Add SOURCES.md — per-subset intro, processing, stats, and 100-image samples

SOURCES.md

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+# Source Datasets
+
+This document describes the **8 public datasets** aggregated into
+`yxma/gelsight-mini-pretrain`. For each source you'll find:
+
+- a one-paragraph intro
+- the paper / project page / canonical download
+- the upstream license
+- the upstream format and how we transformed it
+- the resulting parquet stats
+- a 100-image sample grid (10×10, central crop to square, ~144 px thumbs)
+
+A high-level summary is at the bottom in [§ Aggregate statistics](#aggregate-statistics).
+
+The full schema and load-time examples are in the main [README](README.md);
+the conversion script is at [`scripts/make_parquet_v2.py`](scripts/make_parquet_v2.py).
+
+![overview](assets/combined_overview.png)
+
+---
+
+## 1 · FoTA — Foundation Tactile (`fota_labeled` + `fota_unlabeled`)
+
+**Intro.** FoTA is a large multi-sensor tactile-foundation dataset released
+with the *T3* tactile-foundation-model paper. Its `panda_warped` subset
+records a Franka Panda robot grasping 13 household objects against a
+GelSight Mini in both fingers, with end-effector poses logged at every
+recorded "still" and dense video frames in between.
+
+**Source release.**
+- 📄 Paper · *Towards a Tactile Foundation Model* — Zhao et al., 2024.
+  [arXiv:2406.13640](https://arxiv.org/abs/2406.13640)
+- 🤗 [`alanz-mit/FoundationTactile`](https://huggingface.co/datasets/alanz-mit/FoundationTactile)
+- 🐙 [github.com/alanzjl/t3](https://github.com/alanzjl/t3)
+- 📜 License: **MIT**
+
+**Original format.** WebDataset (`.tar`) shards mixing per-sensor frames
+with JSON metadata. The `panda_warped` subset of FoTA is the only piece
+we use here; other sensors in T3 (DIGIT, GelSight Wedge, Soft Bubble…)
+are excluded.
+
+**How we processed it.**
+1. Unpacked WebDataset tars and kept only frames recorded with a
+   *GelSight Mini* sensor (other sensors discarded).
+2. Re-encoded each frame as JPEG quality 92.
+3. **Marker classification.** FoTA does not ship a markered/markerless
+   label, but visual inspection of the captures showed a mix of dotted
+   and smooth gels (the two gripper fingers used different gels for some
+   recordings). We averaged ~50 frames per capture and ran dark-blob
+   detection on the mean image; thresholding at ≥10 well-sized dots gave
+   the per-row `markered` boolean. Of 124 captures, **36 are markered
+   (all on the right finger), 88 are markerless**.
+4. Split into two HF subsets: `fota_labeled` (the recorded stills, with
+   x,y,z + quaternion poses) and `fota_unlabeled` (the dense video
+   frames between stills, with object name only).
+
+**Stats after processing.**
+
+| Subset           | Frames  | Resolution | Markered / Markerless | Splits           |
+|------------------|--------:|------------|----------------------:|------------------|
+| `fota_labeled`   | 29,494  | 640 × 480  | 10,025 / 19,469       | train 23,588 · val 5,906 |
+| `fota_unlabeled` | 516,523 | 640 × 480  | 190,816 / 325,707     | train 413,193 · val 103,330 |
+
+13 distinct contact objects, 5 initial-pose indices, 15,148 unique
+(object, pose, side) captures. End-effector range
+`x ∈ [−25.7, 129.1] mm`, `y ∈ [−137.3, 137.3] mm`,
+`z ∈ [−38.6, 38.6] mm`.
+
+**100 random samples** (`fota_labeled`, mixed gel):
+
+![fota_labeled](assets/samples_100_fota_labeled.png)
+
+**100 markered + 100 markerless** (`fota_labeled`):
+
+| markered (right finger) | markerless (left finger) |
+|:---:|:---:|
+| ![fota markered](assets/samples_100_fota_labeled_markered.png) | ![fota markerless](assets/samples_100_fota_labeled_markerless.png) |
+
+**100 random samples** (`fota_unlabeled`):
+
+![fota_unlabeled](assets/samples_100_fota_unlabeled.png)
+
+---
+
+## 2 · py3DCal — sphere-indentation calibration grid (`threedcal`)
+
+**Intro.** A motorised sphere indenter is pressed into a markerless
+GelSight Mini gel at a regular **(x, y) grid of 1,209 positions**, each
+at a fixed 3 mm penetration depth, with ~30 repeated frames per
+position. Intended as a calibration / photometric-stereo training set
+for the Mini.
