| --- |
| 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. |
|
|
