v8 final: RTM video re-extract (30,956 peak-contact frames); add Balance metrics section (H̃=0.95 domain, 8,459 unique objects); refresh composition/pies charts + RTM sample grid; publish balance_report.json

README.md

@@ -76,14 +76,26 @@ configs:
         path: sim_starstruck/train-*.parquet
       - split: test
         path: sim_starstruck/test-*.parquet
+  - config_name: unit
+    data_files:
+      - split: train
+        path: unit/train-*.parquet
+  - config_name: tacquad
+    data_files:
+      - split: data_indoor
+        path: tacquad/data_indoor-*.parquet
+      - split: data_outdoor
+        path: tacquad/data_outdoor-*.parquet
+      - split: data_fine
+        path: tacquad/data_fine-*.parquet
 ---
 
 # GelSight Mini Pretrain
 
 ![overview](assets/combined_overview.png)
 
-A unified, parquet-native collection of **~717 K [GelSight Mini](https://www.gelsight.com/gelsightmini/) tactile RGB frames** for self-supervised representation learning. **Ten** public datasets are aggregated under one schema, each filtered through a unified area+intensity contact rule with **per-domain thresholds** (I_min = 12 for real subsets, I_min = 10 for sim subsets, 1.5 % background-diversity keep rate) + per-capture phash dedupe:
-- **~338 K real-world frames** from 8 sources (FoTA labeled+unlabeled, 3DCal, FEATS, GelSLAM, TactileTracking, Real Tactile MNIST, FeelAnyForce)
+A unified, parquet-native collection of **~830K [GelSight Mini](https://www.gelsight.com/gelsightmini/) tactile RGB frames** for self-supervised representation learning. **Ten** public datasets are aggregated under one schema, each filtered through a unified area+intensity contact rule with **per-domain thresholds** (I_min = 12 for real subsets, I_min = 10 for sim subsets, 1.5 % background-diversity keep rate) + per-capture phash dedupe:
+- **~536K real-world frames** from 10 sources (FoTA labeled+unlabeled, 3DCal, FEATS, GelSLAM, TactileTracking, Real Tactile MNIST, FeelAnyForce, UniT, TacQuad)
 - **400 K simulated frames** from 2 Mini-calibrated Taxim renders (Sim Tactile MNIST, Sim Starstruck)
 - Plus a companion **CC-BY-NC extension** with another **~15 K** real frames from Meta Sparsh / FAF force-estimation
 
@@ -202,20 +214,53 @@ Per-channel pixel-value distribution comparing **kept** vs **rejected** frames f
 
 ![pixel value distribution](assets/pixel_value_distribution.png)
 
+
+<!-- BALANCE_SECTION -->
+## Balance metrics
+
+We report two complementary scores along four bucket axes — **domain**
+(real/sim), **sensor_id** (13 distinct physical sensor configurations),
+**object_id** (every unique object instance), and **gel_variant**
+(markered/markerless):
+
+- **Normalized Shannon entropy** `H̃ = H/log(B) ∈ [0,1]`. Higher = more
+  uniform across buckets.
+- **Effective Sample Size** `ESS = (Σn)²/Σn²`. Effective number of
+  equally-weighted buckets — `100%` of B means perfectly uniform.
+
+| Axis | B (buckets) | H̃ | ESS | ESS / B |
+|---|---:|---:|---:|---:|
+| domain | 2 | **0.95** | 2 | 94% |
+| sensor | 13 | 0.69 | 5 | 35% |
+| gel variant | 2 | 0.38 | 1 | 58% |
+| object | **8,459** | **0.78** | 239 | 2.8% |
+| 4-tuple bucket | 8,478 | 0.79 | 271 | 3.2% |
+
+The dataset has **~63/37 real/sim** balance (H̃ = 0.95 along the domain
+axis) and covers **8,459 unique object instances** across 13 physical
+sensor configurations. The gel-variant axis is skewed toward markerless
+(92.5% vs 7.5% markered) because most upstream sources only release
+markerless captures.
+
+See `assets/balance_report.json` for the full bucket histograms and
+per-axis distributions.
+
 ## Composition
 
