ThinkMorph-SSL data release

.gitattributes

@@ -58,3 +58,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+text_only/images.jsonl filter=lfs diff=lfs merge=lfs -text
+text_only/provenance.jsonl filter=lfs diff=lfs merge=lfs -text
+text_only/train.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,165 @@
+---
+license: other
+license_name: per-source-terms
+pretty_name: Instruct-181K
+task_categories:
+- visual-question-answering
+- any-to-any
+tags:
+- thinkmorph-ssl
+- primer
+- interleaved-reasoning
+- spatial-reasoning
+configs:
+- config_name: preview
+  data_files:
+  - split: train
+    path: preview/*.parquet
+---
+
+# Instruct-181K
+
+Instruct-181K is the Stage 2 data of PRIMER. It trains
+[ThinkMorph-SSL-Instruct](https://huggingface.co/ThinkMorph/ThinkMorph-SSL-Instruct), starting from
+[ThinkMorph-SSL-Base](https://huggingface.co/ThinkMorph/ThinkMorph-SSL-Base). Each trace has a question, input
+images, textual reasoning with intermediate images, and an answer.
+
+- **Images:** the intermediate images are targets built from the original images: localized
+  regions, depth, geometry, reconstructions, paired views and calibrated objects.
+- **Text:** teacher models write the text; generation and filtering depend on the task.
+
+**Counts.**
+- The paper counts **181,291** traces, the rows of the 21 training folders. This release has
+  **180,490** of them: 142,333 with images and
+  38,157 text only.
+- The difference of 801 rows never reached the model in training:
+  - 800 val rows (`depth_10k`, `egoexo_5k`) have no reasoning image, and Stage 2 drops such
+    rows;
+  - 1 row references a zero-byte image, and the loader skipped it.
+- Stage 2 also trained on the val files, so their rows are part of `train.jsonl`
+  (`source_split` = `val`).
+
+## Composition
+
+| Subset | Rows with images | Text-only rows | Sources |
+| --- | ---: | ---: | --- |
+| `spatial/behind_me_1k` | 1,324 | — | MindCube |
+| `spatial/behind_variants_286` | 284 | — | MindCube |
+| `spatial/directional_query_2k` | 2,594 | — | MindCube |
+| `spatial/movement_direction_1k` | 1,250 | — | MindCube |
+| `spatial/positioned_direction_423` | 423 | — | MindCube |
+| `spatial/turn_degrees_204` | 204 | — | MindCube |
+| `spatial/turn_move_closer_2k` | 2,576 | — | MindCube |
+| `spatial/multiview_58k` | 24,247 | 33,631 | Matterport3D (text only), Realsee3D, 360+x |
+| `spatial/depth_10k` | 9,575 | — | RefSpatial |
+| `spatial/counting_5k` | 5,569 | — | LVIS v1 val (COCO images) |
+| `spatial/rotation_10k` | 10,811 | — | MVImgNet |
+| `spatial/perspective_6k` | 6,512 | — | SAT |
+| `spatial/directional_1k` | 1,162 | — | COCO images (sam3d engine) |
+| `spatial/undirectional_5k` | 4,639 | — | COCO images (sam3d engine) |
+| `spatial/directional_cam_8k` | 8,063 | — | COCO images (Orient-Anything) |
+| `spatial/jigsaw_20k` | 20,000 | — | Pretext-Spatial-50K pools |
+| `perception/jigsaw_20k` | 19,935 | — | jigsaw_20k_v1 pools |
+| `perception/visual_search_bbox_15k` | 14,686 | — | Visual-CoT and crop pools |
+| `perception/visual_search_crop_7.5k` | 7,479 | — | crop pools |
+| `perception/count_1k` | 1,000 | — | synthetic counting scenes |
+| `spatial/egoexo_5k` | 0 | 4,526 | Ego-Exo4D (text only) |
+| **Total** | **142,333** | **38,157** | |
+
+| Functional group (paper) | Pipelines | Paper traces |
+| --- | --- | ---: |
+| L1: visible evidence | visual search, jigsaw QA | 62,100 |
+| L2: spatial structure | counting, depth reasoning | 16,544 |
+| L3: scene reconstruction | panorama, bird's-eye view, alternate view, object rotation, ego-exo transfer | 102,647 |
+| **Total** | | **181,291** |
+
+## Files
+
+| Path | What it is |
+| --- | --- |
+| `shards/SHARDS.json`, `shards/data-*.tar` | The release with images: 142,333 train rows and 286,889 images (157.0 GB), in 148 deterministic tar shards |
+| `text_only/` | 38,157 rows whose images come from Matterport3D or Ego-Exo4D. Their images are not distributed here; see [Rows without images](#rows-without-images) |
+| `preview/*.parquet` | The Dataset Viewer preview: a few rows per subset, with images downscaled to at most 768 px |