+
+**Source release.**
+- Kota, Shah, Colgate, Reardon (2025).
+- 💾 [Zenodo 18462608](https://zenodo.org/records/18462608)
+- 📜 License: **CC-BY-4.0**
+
+**Original format.** Loose PNGs in pose-named folders (e.g.
+`x_010_y_006_z_3/0001.png`). PNG resolution is the GelSight Mini
+low-resolution 320 × 240 mode.
+
+**How we processed it.**
+1. Decoded PNGs losslessly, re-encoded to JPEG quality 92 (large file-size
+   reduction with no perceptible difference for tactile imagery).
+2. Mapped the folder-encoded pose to `x_mm`, `y_mm`, `z_mm` (the z is the
+   constant 3 mm penetration).
+3. Tagged `markered=False` (gel is smooth).
+4. Single `train` split.
+
+**Stats after processing.**
+
+| Subset      | Frames | Resolution | Markered | Probe coverage |
+|-------------|-------:|------------|---------:|----------------|
+| `threedcal` | 36,270 | 320 × 240  | 0        | 1,209 grid positions |
+
+`x ∈ [0, 19] mm`, `y ∈ [0, 15] mm`, fixed `z = 3 mm`, ~30 frames per
+position.
+
+**100 random samples:**
+
+![threedcal](assets/samples_100_threedcal.png)
+
+**Probe coverage heatmap:**
+
+![threedcal coverage](assets/threedcal_coverage.png)
+
+---
+
+## 3 · FEATS — Force Estimation for Tactile Sensors (`feats`)
+
+**Intro.** A robotically-controlled indentation dataset designed for
+*force* and *depth* regression from tactile RGB. Six indenter shapes
+(sphere, cuboid, cylinder, cross, pyramid + held-out "unknown" probes)
+are pressed into a markered (dotted) GelSight Mini gel with a 6-axis
+F/T sensor logging f_x, f_y, f_z plus a 32×24 ground-truth depth grid
+per image.
+
+**Source release.**
+- 📄 Author: Erik Helmut (2025).
+- 🤗 [`erikhelmut/FEATS`](https://huggingface.co/datasets/erikhelmut/FEATS)
+- 📜 License: **MIT**
+
+**Original format.** `.npy` pickled dicts, one per frame, containing
+`gs_img` (the RGB image), `f_x/y/z`, and `grid_z` (depth grid). Six
+splits including out-of-distribution test sets.
+
+**How we processed it.**
+1. Loaded each `.npy`, extracted `gs_img`, re-encoded as JPEG q=92.
+2. Recorded `f_x`, `f_y`, `f_z`, `grid_z_max`, `grid_z_mean` per row.
+3. Parsed filename stem (e.g. `113_cuboid_12`) into
+   `indenter="cuboid"`, `indenter_param="12"`.
+4. **Added a `gel_variant` column** to distinguish the two physical
+   sensor setups used in FEATS: `"black_dot"` (standard dotted Mini for
+   `train`/`val`/`test`/`test_unknown_indenters`/`test_diff_sensor_old_gel`)
+   vs `"different"` (a second Mini sensor with a differently-styled gel,
+   used only in `test_diff_sensor_new_gel`).
+5. **Removed empty frames.** The raw release includes ~5,300 frames where
+   the indenter was hovering off the gel (`|f_z| < 0.5 N`). These are
+   filtered out here; original 22,013 rows → kept 16,711.
+
+**Stats after processing.**
+
+| Split                          | Frames |
+|--------------------------------|-------:|
+| `train`                        | 11,415 |
+| `test_unknown_indenters`       | 2,581  |
+| `test`                         | 1,342  |
+| `val`                          | 693    |
+| `test_diff_sensor_new_gel`     | 341    |
+| `test_diff_sensor_old_gel`     | 339    |
+| **Total**                      | **16,711** |
+
+Normal-force range `f_z ∈ [−73.3, 0.0] N` (mean −9.30 N, std 10.66 N);
+shear `f_x ∈ [−4.86, 4.86] N`, `f_y ∈ [−5.89, 5.87] N`.
