mlx-cv

MLX-native computer vision for Apple Silicon — an inference-only pipeline for current-generation detection, segmentation, depth, tracking, and text-prompted grounding.

Pre-alpha. v0.0.3 adds package-native Hugging Face loading for the verified BF16 LocateAnything-3B and SAM 3.1 runtimes. The public API may change without notice.

What is this?

mlx-cv provides one parity-tested way to run modern computer-vision models natively on Apple Silicon through MLX. Models return typed results for boxes, masks, points, depth, camera geometry, and tracks.

The library is inference-only and weight-agnostic. Code is MIT licensed; model weights are fetched separately and retain their original licenses.

Architecture spine

• Result — one typed container for detections, masks, points, depth, cameras, tracks, and video results.
• SpatialTransform — deterministic coordinate and dense-output mapping back to the original input.
• Registry — model, vision-backbone, language-backbone, and head builders with plugin entry points.
• Ops and transforms — box, coordinate, sampling, resize, and letterbox primitives.
• Parity harness — committed fixtures plus required real-checkpoint gates that distinguish PASS from an external blocker.

Full design: docs/ARCHITECTURE.md.

Installation

pip install mlx-cv

The top-level spine remains import-light. Install the optional MLX runtime to execute model families on Apple Silicon:

pip install "mlx-cv[mlx]"

Requires Python 3.13+. Model execution requires an MLX-supported environment.

Install the Hub extra to resolve App Automaton aliases or exact Hugging Face repository IDs:

pip install "mlx-cv[mlx,hub]==0.0.3"

Local package directories work without a network lookup. Hub downloads are revision-aware, honor HF_HUB_OFFLINE=1, and never execute remote model code.

Published MLX weights

The repository links become live after the 0.0.3 publication checkpoint.

from mlx_cv.models.locateanything import LocateAnythingPipeline
from mlx_cv.models.sam3 import SAM3Processor, SAM3VideoSession

locate = LocateAnythingPipeline.from_pretrained("locateanything-3b-bf16")
grounding = locate.predict(image, "find every traffic sign")

sam_image = SAM3Processor.from_pretrained("sam3.1")
segments = sam_image.predict(image, "traffic sign")

sam_video = SAM3VideoSession.from_pretrained("sam3.1")

Model status

The canonical release-parity matrix is .agent/work/2026-06-16-release-parity-hardening/parity-status.json.

Normal checkpoint-less CI keeps external gates as honest skips or blocker records. It does not infer upstream parity from local fixtures.

Forward work

1. Admit EoMT-DINOv3 through a bounded real-checkpoint gate.

See .agent/steering/ROADMAP.md for the current phase contract.

0.0.3 release notes

• Added final-layout BF16 Safetensors contracts for LocateAnything-3B and the combined SAM 3.1 detector/Object Multiplex tracker.
• Added local path, exact Hugging Face ID, alias, revision, cache, and offline resolution.
• Added self-contained LocateAnything, SAM image, and SAM video from_pretrained entry points.
• Added reproducible model-package staging, SHA256 manifests, tracked model cards, license bundling, safe sequential upload, and remote verification tooling.
• Preserved the measured SAM 3.1 Metal parity results and the four-image LocateAnything BF16 regression baseline.

License

MIT © AppAutomaton — code only. LocateAnything weights use NVIDIA's non-commercial license; RF-DETR N–L weights are Apache-2.0; DA3-SMALL/BASE weights are Apache-2.0; SAM weights use the SAM license.