 |       Subset      | Source dataset                          | Frames    | Gel        | Has labels                              |
 |-------------------|------------------------------------------|----------:|------------|------------------------------------------|
 | `fota_labeled`    | FoTA — *panda_warped* still captures    | **29,494** (66% markerless, 34% markered) | mixed¹ | end-effector x,y,z + quaternion |
-| `fota_unlabeled`  | FoTA — same captures, video frames      | **66,761** train-only (stride-subsampled from 516K — see ³ below) | mixed¹ | object name only            |
+| `fota_unlabeled`  | FoTA — same captures, video frames      | **266,761** train-only (re-sampled with looser dedupe; see ³) | mixed¹ | object name only            |
 | `threedcal`       | py3DCal sphere indentation grid          | **36,270**| markerless | probe x, y, penetration depth (mm)       |
 | `feats`           | FEATS indentation with force grids       | **16,711**| **markered** (two gel variants — see below) | indenter shape/size + contact forces |
 | `gelslam`         | GelSLAM tactile SLAM tracking + reconstruction | **89,612** (28K tracking + 61K reconstruction) | markerless | episode + object name |
 | `tactile_tracking`| TactileTracking (NormalFlow) 6DoF pose tracking | **1,605** | markerless | object + trial id |
-| `real_tactile_mnist` | Real Tactile MNIST 3D-printed digit touches | **56,723** (46K train + 11K test, I_min=12) | markerless | digit class (0–9) + round id |
+| `real_tactile_mnist` | Real Tactile MNIST 3D-printed digit touches | **30,956** (26K train + 5K test; re-extracted from **video upstream** with peak-contact-per-touch picker, I_min=12) | markerless | digit class (0–9) + print id |
 | `feelanyforce`    | FeelAnyForce force-controlled indentations | **50,997** | markerless² | object name |
-| `sim_tactile_mnist` | **SIM** · Taxim-rendered Mini imagery of digit touches | **185,745** (95K train + 91K test, I_min=10) | markerless | digit class + episode |
-| `sim_starstruck`  | **SIM** · Taxim-rendered Mini imagery of star objects | **192,936** (146K train + 47K test, I_min=10) | markerless | episode |
+| `sim_tactile_mnist` | **SIM** · Taxim-rendered Mini imagery of digit touches | **150,601** (102K train + 49K test; raw upstream + stride=2 + I_min=10) | markerless | digit class + episode |
+| `sim_starstruck`  | **SIM** · Taxim-rendered Mini imagery of star objects | **166,104** (150K train + 16K test; raw upstream + stride=3 + I_min=10) | markerless | episode |
+| `unit`            | UniT continuous 3D-pose tracking      | **11,340** | markerless | 3D-pose target (x,y,z,yaw) |
+| `tacquad`         | TacQuad quad-sensor benchmark (Mini stream) | **12,195** (5K indoor + 4K outdoor + 3K fine, 181 objects) | markerless | object name + environment |
 
 ¹ FoTA used **different gels on the two gripper fingers** for many of its captures. Approximately 36 of 124 captures use a markered gel on the right finger and a markerless gel on the left; the remaining 88 captures use markerless gels on both. The per-row `markered` column was set by averaging ~50 frames per capture and counting visible dark dots in the mean image (threshold ≥10 dots). Use it to filter:
 

assets/balance_report.json

@@ -0,0 +1,68 @@
+{
+  "total_rows": 852868,
+  "by_source": {
+    "feats": 16969,
+    "feelanyforce": 48197,
+    "fota_labeled": 26394,
+    "fota_unlabeled": 266761,
+    "gelslam": 114019,
+    "real_tactile_mnist": 30956,
+    "sim_starstruck": 166104,
+    "sim_tactile_mnist": 150601,
+    "tacquad": 12195,
+    "tactile_tracking": 2408,
+    "3dcal": 6924,
+    "unit": 11340
+  },
+  "by_domain": {
+    "real": 536163,
+    "sim": 316705
+  },
+  "by_sensor": {
+    "mini_feats_main": 16276,
+    "mini_feats_new_gel": 348,
+    "mini_feats_old_sensor": 345,
+    "mini_feelanyforce": 48197,
+    "mini_fota_left": 134082,
+    "mini_fota_right": 159073,
+    "mini_gelslam": 114019,
+    "mini_rtm": 30956,
+    "sim_taxim_mini": 316705,
+    "mini_tacquad": 12195,
+    "mini_tracking": 2408,
+    "mini_3dcal": 6924,
+    "mini_unit": 11340
+  },
+  "by_gel": {
+    "markered": 63586,
+    "markerless": 789282
+  },
+  "n_unique_objects": 8459,
+  "n_buckets": 8478,
+  "metrics": {
+    "source": [
+      0.7643821946760945,
+      5.2318542960121865
+    ],
+    "domain": [
+      0.9516963141867442,
+      1.8757988922111002
+    ],
+    "sensor": [
+      0.6937418667986375,
+      4.533059180517704
+    ],
+    "gel": [
+      0.38269839905467906,
+      1.1600846713447743
+    ],
+    "object": [
+      0.7795374766316283,
+      239.25451718000954
+    ],
+    "bucket": [
+      0.7889215949718474,
+      270.8001303884341
+    ]
+  }
+}

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