+
+**100 random samples:**
+
+![feats](assets/samples_100_feats.png)
+
+**Force distribution and indenter mix:**
+
+![feats force](assets/force_distribution.png)
+
+---
+
+## 4 · GelSLAM — tactile SLAM tracking + reconstruction (`gelslam`)
+
+**Intro.** A real-time tactile SLAM dataset from CMU's RPL. Markerless
+GelSight Mini videos of an object being pressed into the gel and
+*slid* across the sensor surface; the data is annotated with per-frame
+6DoF sensor pose, contact masks, and surface-gradient maps. Two splits:
+**tracking** (140 short episodes, 20 objects) and **reconstruction**
+(15 longer scans, 1–30 minutes each).
+
+**Source release.**
+- 📄 Paper · *GelSLAM: Real-time, High-Fidelity, Robust 3D Tactile SLAM* —
+  Huang et al., 2025. [arXiv:2508.15990](https://arxiv.org/abs/2508.15990)
+- 🤗 [`joehjhuang/GelSLAM_dataset`](https://huggingface.co/datasets/joehjhuang/GelSLAM_dataset)
+- 🐙 [github.com/rpl-cmu/gelslam](https://github.com/rpl-cmu/gelslam)
+- 📜 License: **MIT**
+
+**Original format.** Single `dataset.zip` (~73 GB). Each episode is a
+folder containing `gelsight.avi` (the RGB tactile video, ~25 FPS),
+`true_start_T_currs.npy` (per-frame 4×4 pose), `contact_masks.npy`,
+`gradient_maps.npy`. Reconstruction objects similarly have
+`gelsight.avi` + `config.yaml`.
+
+**How we processed it.**
+1. Decoded all 155 `gelsight.avi` files frame-by-frame with OpenCV.
+2. **Validity filter** — per-episode baseline = median of the first 10
+   frames (typically the no-contact prologue); kept any frame whose
+   central-region deformation from baseline ≥ 4 grey levels. This drops
+   pre- and post-contact frames where the sensor wasn't yet pressed
+   against an object.
+3. **Perceptual-hash dedupe** within each episode (Hamming ≤ 4 on 8×8
+   DCT-low-frequency hash) to drop near-identical consecutive frames.
+4. Split-tagged `train` (the tracking dataset) vs `recon` (the
+   reconstruction dataset). Per-row `episode` and `frame_idx` columns
+   are populated.
+
+Kept rate: **295,525 raw → 60,982** (20.6%). The pre/post-contact
+motion in tracking episodes accounts for most of the drops.
+
+**Stats after processing.**
+
+| Split   | Frames | Description |
+|---------|-------:|-------------|
+| `train` | 27,763 | 140 tracking episodes across 20 objects |
+| `recon` | 33,219 | 15 long-form reconstruction scans |
+
+**100 random samples:**
+
+![gelslam](assets/samples_100_gelslam.png)
+
+---
+
+## 5 · TactileTracking / NormalFlow — 6DoF pose tracking (`tactile_tracking`)
+
+**Intro.** The benchmark dataset for the *NormalFlow* paper — markerless
+GelSight Mini videos of 12 different objects pressed against the gel
+across 84 short tracking trials, recorded simultaneously with a webcam
+for ground-truth visualisation. Used to evaluate contact-based 6DoF
+pose tracking.
+
+**Source release.**
+- 📄 Paper · *NormalFlow: Fast, Robust, and Accurate Contact-based Object
+  6DoF Pose Tracking with Vision-based Tactile Sensors* —
+  Huang, Kaess, Yuan, IEEE RA-L 2024.
+  [DOI 10.1109/LRA.2024.3505815](https://doi.org/10.1109/LRA.2024.3505815)
+- 🤗 [`joehjhuang/TactileTracking`](https://huggingface.co/datasets/joehjhuang/TactileTracking)
+- 🐙 [github.com/rpl-cmu/normalflow](https://github.com/rpl-cmu/normalflow)
+- 📜 License: **MIT**
+
+**Original format.** Single `dataset.zip` (~7 GB) with 84 trial folders
+(e.g. `corner3`, `hammer1`, …). Each contains `gelsight.avi`,
+`webcam.avi`, `true_start_T_currs.npy`, `contact_masks.npy`,
+`gradient_maps.npy`. We use only the GelSight video.
