Datasets:

yxma
/

gelsight-mini-pretrain-nc

license: cc-by-nc-4.0
task_categories:
  - image-classification
  - feature-extraction
  - image-to-image
tags:
  - tactile
  - gelsight
  - gelsight-mini
  - robotics
  - tactile-sensing
  - pretraining
  - non-commercial
size_categories:
  - 10K<n<1M
pretty_name: GelSight Mini Pretrain · NC extension
configs:
  - config_name: sparsh
    data_files:
      - split: flat
        path: sparsh/flat-*.parquet
      - split: sharp
        path: sparsh/sharp-*.parquet
      - split: sphere
        path: sparsh/sphere-*.parquet

GelSight Mini Pretrain · Non-Commercial Extension

⚠️ Non-commercial use only. This repository is licensed CC-BY-NC-4.0 because it includes upstream sources whose licenses restrict commercial use. For commercial-friendly Mini tactile data, see the main yxma/gelsight-mini-pretrain repo (CC-BY-4.0).

This dataset is the CC-BY-NC extension of yxma/gelsight-mini-pretrain. It contains only the GelSight Mini sources whose upstream licenses are not compatible with CC-BY-4.0 aggregation. All data is processed through the same pipeline as the main repo (same schema, same area+intensity validity filter, same channel-order normalization), so users can simply load both repos and concatenate them to get a larger pool.

Sample images

Sparsh (3 indenter shapes)

Indenter	Frames	Random samples
flat	8,056
sharp	11,844
sphere	46,544

When to use this repo

Use the main repo if:

You want commercial use rights
You only need ~830K frames across 12 sources (already plenty for VAE pretraining)

Use both (main + this extension) if:

Your work is non-commercial (academic, internal research)
You want maximum coverage of public Mini tactile data
You're OK respecting the NC clause on the extension subsets only

How to combine with the main repo

from datasets import concatenate_datasets, load_dataset

# Load all subsets from the main repo (CC-BY-4.0)
main_real = concatenate_datasets([
    load_dataset("yxma/gelsight-mini-pretrain", c, split="train")
    for c in ["fota_labeled", "fota_unlabeled", "threedcal", "feats",
              "gelslam", "tactile_tracking", "real_tactile_mnist",
              "feelanyforce", "unit", "tacquad"]
])

# Add NC extension (sparsh has 3 indenter-named splits)
nc_extras = concatenate_datasets([
    load_dataset("yxma/gelsight-mini-pretrain-nc", "sparsh", split=s)
    for s in ["flat", "sharp", "sphere"]
])

all_markerless = concatenate_datasets([main_real, nc_extras])
print(f"Total: {len(all_markerless):,} frames")

Schema

Identical to the main repo (30 columns, parquet, JPEG q=92 images). Every row has domain="real", markered=False, gel_variant="markerless", plus source-specific metadata.

Pipeline parity

Applies the same unified area+intensity validity filter as the main repo:

pixel_diff = |frame - baseline| on central 50% crop, greyscale
mask = pixel_diff > 10 (sensor noise floor)
area = mask.sum(), intensity = pixel_diff[mask].mean()
KEEP iff area >= 40 px AND intensity >= 12 grey-levels
ELSE keep with probability 1.5% (background diversity)

Channel-order normalization

Sparsh's upstream pickles contain a mix of RGB and BGR images (apparently from heterogeneous data-collection pipelines at Meta). We normalize all images to RGB by checking each image's per-channel means and swapping R<->B when mean(R) > mean(B) (GelSight Mini's at-rest gel is illuminated such that B > R is the correct channel order). After normalization, every image in this repo is guaranteed RGB.

Sources currently included

Subset	Upstream	License	Frames	Notes
`sparsh`	`facebook/SparshGelSight`	CC-BY-NC-4.0	66,444 (flat 8K + sharp 12K + sphere 47K)	Part of Meta's Sparsh / TacBench. GelSight Mini, markerless, 3 indenter shapes with paired ATI nano17 force ground truth. Includes channel-order normalization (upstream pkls mix RGB and BGR).

Deprecated: The earlier faf_force_estimation/ subset (from facebook/gelsight-force-estimation) has been removed -- it was a smaller snapshot of the same collection protocol and data as sparsh, just released under a different upstream repo name. Sparsh is the canonical larger version of the same protocol.

Citation chain

If you use this extension, please cite both the main aggregation and the upstream NC source:

@dataset{gelsight_mini_pretrain_nc,
  title  = {GelSight Mini Pretrain - NC extension},
  author = {Ma, Yuxiang},
  year   = {2026},
  url    = {https://huggingface.co/datasets/yxma/gelsight-mini-pretrain-nc},
}

@misc{sparsh_2024,
  title  = {Sparsh: Self-supervised touch representations for vision-based tactile sensing},
  author = {Higuera, Carolina and Sharma, Akash and Bodduluri, Chaithanya Krishna and Fan, Taosha and Lancaster, Patrick and Kalakrishnan, Mrinal and Kaess, Michael and Boots, Byron and Lambeta, Mike and Wu, Tingfan and Mukadam, Mustafa},
  year   = {2024},
  url    = {https://huggingface.co/datasets/facebook/SparshGelSight},
}

License

This aggregated release is CC-BY-NC-4.0 because it inherits the strictest license among the included sources. Component datasets retain their own original licenses; cite each upstream source individually.