image unknown | image_format stringclasses 1
value | source stringclasses 2
values | markered bool 1
class | capture stringclasses 239
values | split stringclasses 2
values | height int32 240 240 | width int32 320 320 | obj_name stringclasses 166
values | init_pose int32 | side stringclasses 0
values | x_mm float32 | y_mm float32 | z_mm float32 | quat_x float32 | quat_y float32 | quat_z float32 | quat_w float32 | indenter stringclasses 0
values | indenter_param stringclasses 0
values | f_x float32 | f_y float32 | f_z float32 | grid_z_max float32 | grid_z_mean float32 | episode stringclasses 162
values | frame_idx int32 0 45.6k | digit_class int32 | gel_variant stringclasses 0
values | domain stringclasses 1
value | sequence_id stringclasses 239
values | frame_in_seq int32 0 40.4k | sequence_length int32 1 40.4k | fps float32 6.81 25 |
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End of preview. Expand in Data Studio
GelSight Mini Pretrain · Video / Sequence Subset
🎬 Companion to
yxma/gelsight-mini-pretrain. Where the main repo treats every kept frame as an independent image, this repo preserves temporal sequences — one row per frame, ordered, with explicit sequence-id + position metadata, for video tactile pretraining.
Why this repo
The main repo's pipeline applies perceptual-hash dedupe within each capture to drop near-identical adjacent frames. That's great for image-level pretraining (no redundancy) but bad for video-level pretraining (the small inter-frame changes are exactly the temporal signal we want to learn).
This repo:
- Uses the same area+intensity validity filter as the main repo
(drops gel-at-rest frames —
A ≥ 40, I ≥ 10, PIXEL_THRESH = 10) - Skips phash dedupe so consecutive frames within a sequence are preserved
- Adds sequence-level metadata so users can reconstruct clips
Schema additions (vs main repo)
| Column | Type | Description |
|---|---|---|
sequence_id |
string | unique identifier for the sequence the frame belongs to |
frame_in_seq |
int32 | 0-indexed position of this frame within the sequence |
sequence_length |
int32 | total active-contact frames in this sequence (helpful for windowing) |
fps |
float32 | original capture frame rate (typically ~25 for GelSight Mini) |
Existing columns from the main schema (image, capture, split, height, width, source, markered, domain, etc.) are all carried through.
Sources (video sources only)
| Source | Sequence unit | # sequences | Active frames |
|---|---|---|---|
gelslam |
one gelsight.avi per episode (tracking + reconstruction) |
155 | TBD |
tactile_tracking |
one gelsight.avi per trial |
84 | TBD |
fota_unlabeled |
one capture (object × pose × side) | 60 | TBD |
real_tactile_mnist |
one touch-window per touch | 153,600 | TBD |
(Counts will be filled in as each source is processed.)
Recommended uses
- Video/temporal tactile pretraining (X-frame clips)
- Slow-motion contact dynamics modelling
- Sim-to-real transfer learning over time
- Cross-frame contrastive learning (within sequence vs across sequence)
Sample-windowing recipe
from datasets import load_dataset
import numpy as np
ds = load_dataset("yxma/gelsight-mini-pretrain-video", "gelslam", split="train")
# Group rows by sequence_id, sorted by frame_in_seq
import pandas as pd
df = ds.remove_columns(["image"]).to_pandas() # just metadata
seqs = df.groupby("sequence_id")["frame_in_seq"].count()
# Pull a 16-frame clip starting at the first frame of episode "0042"
clip = ds.filter(lambda r: r["sequence_id"] == "0042" and r["frame_in_seq"] < 16)
frames = [r["image"] for r in clip] # PIL.Image list of length 16
Status
- Schema established
- Sources processing in progress; counts TBD
License
CC-BY-4.0 (same as main repo). Cite upstream sources per SOURCES.md in the
main repo.
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