| --- |
| license: mit |
| task_categories: |
| - visual-question-answering |
| - question-answering |
| language: |
| - en |
| tags: |
| - robotics |
| - 6dof-pose |
| - grasping |
| - spatial-reasoning |
| - trajectory |
| - depth-estimation |
| - bop-challenge |
| size_categories: |
| - 10M<n<100M |
| pretty_name: BOPASK-Train |
| --- |
| |
| # BOPASK-Train |
|
|
| **BOPASK** is a large-scale visual-question-answering dataset for |
| robotic spatial understanding, built on top of four BOP-Challenge: **HANDAL**, **HOPE**, |
| **LineMOD**, and **YCB-V**. |
|
|
| This release contains **32.68 M training QA pairs** across **172 K unique RGB images**, |
| covering **8 question types**. |
|
|
| ## Contents |
|
|
| | Family | QA pairs | Unique images | |
| |----------|-----------:|--------------:| |
| | handal | 442,729 | 4,416 | |
| | hope | 18,323,007 | 82,229 | |
| | linemod | 13,165,385 | 61,883 | |
| | ycbv | 749,461 | 23,524 | |
| | **Total**| **32,680,582** | **172,052** | |
|
|
| ### Question-type breakdown (all families combined) |
|
|
| | Question type | Count | |
| |------------------------|------------:| |
| | trajectory | 13,552,164 | |
| | spatial_reasoning | 8,888,686 | |
| | depth_relative | 7,256,308 | |
| | pose | 1,438,641 | |
| | grasp | 853,401 | |
| | depth_absolute | 446,658 | |
| | object_rearrangement | 157,095 | |
| | camera (extrinsics) | 87,629 | |
|
|
| ## Layout |
|
|
| ``` |
| bopask-train/ |
| ├── handal/ |
| │ ├── bopask-handal-train.jsonl |
| │ ├── images/ # RGB frames (.png) |
| │ ├── depth_maps/ # aligned depth (.png, uint16 mm) |
| │ └── masks/ # per-object binary masks (.png) |
| ├── hope/ (same structure) |
| ├── linemod/ (same structure) |
| └── ycbv/ (same structure) |
| ``` |
|
|
| All paths inside each jsonl are **family-relative**: |
| - `images/<basename>.png` |
| - `depth_maps/<basename>_depth.png` |
| - `masks/<basename>_..._mask.png` (comma-separated if multiple objects) |
|
|
| ## Record format (LLaVA-style) |
|
|
| Each line of a `*-train.jsonl` is a JSON object: |
|
|
| ```json |
| { |
| "conversations": [ |
| {"from": "user", "value": "<image>\n<question>"}, |
| {"from": "gpt", "value": "<answer>"} |
| ], |
| "images": ["images/scene_000000_frame_000000.png"], |
| "depths": ["depth_maps/scene_000000_frame_000000_depth.png"], |
| "masks": "masks/scene_000000_frame_000000_obj_000018_mask.png", |
| "question_type": "pose", |
| "question_subtype": "matrix", |
| "object_id": 18 |
| } |
| ``` |
|
|
| ### Field notes |
| - `conversations`: a user/assistant turn pair. The user prompt starts with the |
| `<image>` sentinel token used by many VLMs (e.g. LLaVA / Qwen-VL). |
| - `images`, `depths`: lists of paths relative to the family folder. |
| - `masks`: a single string. If multiple masks are relevant (e.g. pairwise |
| `trajectory`, `spatial_reasoning`, `object_rearrangement` questions) they are |
| comma-separated: `"masks/...target..._mask.png,masks/...goal..._mask.png"`. |
| Some rows have `masks: null` when the question does not target a specific object |
| (e.g. `camera` extrinsics). Masks are **optional** for most downstream uses. |
| - `object_id`: integer for single-object questions, or a `"target,goal"` string |
| for pairwise ones. Absent for `camera` questions. |
| - `question_type`: one of `pose`, `grasp`, `camera`, `depth_absolute`, |
| `depth_relative`, `spatial_reasoning`, `trajectory`, `object_rearrangement`. |
| - `question_subtype`: further specifies the answer format (e.g. |
| `matrix` / `quaternion` / `2dbbox` / `3dbbox` for `pose`; `2d` / `3d` for |
| `trajectory`; etc.). |
|
|
| ## Known caveats |
| - A very small number of depth / mask files (≈0.004% of rows, mostly in |
| LineMOD scenes 12 & 39) are absent because the originals were not recoverable. |
| The JSONLs still reference them so you may want to handle `FileNotFoundError` |
| gracefully in your loader. |
| - `masks` are not strictly required for most VQA training setups; downstream |
| users who only need RGB + depth + the conversations can safely ignore them. |
|
|
| ## Quick start |
|
|
| ```python |
| import json |
| from datasets import load_dataset |
| |
| ds = load_dataset( |
| "bhatvineet/bopask-train", |
| data_files={"handal": "handal/bopask-handal-train.jsonl", |
| "hope": "hope/bopask-hope-train.jsonl", |
| "linemod": "linemod/bopask-linemod-train.jsonl", |
| "ycbv": "ycbv/bopask-ycbv-train.jsonl"}, |
| ) |
| print(ds) |
| ``` |
|
|
| Or streaming one family at a time: |
|
|
| ```python |
| import json |
| |
| path = "handal/bopask-handal-train.jsonl" |
| with open(path) as f: |
| for line in f: |
| rec = json.loads(line) |
| # rec["images"][0] is relative to the "handal/" folder |
| ... |
| ``` |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite the [BOPASK](https://arxiv.org/abs/2511.16857) paper |
| and the underlying BOP-Challenge object-pose datasets |
| (HANDAL, HOPE, LineMOD, YCB-V). |
|
|
| ## License |
|
|
| Released under the MIT License for the question-answer annotations. |
| The underlying RGB, depth, and mask assets inherit the license of their source |
| BOP-Challenge datasets. |
|
|
