Cryo-ET Object Identification (wayiok)

Research applicability

A small research-coding reference for structural-biology image analysis, not a validated microscopy service or benchmark.

Getting started

Editorial, untested starting point: read the README and project report, resolve the YOLO11/2D U-Net naming inconsistency, then inspect a notebook before choosing an environment. Validate segmentation or detections against expert-labelled examples.

Maintenance

Documentation read on 2026-10-17; maintenance and runtime compatibility not tested.

License

Licensing and third-party data rights were not independently audited; check before reuse.

Workflow & human oversight

Workflow level intentionally unspecified for this reusable resource. Researchers select inputs, validate outputs and retain responsibility for interpretation.

Workflow level is not an evidence score; higher is not better evidence.

Approach

The README lists source-data, preprocessing and training-notebook directories for a 2D approach, plus a 3D U-Net approach. Its exact 2D training-model terminology is inconsistent.

Limitations

README and repository About review only; report, notebooks and datasets were not inspected or executed. README describes a YOLO11 2D approach but later calls its training code a 2D U-Net; this inconsistency is unresolved. No accuracy, comparative benefit, dataset completeness or reproducibility claim is validated.

Reproducibility

Primary repository documentation inspected; no installation, code execution, dataset inspection or independent outcome reproduction.

Adoption requirements

Requires ability to inspect and adapt machine-learning notebooks and appropriately licensed cryo-ET inputs. Exact dependencies, compute needs and data availability have not been verified.