[!IMPORTANT] mlx-cv is pre-alpha. The model implementations and parity evidence are real, and the public loader/result/visualization contracts now cover every supported family. Model weights remain external artifacts.

What is implemented

Architecture

flowchart LR
    A[Images / frames<br/>and optional prompts] --> B[Processor<br/>preprocess]
    B --> C[MLX model<br/>backbone + task path]
    C --> D[Processor<br/>postprocess]
    D --> E[Typed Python output]
    B -. SpatialTransform .-> D
    F[External checkpoint<br/>or model package] --> C
    G[Reference capture] -. parity gates .-> C

The stable core is intentionally small:

• Typed results for detections, masks, points, depth, camera geometry, and tracks.
• SpatialTransform for deterministic coordinate and dense-output inversion.
• Reusable MLX blocks for ViT, DINOv2, DINOv3, MoonViT, Qwen2.5, dense heads, and DETR-style decoding.
• Processors and sessions that keep image I/O, prompts, and temporal state outside compute modules.
• One lazy loader catalog exposed through mlx_cv.load(...) without making the package root import MLX.
• Pillow visualization through Result.draw() for boxes, masks, points, keypoints, tracks, and depth.
• Repository parity tooling under tools/ that distinguishes local fixture coverage from a real upstream comparison without entering the runtime wheel.

See Architecture for the implemented module boundaries and known gaps.

Installation

mlx-cv requires Python 3.13 or newer. The base package stays import-light and depends only on NumPy and Pillow.

The project is pre-alpha and the public API still moves, so an editable install from a checkout is the recommended path. It keeps you on the same code the parity ledger, docs/, and this README describe:

git clone https://github.com/appautomaton/mlx-cv
cd mlx-cv
python -m pip install -e ".[mlx]"

Released versions are published to PyPI and install the same way, but they lag main and carry whatever API was current when they were cut:

pip install "mlx-cv[mlx]"

The mlx extra pulls in the MLX runtime that executes models. Add hub when resolving a remote snapshot or loading a self-contained LocateAnything package (".[mlx,hub]"). Installing with no extras gives you the typed result, transform, and package contracts without a runtime.

Local package directories are resolved without importing Hugging Face Hub. Remote resolution is revision-aware, honors HF_HUB_OFFLINE=1, and never executes Python code from a model repository.

Unified loading

Every supported runtime is available through the same lazy entry point. A local package is a directory containing config.json, model.safetensors or model.npz, and any model-specific runtime assets.

import mlx_cv

locate = mlx_cv.load(
    "locateanything-3b",
    "/path/to/locateanything-package"
)
grounding = locate.predict(image, "find every traffic sign")

detector = mlx_cv.load("rfdetr-nano", "/path/to/rfdetr-package")
detections = detector.predict(image)

depth = mlx_cv.load(
    "depth-anything-v3-multiview",
    "/path/to/da3-small-package",
)
geometry = depth.predict(images)

eomt = mlx_cv.load("eomt-dinov3")
panoptic = eomt.predict(image)

sam_image = mlx_cv.load("sam3.1-image", "/path/to/sam31-package")
segments = sam_image.predict(image, "traffic sign")

sam_video = mlx_cv.load("sam3.1-video", "/path/to/sam31-package")

The underlying model classes also expose from_pretrained(...). Package layouts and direct-checkpoint requirements are documented in Model packages. Local paths use the same package contract. EoMT's configured default resolves its official external repository; the other runtimes require an explicit package path or exact repository ID.

Verification status

The earlier model families retain their canonical evidence ledger in .agent/work/2026-06-16-release-parity-hardening/parity-status.json. EoMT-DINOv3 is covered by its current reproducible real-checkpoint gate and implementation notes.

Normal checkpoint-free CI exercises unit tests and committed fixtures. Heavy real-checkpoint gates are explicit opt-in checks; a skipped external gate is never promoted into a parity claim.

Development and test artifacts

Generated files do not belong in the repository. Use an isolated system temporary directory for pytest state, caches, build outputs, downloads, and debug artifacts:

MLX_CV_TEST_ROOT="$(mktemp -d "${TMPDIR:-/tmp}/mlx-cv-tests.XXXXXX")"
trap 'rm -rf "$MLX_CV_TEST_ROOT"' EXIT

PYTHONDONTWRITEBYTECODE=1 \
UV_CACHE_DIR="$MLX_CV_TEST_ROOT/uv-cache" \
python -m pytest -q \
  --basetemp="$MLX_CV_TEST_ROOT/pytest" \
  -o "cache_dir=$MLX_CV_TEST_ROOT/pytest-cache"

Tracked files under tests/fixtures/ are deterministic regression inputs, not generated test output. Upstream reference checkouts and model weights also stay outside the Git tree.

Project status

The public inference surface is now consistent across the implemented families:

• mlx_cv.available_models() reports the canonical lazy-loader catalog;
• mlx_cv.load(...) dispatches package loading without importing every model;
• image models return Result, while SAM video propagation returns VideoResult;
• Result.draw() returns a new RGB Pillow image and does not mutate its input;
• all five families accept standard local packages through from_pretrained(...).

Weights are not bundled, training and serving are out of scope, and the API is still pre-alpha. The admitted EoMT surface is the COCO panoptic small-640 model; larger variants and dataset evaluation are not implied.

The current work order lives in the roadmap. Historical specs, plans, reviews, and evidence remain intact under .agent/work/.

Documentation

• Current architecture
• Depth Anything V3
• SAM 3.1 image and Object Multiplex
• EoMT–DINOv3 panoptic segmentation
• Local model packages
• Reference implementation policy

License

The library code is MIT licensed. Model weights are external artifacts and retain their upstream licenses: NVIDIA's LocateAnything license, Apache-2.0 for the selected RF-DETR and DA3 weights, MIT for the EoMT package, and the SAM License for SAM 3.1.