LingBot Vision Vit Large
robbyant
LingBot-Vision is a family of self-supervised Vision Transformer backbones for dense spatial perception. The models are pretrained with masked boundary modeling, a boundary-centric objective that encourages spatially…
Source notes
LingBot-Vision is a family of self-supervised Vision Transformer backbones for dense spatial perception. The models are pretrained with masked boundary modeling, a boundary-centric objective that encourages spatially structured patch features while retaining strong semantic representations.
- Code
- Unverified
- Weights
- Available
- Checkpoint
- Available
- License
- apache-2.0
Decision summary
perception
Hardware, latency, dependencies, and checkpoint loading are not pipeline-tested.
Run a checkpoint loading smoke test before marking the model pipeline-tested.
Release history
Source-backed release timing for this canonical model record.
- Official source
Checkpoint published
Release timing is recorded from the official source creation date.
Selection readiness
Evidence dimensions for deciding whether this model is ready to inspect, compare, or adopt. Scores describe catalog evidence, not task performance.
Published evaluation
How scores work80/100
Provisional
3/6 dimensions scored · 43% confidence
Engineering checks (OBRS) behind this record
Needs Audit
OBRS metadata and reported test evidence. A breakdown behind Selection readiness, not a separate score or an independent OpenBot certification.
- A passing real-hardware test report is required.
- A passing ingestion/pipeline test report is required.
- A passing privacy and provenance review is required.
Why these scores
The strongest decision reasons behind Selection readiness.
Artifact availability
UsefulCheckpoint files were verified in the official repository metadata.
Training and loading reproducibility
UnknownNo verified loading configuration is available.
Evaluation evidence
UnknownNo structured evaluation evidence has been verified.
Data requirements
Declared loop-data needs, missing evidence, and the next checks that matter.
Observation / ego video
video · depth · image-feature-extraction · depth-estimation
Language intent / task phase
language
Action / robot state
image-feature-extraction
Sim-real / embodiment metadata
depth · depth-estimation
Critical gaps
Core categories are represented. Interface alignment and data quality still require verification.
Linked datasets
Signal-level links only. Verify runtime interfaces before use.
No related catalog datasets are linked to this model yet.
More evidenceShowHide
Additional decision dimensions
Access and license are declared by the source.
75 · c85Required signal categories are structured; exact tensor and action interfaces still need verification.
76 · c45Hardware, latency, dependencies, and checkpoint loading are not pipeline-tested.
— · c10Artifact facts
- availability
- metadata_verified
- open
- repository metadata
- license
- metadata_verified
- apache-2.0
- repository metadata: license
- repository.files
- metadata_verified
- .gitattributes · README.md · model.pt
- repository file tree
- dataset.signals
- official_claim
- video · depth · language · image-feature-extraction · depth-estimation
- README/card declaration
- dataset.scale
- metadata_verified
- label: repository size · value: 1.21GB
- repository blob sizes
- model.weight_files
- metadata_verified
- model.pt
- repository file tree
- model.checkpoint_files
- metadata_verified
- model.pt
- repository file tree