Dataset card organized by task

README.md

@@ -2,6 +2,13 @@
 license: other
 license_name: per-source-terms
 pretty_name: Instruct-181K
+language:
+- en
+size_categories:
+- 100K<n<1M
+task_categories:
+- visual-question-answering
+- any-to-any
 configs:
 - config_name: panoramic_completion
   data_files:
@@ -71,4 +78,102 @@ configs:
 
 # Instruct-181K
 
-More details will be added later.
+Instruct-181K 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.
+
+## 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-181K --repo-type dataset --include "shards/*" --local-dir Instruct-181K
+mkdir -p data
+for f in Instruct-181K/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.
+
+## 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.
+
+## License
+
+The images come from several public datasets, and each keeps its own terms. There is no blanket
+license for the images. A list of the image sources per task will be added with the paper.