README: pushT 09-12 (55 seg / 59.5 min); real download sizes; name old_data/

README.md

@@ -44,7 +44,7 @@ configs:
 
 Dense, contact-rich, synchronized multimodal interaction data collected from **human hands holding handheld GelSight tactile sensors** (no robot arm). Intended for **tactile-visual dynamics / world-model learning**.
 
-> **173 min · 312 k frames @ 30 Hz · 3× RGB + 2× GelSight + 2× wrist + OptiTrack · 3 tasks**
+> **200 min · 361 k frames @ 30 Hz · 3× RGB + 2× GelSight + 2× wrist + OptiTrack · 3 tasks**
 
 ![dataset statistics](assets/stats_wrist_era.png)
 
@@ -61,15 +61,28 @@ training on `data/<task>/`.
 | Task | Source recordings | Published segments | Duration | Median contact force |
 |---|---|---|---|---|
 | **motherboard** | 12 | 25 | 93.9 min | 2.94 N |
-| **pushT** | 10 | 31 | 32.5 min | 4.29 N |
+| **pushT** | 20 | 55 | 59.5 min | 2.94 N |
 | **rope** | 10 | 27 | 47.1 min | 1.38 N |
-| *(total)* | *32* | *83* | *173.5 min* | |
+| *(total)* | *42* | *107* | *200.5 min* | |
 
-`data/validation/` holds a separate 5-segment / 7.1 min set from an
+Two of those medians are the same number because they are the *same output
+value*: the force calibration ends in an isotonic regression, so it emits a
+few thousand discrete levels and 2.94 N is a heavily populated one. See
+[Known limits](#known-limits).
+
+`data/validation/` holds a separate 9-segment / 25.2 min set from an
 earlier session (2026-09-09) that carries **different wrist cameras and a
 different calibration epoch**. It is not a random held-out split — training on
 `data/<task>` and evaluating there measures domain shift. See its own README.
 
+**`old_data/` is a separate tree at the repo root**, not part of `data/`. It
+holds the pre-2026-09 sessions (motherboard 2026-05, pushT 2026-06), which
+have no wrist camera, use an earlier calibration epoch, and — unlike
+everything under `data/` — are **not cut to their clean spans**, so a reader
+who ignores the `bad_frames.json` beside them will train on flagged frames.
+It is 36.5 GB, most of it depth; see [`old_data/README.md`](old_data/README.md)
+and the download table below before cloning the repo whole.
+
 ![earlier session](assets/stats_arducam_session.png)
 
 ## Known limits
@@ -81,15 +94,16 @@ Read these before using the force channel or the tactile stream.
 anything above the fitted range is clipped to 7.87 N. Affected fraction of
 **contact** frames:
 - `motherboard` — 8.3 %
-- `pushT` — 8.4 %
+- `pushT` — 6.8 %
 - `rope` — 0.6 %
+- `validation` — 2.6 %
 
 Treat those frames as **right-censored**, not as measurements. The force labels
 also carry roughly **1 N of held-out error**, so do not compare models at 0.1 N
 resolution.
 
 **Tactile frames repeat.** The GelSight Mini tops out near 17.8 Hz while rows
-are written at 29.8 Hz, so only about **56–58 %** of rows are a fresh sensor
+are written at 29.8 Hz, so only about **54–57 %** of rows are a fresh sensor
 reading. `tactile_{left,right}_is_new` marks which. Train tactile dynamics on
 the flagged rows, not on all of them.
 
@@ -98,7 +112,7 @@ streams actually run at 29.80 Hz.
 
