Wormhole Converted Core ML Models
This repository contains Core ML artifacts generated for wormhole from publicly available MediaPipe TensorFlow Lite models.
These artifacts are intended for:
- local development of the
wormhole perception runtime,
- benchmarking model-backed tracking against the legacy heuristic path,
- staging Core ML packages and compiled model bundles for Apple-platform testing.
Provenance
The source models come from MediaPipe's public model distribution and are fetched from the mediapipe-assets bucket referenced by MediaPipe's legacy solution docs.
Examples:
hand_landmark_full.tflitehand_landmark_lite.tflitepalm_detection_full.tflitepalm_detection_lite.tfliteface_landmark.tfliteface_detection_short_range.tfliteface_detection_full_range.tfliteiris_landmark.tflite
The conversion pipeline currently uses:
tflite2tensorflowcoremltoolsxcrun coremlcompiler
with compatibility patches required for the current local toolchain.
Artifact Layout
coreml/: .mlpackage bundles suitable for Core ML inspection and distributioncompiled/: .mlmodelc bundles compiled for Apple runtime loading
wormhole prefers the compiled .mlmodelc form at runtime.
Included Models
The current staged set is:
hand_landmark_fullhand_landmark_litepalm_detection_fullpalm_detection_liteface_landmarkface_detection_short_rangeface_detection_full_rangeiris_landmark
Intended Runtime Use
These models are being evaluated as replacements for the current heuristic tracking path in wormhole.
Current mapping:
- hand tracking: palm detector + hand landmark model
- face tracking: face detector + face landmark model
- iris tracking: iris landmark model
Known Gaps
The following public MediaPipe models are not currently included because the present conversion path does not handle them successfully:
face_landmark_with_attention
reason: unresolved custom op Landmarks2TransformMatrixface_detection_full_range_sparse
reason: current tflite2tensorflow path fails during sparse/densify handling
Notes
- These artifacts are generated outputs, not hand-authored model implementations.
- The binaries are meant to be reproducible from the original MediaPipe assets using the scripts in the main
wormhole repository.
- Licensing and redistribution should be reviewed against the upstream MediaPipe assets and their associated model cards before wider distribution.
