fasthands 0.3.0

Fastest MediaPipe-compatible hand tracker: hand landmarks on the Apple Neural Engine at 0.7 ms/frame

fasthands

The fastest MediaPipe-compatible hand tracker. MediaPipe Hands' official models running on the Apple Neural Engine via CoreML — 0.55 ms per tracked frame on Apple Silicon (1800 FPS), ~6× faster than MediaPipe itself, with a faithful port of the full HandLandmarker pipeline (SSD anchors, weighted NMS, ROI tracking, landmark projection, deduplication).

macOS / Apple Silicon only.

Install

pip install fasthands

Use

import cv2
import fasthands

tracker = fasthands.load(num_hands=1)

image = cv2.cvtColor(cv2.imread("hand.jpg"), cv2.COLOR_BGR2RGB)
hands = tracker(image)                 # single image
# hands = tracker.detect_video(frame)  # video: tracks between frames, ~0.7 ms

for hand in hands:
    print(hand["handedness"], hand["score"])
    print(hand["landmarks"])        # 21 x (x, y, z), normalized image coords
    print(hand["world_landmarks"])  # 21 x (x, y, z), meters, hand-centered

Pipelined streaming (overlaps CPU pre/post with Neural Engine inference, ~15% faster end-to-end — see below):

import cv2, fasthands
tracker = fasthands.load(num_hands=1)
cap = cv2.VideoCapture(0)
def frames():
    while True:
        ok, f = cap.read()
        if not ok: break
        yield f
for frame_bgr, hands in fasthands.stream(tracker, frames()):
    ...  # draw/use hands; this runs concurrently with the next inference

Or from the command line:

fasthands photo.jpg --out annotated.jpg
fasthands-webcam --mirror     # live demo (pipelined) with FPS overlay

Speed (Apple M4, 540×720 frame, one hand)

	tracking	detect + track
fasthands (ANE)	0.55 ms (1800 FPS)	1.65 ms (600 FPS)
mediapipe (XNNPACK CPU)	3.25 ms (310 FPS)	8.77 ms (115 FPS)
	5.9× faster	5.3× faster

Landmarks agree with MediaPipe to ~9e-4 (Neural Engine fp16); the pipeline logic itself is verified to MediaPipe's own float32 reproducibility floor.

How

The hand_landmarker.task models are converted to CoreML, and every MediaPipe calculator in the pipeline (anchors, decode, weighted NMS, rect transforms, rotated crops, projections, VIDEO-mode ROI tracking, dedup) is reimplemented in numpy with float32 op-order fidelity. Model weights © Google, Apache 2.0.

Source, the PyTorch reference implementation, and the full verification harness: https://github.com/VimalMollyn/Mediapipe-Hands-PyTorch-CoreML