---
license: other
license_name: per-source-terms
pretty_name: Instruct-180K
language:
- en
size_categories:
- 100K<n<1M
task_categories:
- visual-question-answering
- any-to-any
configs:
- config_name: birds_eye_view_rotation
  default: true
  data_files:
  - split: train
    path: preview/birds_eye_view_rotation.parquet
- config_name: panoramic_completion
  data_files:
  - split: train
    path: preview/panoramic_completion.parquet
- config_name: jigsaw_multi_frame
  data_files:
  - split: train
    path: preview/jigsaw_multi_frame.parquet
- config_name: jigsaw_single_image
  data_files:
  - split: train
    path: preview/jigsaw_single_image.parquet
- config_name: visual_search_bbox
  data_files:
  - split: train
    path: preview/bbox.parquet
- config_name: object_rotation
  data_files:
  - split: train
    path: preview/object_rotation.parquet
- config_name: object_orientation
  data_files:
  - split: train
    path: preview/object_orientation.parquet
- config_name: crop
  data_files:
  - split: train
    path: preview/crop.parquet
- config_name: depth_map
  data_files:
  - split: train
    path: preview/depth_map.parquet
- config_name: marked_position_view
  data_files:
  - split: train
    path: preview/marked_position_view.parquet
- config_name: object_segmentation
  data_files:
  - split: train
    path: preview/object_segmentation.parquet
- config_name: object_viewpoint_render
  data_files:
  - split: train
    path: preview/object_viewpoint_render.parquet
- config_name: marked_depth_map
  data_files:
  - split: train
    path: preview/marked_depth_map.parquet
- config_name: object_viewpoint_render_oriented
  data_files:
  - split: train
    path: preview/object_viewpoint_render_oriented.parquet
- config_name: object_editing
  data_files:
  - split: train
    path: preview/object_editing.parquet
- config_name: birds_eye_view
  data_files:
  - split: train
    path: preview/birds_eye_view.parquet
---

# Instruct-180K

Instruct-180K is the instruction-tuning data of
[ThinkMorph-SSL-Instruct](https://huggingface.co/ThinkMorph/ThinkMorph-SSL-Instruct), trained from
[ThinkMorph-SSL-Base](https://huggingface.co/ThinkMorph/ThinkMorph-SSL-Base). It holds 180,490 interleaved reasoning
traces. Each trace has a question, one or more input images, reasoning text with intermediate
images, and a final answer.

The traces are grouped into 17 tasks by what their intermediate images do. The levels follow
the paper: basic visual perception, static spatial understanding, and mental world modeling
(object manipulation and viewpoint synthesis).

## Tasks

| Level | Task | Intermediate images | Rows with images | Text-only rows |
| --- | --- | --- | ---: | ---: |
| L1 Basic visual perception | `bbox` | a box drawn around the target | 14,686 | — |
|  | `crop` | a zoomed-in crop of the target | 7,479 | — |
|  | `jigsaw_single_image` | the pieces of one image, reassembled | 19,935 | — |
|  | `jigsaw_multi_frame` | separate image pieces, reassembled into one image | 20,000 | — |
| L2 Static spatial understanding | `object_segmentation` | the objects to count, masked out | 5,569 | — |
|  | `depth_map` | a depth map | 6,849 | — |
|  | `marked_depth_map` | the query points marked on the image, then a depth map | 2,726 | — |
| L3 Object manipulation | `object_rotation` | the object after each camera rotation | 10,811 | — |
|  | `object_editing` | the scene after the edits | 1,000 | — |
| L3 Viewpoint synthesis | `birds_eye_view` | a bird's-eye view of the scene | 284 | — |
|  | `birds_eye_view_rotation` | a bird's-eye view, then re-oriented views | 8,371 | — |
|  | `panoramic_completion` | the views completed into a panorama | 24,247 | 33,631 |
|  | `object_viewpoint_render` | object masks, then the view from one object toward a point | 4,639 | — |
|  | `object_viewpoint_render_oriented` | object masks, the object's 3D axes, then the view along its facing direction | 1,162 | — |
|  | `object_orientation` | an object mask, then its 3D axes | 8,063 | — |
|  | `marked_position_view` | the view from a marked spot after turning (rendered rooms) | 6,512 | — |
|  | `exo_to_ego` | the person's own view, from an outside view | — | 4,526 |
| **Total** | | | **142,333** | **38,157** |

All rows are training rows. Choose a task in the Dataset Viewer to see examples; there `bbox` is
listed as `visual_search_bbox`.

## Files

| Path | Content |
| --- | --- |
| `shards/annotations.tar` | `MANIFEST.json` and every task file: all rows, no images |
| `shards/data-*.tar` | the 286,889 images (157 GB) in 148 tar shards |
| `shards/SHARDS.json` | size and SHA256 of every shard |
| `text_only/` | 38,157 rows whose images (Matterport3D, Ego-Exo4D) are not distributed here; see [Rows without images](#rows-without-images) |
| `preview/` | the Dataset Viewer examples, one file per task |

## Download

```bash
hf download ThinkMorph/Instruct-180K --repo-type dataset --include "shards/*" --local-dir Instruct-180K
mkdir -p data
for f in Instruct-180K/shards/*.tar; do tar -xf "$f" -C data; done
```

- `shards/SHARDS.json` gives each shard's SHA256, and `MANIFEST.json` each extracted file's.
- To read the rows without the images, download and extract `shards/annotations.tar` only.

