Datasets:

yxma
/

gelsight-mini-pretrain

@@ -146,12 +146,12 @@ FoTA does **not** ship a markered/markerless label, but on visual inspection the
 | Markered captures (top half) | Markerless captures (bottom half) |
 |:---:|:---:|
-| ![fota markered](assets/samples_100_fota_labeled_markered.png) | ![fota markerless](assets/samples_100_fota_labeled_markerless.png) |
 **4. Empty-frame removal — cleaning FEATS**
 FEATS' raw frames include captures where the indenter was hovering off the gel (no contact at all). These would teach a pretraining model nothing useful. We filtered them with `|f_z| < 0.5 N` (using FEATS's force-sensor ground truth), removing **5,302 frames** out of 22,013. We also added a `gel_variant` column distinguishing the two physical sensor setups used in FEATS (`black_dot` for the main markered gel, `different` for the second sensor used in `test_diff_sensor_new_gel`).
-![feats](assets/samples_100_feats.png)
 <sub>*FEATS samples after empty-frame removal — every frame now shows a real contact.*</sub>
 **5. Tier-2 expansion: adding four more markerless Mini sources**
@@ -229,23 +229,23 @@ One labeled still captured at every recorded end-effector pose along a Franka Pa
 Random sample (mixed):
-![fota_labeled mixed](assets/samples_100_fota_labeled.png)
 Per gel variant:
 | markerless (66% of the data) | markered (34% of the data) |
 |:---:|:---:|
-| ![fota markerless](assets/samples_100_fota_labeled_markerless.png) | ![fota markered](assets/samples_100_fota_labeled_markered.png) |
 ### `threedcal` &nbsp;·&nbsp; 36,270 frames &nbsp;·&nbsp; markerless &nbsp;·&nbsp; +xyz pose
 A motorised sphere indenter is pressed into the gel at **1,209 different (x, y) positions** at a fixed 3 mm depth. The bright spot moves as the probe walks across the sensor surface — useful for learning a calibrated position→appearance mapping.
-![threedcal](assets/samples_100_threedcal.png)
 ### `feats` &nbsp;·&nbsp; 16,711 frames &nbsp;·&nbsp; **markered** &nbsp;·&nbsp; +force
 Six indenter shapes (sphere, cuboid, cylinder, pyramid, cross, plus one "unknown" set of held-out probes), each pressed into a markered (dotted) GelSight Mini gel. Provides f_x, f_y, f_z forces and a 32×24 depth grid per image. Forces span from ~0 N (light touch) to **−73 N** (heavy normal compression).
-![feats](assets/samples_100_feats.png)
 > **⚠️ FEATS has two physical gel variants.** A new column `gel_variant` distinguishes them:
 > - `"black_dot"` — the standard dotted Mini gel used for `train`, `val`, `test`, `test_unknown_indenters`, `test_diff_sensor_old_gel`.
@@ -267,22 +267,22 @@ The GelSLAM dataset of [Huang et al. 2025](https://arxiv.org/abs/2508.15990) —
 Per-frame `frame_idx` and per-row `episode` columns are populated. After empty-frame and dedupe filtering, **kept 21% of raw frames** (the majority of GelSLAM frames are pre/post-contact approach motion, which the validity filter removes).
-![gelslam](assets/samples_100_gelslam.png)
 ### `tactile_tracking` &nbsp;·&nbsp; 1,143 frames &nbsp;·&nbsp; markerless &nbsp;·&nbsp; +per-trial 6DoF
 The TactileTracking benchmark from the [NormalFlow paper](https://github.com/rpl-cmu/normalflow) (Huang et al. 2024) — 84 tracking trials across 12 objects. Aggressively filtered (empty-frame + perceptual-hash dedupe) because the trials are short and most consecutive frames are near-identical. Kept rate after filtering: 15%.
-![tactile_tracking](assets/samples_100_tactile_tracking.png)
 ### `real_tactile_mnist` &nbsp;·&nbsp; 153,600 frames &nbsp;·&nbsp; markerless &nbsp;·&nbsp; +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](assets/samples_100_real_tactile_mnist.png)
 ### `feelanyforce` &nbsp;·&nbsp; 50,997 frames &nbsp;·&nbsp; markerless &nbsp;·&nbsp; +per-indentation object
 The [FeelAnyForce dataset](https://huggingface.co/datasets/amirsh1376/FeelAnyForce) (Sharei et al. 2024) of robotically-controlled indentations against 42 unique objects (cylinders, cubes, spheres, fruits, household items). Aggressively dedupe-filtered (50% of raw frames dropped) because each indentation is a slow press-and-hold, so adjacent frames are visually near-identical. The schema retains the `obj_name` and `episode` columns; the upstream force labels (in `TacForce_train_set.csv` etc.) are not currently joined in — see the [upstream release](https://huggingface.co/datasets/amirsh1376/FeelAnyForce) for forces.
-![feelanyforce](assets/samples_100_feelanyforce.png)
 ## Useful statistics

