diff --git a/.gitattributes b/.gitattributes index bed0738c7eeb449bca98b5d2f33c89a1ee56349a..67ecd44e7b75acfb97d63dc795200f7b259df3ea 100644 --- a/.gitattributes +++ b/.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 diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9e318302efc3a084fccb5bfb21adff78b432df3a --- /dev/null +++ b/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//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": "", "question": "...", "options": "...", "images": ["images/.jpg"], + "reasoning_thought_0": "...", "reasoning_image_0": "images/.jpg", + "reasoning_thought_1": "...", "answer": "...", "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 + and send it to matterport3d@googlegroups.com. The + rows name the Matterport3D panoramas they use. +- **Ego-Exo4D:** obtain the dataset under its license (). 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. diff --git a/preview/train-00000-of-00001.parquet b/preview/train-00000-of-00001.parquet new file mode 100644 index 0000000000000000000000000000000000000000..9ffe456e2d6f47e6f68f2b971a3d423e5f6cce00 --- /dev/null +++ b/preview/train-00000-of-00001.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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