v7: I_min=12 (real) + I_min=10 (sim) reprocess of RTM and sims; add pixel_value_distribution chart; fix sim_tactile_mnist test routing; refresh all sample grids and composition charts.

README.md

@@ -68,6 +68,8 @@ configs:
     data_files:
       - split: train
         path: sim_tactile_mnist/train-*.parquet
+      - split: test
+        path: sim_tactile_mnist/test-*.parquet
   - config_name: sim_starstruck
     data_files:
       - split: train
@@ -80,8 +82,8 @@ configs:
 
 ![overview](assets/combined_overview.png)
 
-A unified, parquet-native collection of **~700 K [GelSight Mini](https://www.gelsight.com/gelsightmini/) tactile RGB frames** for self-supervised representation learning. **Eleven** public datasets are aggregated under one schema, each filtered through a unified area+intensity contact rule + per-capture phash dedupe:
-- **~503 K real-world frames** from 9 captures (FoTA labeled+unlabeled, 3DCal, FEATS, GelSLAM, TactileTracking, Real Tactile MNIST, FeelAnyForce, TacQuad-mini)
+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)
 - **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
 
@@ -196,6 +198,10 @@ Real Tactile MNIST · digit-class balance (used as a sanity check that the upstr
 
 ![rtm digits](assets/rtm_digit_distribution.png)
 
+Per-channel pixel-value distribution comparing **kept** vs **rejected** frames for the three subsets that were filtered most recently (real_tactile_mnist with I_min=12; sim_tactile_mnist and sim_starstruck with I_min=10). The red/blue separation per channel confirms the filter discriminates contact-bearing frames from at-rest frames cleanly:
+
+![pixel value distribution](assets/pixel_value_distribution.png)
+
 ## Composition
 
 |       Subset      | Source dataset                          | Frames    | Gel        | Has labels                              |
@@ -206,10 +212,10 @@ Real Tactile MNIST · digit-class balance (used as a sanity check that the upstr
 | `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 | **16,961** (14K train + 2.8K test) | markerless | digit class (0–9) + round 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 |
 | `feelanyforce`    | FeelAnyForce force-controlled indentations | **50,997** | markerless² | object name |
-| `sim_tactile_mnist` | **SIM** · Taxim-rendered Mini imagery of digit touches | **200,000** | markerless | digit class + episode |
-| `sim_starstruck`  | **SIM** · Taxim-rendered Mini imagery of star objects | **200,000** | markerless | episode |
+| `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 |
 
 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:
 
@@ -275,8 +281,8 @@ The TactileTracking benchmark from the [NormalFlow paper](https://github.com/rpl
 
 ![tactile_tracking](assets/samples_40_tactile_tracking.png)
 
-### `real_tactile_mnist`  ·  153,600 frames  ·  markerless  ·  +digit class
-From the [Real Tactile MNIST benchmark](https://arxiv.org/abs/2506.06361) (Schneider et al. 2025) — **600 3D-printed MNIST digits**, each touched 256 times by a robot arm, giving 153,600 touches in total. The upstream release ships each touch as a short video clip; here we keep **one middle-frame per touch video** (near peak contact). The `digit_class` column gives the digit 0–9; the `episode` column gives the print id (which of the 600 physical digits was touched).
+### `real_tactile_mnist`  ·  56,723 frames  ·  markerless  ·  +digit class
+From the [Real Tactile MNIST benchmark](https://arxiv.org/abs/2506.06361) (Schneider et al. 2025) — **600 3D-printed MNIST digits**, each touched 256 times by a robot arm. The upstream release ships each touch as a short video clip; here we keep **one middle-frame per touch video** (near peak contact). After running the unified area+intensity contact filter with **I_min = 12** (calibrated for real-image noise floor; see `assets/pixel_value_distribution.png`), we retain 56,723 frames (28.4 % of the 200K raw frames; ~1.5 % background-diversity keep rate). The `digit_class` column gives the digit 0–9; the `episode` column gives the print id (which of the 600 physical digits was touched).
 
 ![real_tactile_mnist](assets/samples_40_real_tactile_mnist.png)
 
@@ -385,7 +391,13 @@ sim_pool = concatenate_datasets([
 ```
 
 The two sim subsets are from the **same authors as `real_tactile_mnist`**
-(Schneider et al. 2025) and use the same Mini sensor model.
+(Schneider et al. 2025) and use the same Mini sensor model. Sim frames
+are noise-free, so the contact filter is applied with a lower threshold
+**I_min = 10** (vs I_min = 12 / 15 for real subsets), yielding ~93 %
+retention for `sim_tactile_mnist` and ~97 % for `sim_starstruck`. See
+`assets/pixel_value_distribution.png` for the per-channel pixel-value
+histograms showing how the filter discriminates contact vs at-rest
+frames in each subset.
 
 ## Recommended uses
 

real_tactile_mnist/test-00000-of-00001.parquet

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