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

Detector

As of 0.4.0 the default detector is WHIM — a fine-tuned full-hand-box detector that's steadier and more robust than MediaPipe's palm detector (which targets a smaller palm box and is more easily lost on rotated or partially-occluded hands). It feeds the same MediaPipe landmark model through a re-tuned ROI, and on the test image its landmarks match the original pipeline to ~0.2%. To use the original palm detector:

tracker = fasthands.load(detector="mediapipe")

fasthands photo.jpg --detector mediapipe

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

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