+
+**How we processed it.**
+1. Parsed the trial folder name (e.g. `corner3` → object `corner`, trial
+   `3`) and decoded the GelSight video.
+2. Same validity filter (median-of-first-10 baseline, τ = 4) and
+   perceptual-hash dedupe as GelSLAM.
+3. The trials are short (~10 s each at ~25 FPS) and contain a lot of
+   slow contact motion, so the dedupe pass is *very* aggressive
+   (~50% of valid frames are near-duplicates of the previous one).
+
+Kept rate: **7,386 raw → 1,143** (15.5%) — the smallest subset, but
+visually distinct from the others (varied real-world contact objects).
+
+**Stats after processing.**
+
+| Subset            | Frames | Resolution | Unique objects |
+|-------------------|-------:|------------|---------------:|
+| `tactile_tracking`| 1,143  | 320 × 240  | 12             |
+
+**100 random samples:**
+
+![tactile_tracking](assets/samples_100_tactile_tracking.png)
+
+---
+
+## 6 · Real Tactile MNIST — 3D-printed digit touches (`real_tactile_mnist`)
+
+**Intro.** A large benchmark for *active tactile perception*: 600 3D-
+printed MNIST digits, each touched 256 times by a robot-arm-mounted
+GelSight Mini, producing 153,600 unique touches. Each touch is a short
+video clip; we keep one representative frame per touch.
+
+**Source release.**
+- 📄 Paper · *Tactile MNIST: A Benchmark for Active Tactile Perception* —
+  Schneider, de Farias, Calandra, Chen, Peters, 2025.
+  [arXiv:2506.06361](https://arxiv.org/abs/2506.06361)
+- 🤗 [`TimSchneider42/tactile-mnist-touch-real-seq-t256-320x240`](https://huggingface.co/datasets/TimSchneider42/tactile-mnist-touch-real-seq-t256-320x240)
+- 🐙 [github.com/TimSchneider42/tactile-mnist](https://github.com/TimSchneider42/tactile-mnist)
+- 🌐 [Project page](https://sites.google.com/robot-learning.de/tactile-mnist/)
+- 📜 License: **CC-BY-2.0** (code is MIT)
+
+**Original format.** Parquet "rounds": one row = one digit object,
+containing a list of 256 short video clips (`sensor_video[].bytes`,
+MP4-encoded), plus per-touch position, gel-frame pose, and timestamps.
+
+**How we processed it.**
+1. For each touch video clip, decoded with OpenCV and kept the **middle
+   frame** (near peak contact, where the imprint is most informative).
+2. No empty-frame filtering needed — every middle frame is by
+   construction at peak contact.
+3. No dedupe — each touch is a unique digit at a unique position.
+4. Per-row metadata: `digit_class` (0–9), `episode` (which of the 600
+   physical digit objects), `obj_name="digit_<N>"`.
+
+Kept rate: **153,600 raw → 153,600** (100%).
+
+**Stats after processing.**
+
+| Split    | Frames  | Resolution |
+|----------|--------:|------------|
+| `train`  | 128,000 | 320 × 240  |
+| `test`   | 25,600  | 320 × 240  |
+
+**100 random samples:**
+
+![rtm](assets/samples_100_real_tactile_mnist.png)
+
+**Digit-class balance:**
+
+![rtm digits](assets/rtm_digit_distribution.png)
+
+---
+
+## 7 · FeelAnyForce — force-controlled indentations (`feelanyforce`)
+
+**Intro.** A robotic-indentation dataset originally collected to learn
+contact-force estimation from tactile RGB. 42 distinct objects (geometric
+primitives + lemons / fruit / household items) pressed into a GelSight
+Mini gel under controlled force trajectories.
+
+**Source release.**
+- 📄 Sharei et al., 2024 (paper title: *FeelAnyForce*).
+- 🤗 [`amirsh1376/FeelAnyForce`](https://huggingface.co/datasets/amirsh1376/FeelAnyForce)
+- 📜 License: **CC-BY-4.0**
+
+**Note on gel variant.** Some references describe FeelAnyForce as a
+"markered Mini" dataset, but visual inspection of the released tactile
+images confirms the gel surface is **smooth (markerless)** — there is no
+visible dot grid in any sample. We label it `markered=False` to reflect
+what is actually in the data.
