README.md

metadata

library_name: mlx
license: apache-2.0
license_link: https://github.com/facebookresearch/EdgeTAM/blob/main/LICENSE
base_model: facebookresearch/EdgeTAM
pipeline_tag: image-segmentation
tags:
  - mlx
  - segmentation
  - promptable-segmentation
  - sam2
  - edgetam

mlx-community/EdgeTAM-fp16

EdgeTAM — on-device SAM 2 for promptable segmentation + video tracking — converted to Apple MLX (-fp16) for the mlx-edgetam-swift Swift package (MLXEngine promptSegment + trackObject ModelPackage). 22× faster than SAM 2, 16 FPS on iPhone 15 Pro Max.

From-scratch MLX-Swift architecture port. Image-mode (point/box → mask): RepViT-M1 encoder + FPN + SAM prompt encoder + two-way mask decoder — parity-locked vs the PyTorch oracle on the CPU stream (image_embed 9.7e-6, mask logits 8.9e-5; end-to-end mask IoU 0.99 vs PyTorch). Video-mode (trackObject, click on one frame → per-frame masklet): adds the video memory stack — PerceiverResampler

• MemoryEncoder + MemoryAttention (RoPE-2D) + the SAM2 memory-bank state machine — every op parity-locked vs the oracle; full masklet propagation min-IoU 0.92. This single -fp16 file carries both (874 tensors).

Use

// Package.swift → .package(url: "https://github.com/xocialize/mlx-edgetam-swift", from: "0.1.0")
import EdgeTAM
// Image: click → object mask
let p = try EdgeTAMPredictor.fromPretrained(weightsPath, dtype: .float16)
p.setImage(sourceCGImage)
let (mask, score, _, _) = p.predict(point: (500, 375))
// Video: click on a frame → per-frame masklet
let vp = try EdgeTAMVideoPredictor.fromPretrained(weightsPath, dtype: .float16)
let track = vp.track(frames: cgImages, clickFrame: 0, points: [[210, 350]], labels: [1])

Or as an MLXEngine ModelPackage (MLXEdgeTAM.EdgeTAMPackage) — promptSegment (image) + trackObject (video) surfaces — resolving this repo via the Hub.

Weights: Apache-2.0 (facebookresearch/EdgeTAM). Port code: MIT.