| --- |
| license: other |
| license_name: per-source-terms |
| pretty_name: Pretext-270K |
| language: |
| - en |
| size_categories: |
| - 100K<n<1M |
| task_categories: |
| - image-to-image |
| - any-to-any |
| configs: |
| - config_name: jigsaw |
| data_files: |
| - split: train |
| path: preview/jigsaw.parquet |
| - config_name: orthogonal_transform |
| data_files: |
| - split: train |
| path: preview/orthogonal_transform.parquet |
| - config_name: deformation_recovery |
| data_files: |
| - split: train |
| path: preview/deformation_recovery.parquet |
| - config_name: super_resolution |
| data_files: |
| - split: train |
| path: preview/super_resolution.parquet |
| - config_name: colorization |
| data_files: |
| - split: train |
| path: preview/colorization.parquet |
| - config_name: denoising |
| data_files: |
| - split: train |
| path: preview/denoising.parquet |
| - config_name: masked_inpainting |
| data_files: |
| - split: train |
| path: preview/masked_inpainting.parquet |
| --- |
| |
| # Pretext-270K |
|
|
| Pretext-270K is the first-stage training data of |
| [ThinkMorph-SSL-Base](https://huggingface.co/ThinkMorph/ThinkMorph-SSL-Base). It holds 270,896 training traces |
| and 1,600 validation traces. Each trace starts from a corrupted input image. A description |
| follows, then the restored image, then a further description. There is no question and no |
| answer. |
|
|
| The traces are grouped into seven tasks, one per corruption that the model undoes. |
|
|
| ## Tasks |
|
|
| | Task | Input image | Restored image | Train rows | Val rows | Of these, text only | |
| | --- | --- | --- | ---: | ---: | ---: | |
| | `jigsaw` | shuffled tiles: one image cut into tiles, or separate tile images | reassembled image | 52,975 | 571 | 10,266 | |
| | `orthogonal_transform` | rotated or flipped image | upright image | 36,763 | 183 | — | |
| | `deformation_recovery` | geometrically warped image | unwarped image | 36,672 | 189 | — | |
| | `super_resolution` | low-resolution image | high-resolution image | 36,409 | 179 | — | |
| | `colorization` | grayscale image | colored image | 36,104 | 168 | — | |
| | `denoising` | noisy image | clean image | 36,023 | 155 | — | |
| | `masked_inpainting` | image with masked regions | completed image | 35,950 | 155 | — | |
| | **Total** | | | **270,896** | **1,600** | **10,266** | |
|
|
| Stage 1 trained on the train rows only. 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 533,366 images (141 GB) in 133 tar shards | |
| | `shards/SHARDS.json` | size and SHA256 of every shard | |
| | `text_only/` | 10,266 rows whose images (Matterport3D) 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/Pretext-270K --repo-type dataset --include "shards/*" --local-dir Pretext-270K |
| mkdir -p data |
| for f in Pretext-270K/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 |
| colorization/train.jsonl |
| colorization/val.jsonl |
| ... one folder per task |
| ``` |
|
|
| ```json |
| {"id": "<sha256>", "task": "colorization", "images": ["../images/<sha256>.jpg"], |
| "reasoning_thought_0": "...", "reasoning_image_0": "../images/<sha256>.jpg", |
| "reasoning_thought_1": "..."} |
| ``` |
|
|
| - `images` is the corrupted input. `reasoning_image_0` is the restored image. |
| - Image paths are relative to the row's file. `id` is a stable public id. |
|
|
| ## Rows without images |
|
|
| `text_only/` holds the rows of the `jigsaw` task whose images come from Matterport3D. 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. |
|
|
| ## 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. |
|
|