+
+**Original format.** Multi-part zip archive (`dataset.zip` + `.z01` +
+`.z02` + `.z03` + `dataset_part_a/b/c`, reassembled to ~82 GB extracted).
+Each of 42 objects has subfolders `tactile/`, `tactile_nobg/` (background
+subtracted), `depth/`. We use only `tactile/` (raw RGB). Plus three CSVs
+(`TacForce_train/val/test_set.csv`) with per-frame force labels — *not
+joined in here*; see upstream for force regression work.
+
+**How we processed it.**
+1. Iterated all `tactile/*.png` files (320 × 240 PNG).
+2. Re-encoded each as JPEG q=92.
+3. **Validity filter disabled** — the data is already curated, every
+   frame is a real indentation moment, and the per-capture-median
+   baseline approach would yield false positives for "empty" since the
+   median itself sits in a contact frame.
+4. **Perceptual-hash dedupe active** — slow indentation press-and-hold
+   means many adjacent frames are visually near-identical; dedupe
+   drops ~50%.
+
+Kept rate: **101,883 raw → 50,997** (50.1%). Drops are all dedupe; zero
+empty drops.
+
+**Stats after processing.**
+
+| Subset         | Frames  | Resolution | Markered | Unique objects |
+|----------------|--------:|------------|---------:|---------------:|
+| `feelanyforce` | 50,997  | 320 × 240  | 0        | 42             |
+
+**100 random samples:**
+
+![feelanyforce](assets/samples_100_feelanyforce.png)
+
+---
+
+## Aggregate statistics
+
+![composition](assets/composition.png)
+
+| Subset                | Frames  | Bytes (GB) | Resolution | Markered | Markerless |
+|-----------------------|--------:|-----------:|------------|---------:|-----------:|
+| `fota_labeled`        | 29,494  | 0.50       | 640 × 480  | 10,025   | 19,469     |
+| `fota_unlabeled`      | 516,523 | 21.09      | 640 × 480  | 190,816  | 325,707    |
+| `threedcal`           | 36,270  | 0.29       | 320 × 240  | 0        | 36,270     |
+| `feats`               | 16,711  | 0.27       | 320 × 240  | 16,711   | 0          |
+| `gelslam`             | 60,982  | 0.58       | 320 × 240  | 0        | 60,982     |
+| `tactile_tracking`    | 1,143   | 0.01       | 320 × 240  | 0        | 1,143      |
+| `real_tactile_mnist`  | 153,600 | 0.48       | 320 × 240  | 0        | 153,600    |
+| `feelanyforce`        | 50,997  | 0.47       | 320 × 240  | 0        | 50,997     |
+| **Total**             | **865,720** | **~23.7** | mixed | **217,552** | **648,168** |
+
+**Resolution distribution:**
+
+![resolution](assets/resolution_distribution.png)
+
+**Gel-variant pool sizes after aggregation:**
+
+- **Markerless pool** (`fota_labeled[markerless]` + `fota_unlabeled[markerless]` +
+  `threedcal` + `gelslam` + `tactile_tracking` + `real_tactile_mnist` +
+  `feelanyforce`): **648,168 frames**
+- **Markered pool** (`feats` + `fota_labeled[markered]` +
+  `fota_unlabeled[markered]`): **217,552 frames**
+
+Filter examples:
+
+```python
+from datasets import load_dataset, concatenate_datasets
+
+# Big markerless pool for VAE / MAE / contrastive pretraining
+sources = ["fota_unlabeled", "threedcal", "gelslam", "tactile_tracking",
+           "real_tactile_mnist", "feelanyforce"]
+markerless = concatenate_datasets([
+    load_dataset("yxma/gelsight-mini-pretrain", s, split="train"
+                ).filter(lambda r: not r["markered"])
+    for s in sources
+])
+
+# Markered pool
+markered = concatenate_datasets([
+    load_dataset("yxma/gelsight-mini-pretrain", "feats",      split="train"),
+    load_dataset("yxma/gelsight-mini-pretrain", "fota_labeled", split="train"
+                ).filter(lambda r: r["markered"]),
+])
+```
+
+---
+
+## Citation chain
+
+If you use this aggregated release, please cite the **upstream sources**
+for the subsets you use. See the per-subset sections above for paper /
+DOI references; the [main README](README.md) consolidates the BibTeX-
+ready citation list.