+
+## Download and restore
+
+```bash
+python scripts/download_release.py shards/SHARDS.json \
+  --base-url https://huggingface.co/datasets/ThinkMorph/Instruct-181K/resolve/<COMMIT>/shards --output shards
+python scripts/shard_data_release.py restore shards/SHARDS.json shards DATA_ROOT/primer-instruct-181k
+```
+
+(`hf download ThinkMorph/Instruct-181K --repo-type dataset --include "shards/*"` works too.)
+
+- `download_release.py` checks each shard's size and SHA256.
+- `restore` checks every shard before extracting it, then checks every file against the
+  `MANIFEST.json` inside the shards.
+- A restored release holds `train.jsonl` (and `val.jsonl` if present), `images/` and
+  `MANIFEST.json`, which is the layout the BAGEL loader reads.
+- The scripts are in the PRIMER code release.
+
+**Check.** Before upload, the restored release went through the BAGEL loader, which decoded
+every image: 142,333 usable rows and 286,889 images, result
+PASS.
+
+## Row format
+
+```json
+{"id": "<sha256>", "question": "...", "options": "...", "images": ["images/<sha256>.jpg"],
+ "reasoning_thought_0": "...", "reasoning_image_0": "images/<sha256>.jpg",
+ "reasoning_thought_1": "...", "answer": "...", "subset": "<source subset>", "source_split": "train"}
+```
+
+- The supervision text (`question`, `options`, `answer`/`gt_answer`, every
+  `reasoning_thought_N`) and the image bytes are exactly as in the training copy.
+- Image paths are content-hashed relative paths, and `id` is a public id.
+- `subset` names the storage subset a row came from, and `source_split` the split it had
+  there.
+- Source metadata fields are removed, because they contain internal storage paths.
+
+## Rows without images
+
+`text_only/` holds the rows whose images come from **Matterport3D or Ego-Exo4D**. Their terms do not allow
+us to redistribute the images. Obtain the source data yourself and rebuild the images.
+
+- `text_only/train.jsonl` (and `val.jsonl`): the rows, normalized like the main release
+  (same public ids), with `subset` and `source_split`. The image fields keep the
+  **original file names**.
+- `text_only/images.jsonl`: one record per original image (`subset`, `split`, `name`). Each
+  record gives the byte size and MD5 of the exact file used in training, the upstream
+  dataset and ids, the role and a derivation sentence.
+- `text_only/provenance.jsonl`: per row, the reconstruction parameters (for example yaw
+  angles of paired views, jigsaw grid and tile permutation, original image id and size, take
+  name and frame rank).
+
+To get the source data:
+
+- **Matterport3D:** sign the Matterport3D Terms of Use. Get the form from
+  <https://niessner.github.io/Matterport/> and send it to matterport3d@googlegroups.com. The
+  rows name the Matterport3D panoramas they use.
+- **Ego-Exo4D:** obtain the dataset under its license (<https://ego-exo4d-data.org/>). The
+  frames can be regenerated with `data/builders/egoexo/export_take_ninepack.py` from the PRIMER
+  code release; its README gives the exact parameters. An image name gives the take, ego or
+  exo, and the frame rank.
+
+A rebuilt image may differ in bytes even when it shows the same content. Compare it with the
+recorded MD5.
+
+## Source terms
+
+Each source keeps its own terms. The image release has no blanket license. Matterport3D and
+Ego-Exo4D rows are text only.
+
+## Evaluation proximity
+
+- VStar, MMSI and MindCube are close to the visual-search, panorama and viewpoint
+  supervision.
+- HRBench and MME-RW-L are nearby perception tasks.
+- No evaluation questions or official templates were used to build the data.
+
+## Provenance
+
+- **Training copy.** Training read an internal storage copy that is no longer reachable.
+- **This release.** It is built from the team's archived copy of the same training folders.
+- **What matches.** Row counts agree with the recorded counts of the copy training read.
+- **What could not be checked.** Byte identity with the training copy.

preview/train-00000-of-00001.parquet

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