 ## Format — LeRobot-style video release
 
-Each episode ships as **7 MP4 video streams** (640×480, H.264) + a **per-frame parquet** of poses and contact metrics. This matches how LeRobot / DROID / Open X-Embodiment ship manipulation data: tiny on disk (data/ ≈ 21 GB vs ~1.9 TB raw), random-access decodable, training-ready.
+Each episode ships as **7 MP4 video streams** (640×480, H.264) + a **per-frame parquet** of poses and contact metrics. This matches how LeRobot / DROID / Open X-Embodiment ship manipulation data: tiny on disk (`data/` is 16.8 GB without depth, against ~1.9 TB of raw recordings), random-access decodable, training-ready.
 
 ```
 data/<task>/
@@ -150,9 +164,14 @@ data/<task>/depth/<date>/episode_NNN/depth_{left,middle,right}.mkv
 | Task | Dates | Source recordings | Published segments | Duration | Calibration epoch |
 |---|---|---|---|---|---|
 | **motherboard** | 2026-09-11, 2026-09-12 | 12 | 25 | 93.9 min | 2026-09-09 |
-| **pushT** | 2026-09-10, 2026-09-11 | 10 | 31 | 32.5 min | 2026-09-09 |
+| **pushT** | 2026-09-10, 2026-09-11, 2026-09-12 | 20 | 55 | 59.5 min | 2026-09-09 |
 | **rope** | 2026-09-11 | 10 | 27 | 47.1 min | 2026-09-09 |
-| **validation** | 2026-09-09 | 5 | 9 | 25.2 min | 2026-09-09 |
+| **validation** | 2026-09-09 | 6 † | 9 | 25.2 min | 2026-09-09 |
+
+† `validation` holds two sessions of the same date and the recorder reuses
+episode numbers within a date, so its `source_recording` strings collide —
+four distinct names for six recordings. The count above comes from its own
+README, not from the metadata, which cannot express it.
 
 All currently published sessions share the **2026-09-09** extrinsics. Camera
 extrinsics are used only for the projection overlay; **stored poses are
@@ -165,25 +184,43 @@ using it.
 
 See [`tasks.json`](tasks.json) for the machine-readable registry.
 
-## Downloading — depth is optional
+## Downloading — the whole repo is 57.5 GB; you almost certainly want less
 
-The dataset splits into a **lightweight core** (RGB + tactile + wrist + poses, ~21 GB) and an **optional depth tree** (`data/validation/depth/`, ~6 GB lossless; only the 2026-09-09 session has depth). Depth lives in its own subtree so you can skip it entirely.
+Two things make the full clone much larger than the training data: depth
+(**36.1 GB**, lossless 16-bit) and `old_data/` (**36.5 GB**, of which 31.9 GB
+is depth). Both are opt-in subtrees, and skipping them is one argument.
+
+| What you ask for | Size |
+|---|---|
+| `data/` only, no depth — **the training data** | **16.8 GB** |
+| `data/` only, with depth | 21.0 GB |
+| everything except depth | 21.4 GB |
+| `old_data/` only, no depth | 4.6 GB |
+| everything | 57.5 GB |
 
 ```python
 from huggingface_hub import snapshot_download
 
-# Core only — RGB + tactile + wrist + parquet, NO depth (~21 GB)
+# The training data — RGB + tactile + wrist + parquet, no depth (16.8 GB)
+snapshot_download("yxma/React", repo_type="dataset",
+                  allow_patterns=["data/*"], ignore_patterns=["*/depth/*"])
+
+# One task (motherboard is the largest, at 9.5 GB)
 snapshot_download("yxma/React", repo_type="dataset",
+                  allow_patterns=["data/motherboard/*"],
                   ignore_patterns=["*/depth/*"])
 
-# Everything including depth (~27 GB)
+# Everything, including old_data/ and 36 GB of depth (57.5 GB)
 snapshot_download("yxma/React", repo_type="dataset")
-
-# One task only
-snapshot_download("yxma/React", repo_type="dataset",
-                  allow_patterns=["data/motherboard/*"], ignore_patterns=["*/depth/*"])
 ```
 
+Per task, without depth: motherboard 9.5 GB, pushT 3.1 GB, rope 2.7 GB,
+validation 1.4 GB.
+
+**Depth** is 16-bit lossless FFV1 and exists for two sessions only:
+`data/validation/depth/` (4.2 GB) and `old_data/*/depth/` (31.9 GB). No
+2026-09-10 or later session has depth.
+
 Or use the helper: `python examples/download.py --no-depth` (see [`examples/download.py`](examples/download.py)).
 
 The `ReactVideoDataset` loader **never touches depth unless you pass `load_depth=True`**, so depth-free training requires no depth download.