## Layout and row format

After extracting the shards:

```text
data/
  MANIFEST.json
  images/<sha256>.<ext>          shared by all tasks
  bbox/train.jsonl
  crop/train.jsonl
  ...                            one folder per task
```

```json
{"id": "<sha256>", "task": "bbox", "question": "...", "images": ["../images/<sha256>.jpg"],
 "reasoning_thought_0": "...", "reasoning_image_0": "../images/<sha256>.jpg",
 "reasoning_thought_1": "...", "answer": "B"}
```

- `options` appears only in tasks that keep the choices outside the question.
- Image paths are relative to the row's file. `id` is a stable public id.

## Reading the data

A trace reads in this order: the input images and the question, then
`reasoning_thought_0`, `reasoning_image_0`, `reasoning_thought_1`, and so on, then the answer.

```python
import json
from pathlib import Path
from PIL import Image

folder = Path("data/bbox")                      # one task folder of the extracted release
with open(folder / "train.jsonl") as f:
    row = json.loads(next(f))
inputs = [Image.open(folder / p) for p in row["images"]]
steps, i = [], 0
while f"reasoning_thought_{i}" in row:
    image = row.get(f"reasoning_image_{i}")
    steps.append((row[f"reasoning_thought_{i}"], Image.open(folder / image) if image else None))
    i += 1
print(row["question"], row.get("options"), row["answer"])
```

## Rows without images

`text_only/` holds the rows of `panoramic_completion` whose images come from Matterport3D, and of `exo_to_ego` (Ego-Exo4D). It has the same task folders as the main release.
The terms of their image sources do not allow us to redistribute the images.

- `text_only/images.jsonl` names every image. For each one it gives the upstream identifiers,
  how the image was derived, and the size and MD5 of the file used in training.
- Put the images you obtain in `text_only/images/`; the rows refer to them as `../images/<name>`.

To obtain the source data:

- **Matterport3D:** sign the Matterport3D Terms of Use. The form is at
  <https://niessner.github.io/Matterport/>; send it to matterport3d@googlegroups.com.
- **Ego-Exo4D:** obtain the dataset under its license at <https://ego-exo4d-data.org/>. Each
  image record names the take and the camera. The frame-selection code will be released with
  the training code.

## Sources and licenses

Images keep the terms of the dataset they come from; check them before you use the data.
Several sources allow non-commercial research use only. Licenses below are as stated by each
source.

| Task | Image sources | Terms |
| --- | --- | --- |
| `bbox` | [SA-1B](https://ai.meta.com/datasets/segment-anything/); [Visual-CoT](https://huggingface.co/datasets/deepcs233/Visual-CoT) (its images come from COCO, GQA, TextVQA, Visual Genome, Flickr30k, DocVQA, Open Images, CUB and others) | SA-1B Dataset Research License; Visual-CoT: CC BY-NC 4.0, images under their original terms |
| `crop`, `jigsaw_single_image` | [SA-1B](https://ai.meta.com/datasets/segment-anything/) | SA-1B Dataset Research License |
| `jigsaw_multi_frame` | [Hypersim](https://github.com/apple-aiml-research/ml-hypersim), [ScanNet](https://github.com/ScanNet/ScanNet), [Open X-Embodiment](https://github.com/google-deepmind/open_x_embodiment) robot datasets, [Visual7W](https://github.com/yukezhu/visual7w-toolkit) (COCO images) and other open images | Hypersim: CC BY-SA 3.0; ScanNet: ScanNet Terms of Use; Open X-Embodiment: CC BY 4.0; COCO images: Flickr terms |
| `object_segmentation` | [LVIS v1](https://www.lvisdataset.org/) on [COCO 2017](https://cocodataset.org/#termsofuse) images | LVIS annotations: CC BY 4.0; COCO images: Flickr terms |
| `depth_map`, `marked_depth_map` | [RefSpatial](https://huggingface.co/datasets/JingkunAn/RefSpatial): [Open Images](https://storage.googleapis.com/openimages/web/index.html), [CA-1M](https://github.com/apple/ml-cubifyanything) and simulated scenes | RefSpatial: Apache-2.0; Open Images: CC BY 2.0; CA-1M: CC BY-NC-ND |
| `object_rotation` | [MVImgNet](https://github.com/GAP-LAB-CUHK-SZ/MVImgNet) | MVImgNet Terms of Use (non-commercial research) |
| `object_editing` | synthetic scenes in the style of [CLEVR](https://cs.stanford.edu/people/jcjohns/clevr/) | CLEVR: CC BY 4.0 |
| `birds_eye_view`, `birds_eye_view_rotation` | [MindCube](https://huggingface.co/datasets/MLL-Lab/MindCube) (its images come from [ARKitScenes](https://github.com/apple-aiml-research/ARKitScenes), WildRGB-D, DL3DV-10K and self-collected scenes) | MindCube: MIT; ARKitScenes: Apple license, non-commercial |
| `panoramic_completion` | [Realsee3D](https://doi.org/10.5281/zenodo.17826243), [360+x](https://x360dataset.github.io/); [Matterport3D](https://niessner.github.io/Matterport/) (text only) | Realsee3D: CC BY 4.0; 360+x: CC BY-NC-SA 4.0; Matterport3D: Terms of Use |
| `object_viewpoint_render`, `object_viewpoint_render_oriented`, `object_orientation` | [COCO 2017](https://cocodataset.org/#termsofuse) images | Flickr terms (COCO terms of use) |
| `marked_position_view` | [SAT](https://huggingface.co/datasets/array/SAT) (rendered rooms) | MIT |
| `exo_to_ego` | [Ego-Exo4D](https://ego-exo4d-data.org/) (text only) | Ego-Exo4D license agreement |

The reasoning text, the intermediate images and the release layout are ours. There is no
blanket license over the images.