 | Markered captures (top half) | Markerless captures (bottom half) |
 |:---:|:---:|
+| ![fota markered](assets/samples_40_fota_labeled_markered.png) | ![fota markerless](assets/samples_40_fota_labeled_markerless.png) |
 **4. Empty-frame removal — cleaning FEATS**
 FEATS' raw frames include captures where the indenter was hovering off the gel (no contact at all). These would teach a pretraining model nothing useful. We filtered them with `|f_z| < 0.5 N` (using FEATS's force-sensor ground truth), removing **5,302 frames** out of 22,013. We also added a `gel_variant` column distinguishing the two physical sensor setups used in FEATS (`black_dot` for the main markered gel, `different` for the second sensor used in `test_diff_sensor_new_gel`).
+![feats](assets/samples_40_feats.png)
 <sub>*FEATS samples after empty-frame removal — every frame now shows a real contact.*</sub>
 **5. Tier-2 expansion: adding four more markerless Mini sources**
 Random sample (mixed):
+![fota_labeled mixed](assets/samples_40_fota_labeled.png)
 Per gel variant:
 | markerless (66% of the data) | markered (34% of the data) |
 |:---:|:---:|
+| ![fota markerless](assets/samples_40_fota_labeled_markerless.png) | ![fota markered](assets/samples_40_fota_labeled_markered.png) |
 ### `threedcal` &nbsp;·&nbsp; 36,270 frames &nbsp;·&nbsp; markerless &nbsp;·&nbsp; +xyz pose
 A motorised sphere indenter is pressed into the gel at **1,209 different (x, y) positions** at a fixed 3 mm depth. The bright spot moves as the probe walks across the sensor surface — useful for learning a calibrated position→appearance mapping.
+![threedcal](assets/samples_40_threedcal.png)
 ### `feats` &nbsp;·&nbsp; 16,711 frames &nbsp;·&nbsp; **markered** &nbsp;·&nbsp; +force
 Six indenter shapes (sphere, cuboid, cylinder, pyramid, cross, plus one "unknown" set of held-out probes), each pressed into a markered (dotted) GelSight Mini gel. Provides f_x, f_y, f_z forces and a 32×24 depth grid per image. Forces span from ~0 N (light touch) to **−73 N** (heavy normal compression).
+![feats](assets/samples_40_feats.png)
 > **⚠️ FEATS has two physical gel variants.** A new column `gel_variant` distinguishes them:
 > - `"black_dot"` — the standard dotted Mini gel used for `train`, `val`, `test`, `test_unknown_indenters`, `test_diff_sensor_old_gel`.
 Per-frame `frame_idx` and per-row `episode` columns are populated. After empty-frame and dedupe filtering, **kept 21% of raw frames** (the majority of GelSLAM frames are pre/post-contact approach motion, which the validity filter removes).
+![gelslam](assets/samples_40_gelslam.png)
 ### `tactile_tracking` &nbsp;·&nbsp; 1,143 frames &nbsp;·&nbsp; markerless &nbsp;·&nbsp; +per-trial 6DoF
 The TactileTracking benchmark from the [NormalFlow paper](https://github.com/rpl-cmu/normalflow) (Huang et al. 2024) — 84 tracking trials across 12 objects. Aggressively filtered (empty-frame + perceptual-hash dedupe) because the trials are short and most consecutive frames are near-identical. Kept rate after filtering: 15%.
+![tactile_tracking](assets/samples_40_tactile_tracking.png)
 ### `real_tactile_mnist` &nbsp;·&nbsp; 153,600 frames &nbsp;·&nbsp; markerless &nbsp;·&nbsp; +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](assets/samples_40_real_tactile_mnist.png)
 ### `feelanyforce` &nbsp;·&nbsp; 50,997 frames &nbsp;·&nbsp; markerless &nbsp;·&nbsp; +per-indentation object
 The [FeelAnyForce dataset](https://huggingface.co/datasets/amirsh1376/FeelAnyForce) (Sharei et al. 2024) of robotically-controlled indentations against 42 unique objects (cylinders, cubes, spheres, fruits, household items). Aggressively dedupe-filtered (50% of raw frames dropped) because each indentation is a slow press-and-hold, so adjacent frames are visually near-identical. The schema retains the `obj_name` and `episode` columns; the upstream force labels (in `TacForce_train_set.csv` etc.) are not currently joined in — see the [upstream release](https://huggingface.co/datasets/amirsh1376/FeelAnyForce) for forces.
+![feelanyforce](assets/samples_40_feelanyforce.png)
 ## Useful statistics