataset_info:
features:
- name: sample_id
dtype: string
- name: scene
dtype: string
- name: variant
dtype: int32
- name: scene_variant
dtype: string
- name: frame_id
dtype: int32
- name: image
dtype: image
- name: image_relpath
dtype: string
- name: params_relpath
dtype: string
- name: raw_params_json
dtype: string
- name: camera_json
dtype: string
- name: actors_json
dtype: string
- name: source_json
dtype: string
- name: actor_labels
sequence: string
- name: has_label_mapping
dtype: bool
- name: label_mapping_json
dtype: string
- name: label_mapping_keys
sequence: string
- name: label_mapping_values
sequence: string
- name: original_params_name
dtype: string
- name: upload_batch_utc
dtype: string
configs:
- config_name: default
data_files:
- split: train
path: data/train/*.parquet
---
# turhancan97/SpaRRTa
Generated from `D:\Unreal Engine Environments\game_trial\Content\Python\unreal-scene-gen\unreal-secene-gen\dataset` on 2026-03-09T22:41:09.474333+00:00.
## Summary
- Format: parquet shards with one row per sample.
- Split: `train` only.
- Images are embedded in parquet under the `image` struct column (`bytes`, `path`).
- Full raw metadata is preserved in `raw_params_json`.
## Run Stats
- Discovered paired samples: **99502**
- New rows prepared this run: **30000**
- Skipped because already uploaded: **69502**
- Skipped broken/missing pairs or parse errors: **0**
- Parquet shards written this run: **118**
## Scene Coverage
| scene_variant | scene | variant | paired_samples | new_rows | skipped_existing | missing_pairs |
|---|---:|---:|---:|---:|---:|---:|
| `bridge` | `bridge` | `1` | 9834 | 0 | 9834 | 0 |
| `bridge_2` | `bridge` | `2` | 9834 | 0 | 9834 | 0 |
| `bridge_3` | `bridge` | `3` | 9834 | 0 | 9834 | 0 |
| `city` | `city` | `1` | 10000 | 0 | 10000 | 0 |
| `city_2` | `city` | `2` | 10000 | 0 | 10000 | 0 |
| `city_3` | `city` | `3` | 10000 | 0 | 10000 | 0 |
| `desert` | `desert` | `1` | 10000 | 0 | 10000 | 0 |
| `desert_2` | `desert` | `2` | 10000 | 10000 | 0 | 0 |
| `desert_3` | `desert` | `3` | 10000 | 10000 | 0 | 0 |
| `forest` | `forest` | `1` | 10000 | 10000 | 0 | 0 |
| `hf_upload_reports` | `hf_upload_reports` | `1` | 0 | 0 | 0 | 0 |
## Columns
- `sample_id` (string): stable unique id (`scene_variant:frame_id`)
- `scene` (string): base scene name (e.g. `bridge`)
- `variant` (int): numeric variant from folder suffix (`bridge_3` -> `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:
`<output>/<scene_variant>/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")
```
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