---
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.