YOLOv8 Pose — Core ML

Ready-to-use Core ML conversions of Ultralytics YOLOv8 Pose for human pose estimation on Apple Silicon. The n, s, and m variants are provided as .mlpackage models for use with the CPU, GPU, and Apple Neural Engine.

These models power pose-driven apps and games in Hugging Mac, an open-source platform for building local AI experiences on macOS.

Models

Provenance and conversion

• Source: Ultralytics assets v8.2.0
• Task: person detection and COCO 17-keypoint pose estimation
• Input size: fixed 640 × 640, batch size 1
• Precision: FP16 conversion
• NMS: not embedded; apply confidence filtering and NMS after inference

Input and output

Core ML example

from pathlib import Path

import coremltools as ct
from huggingface_hub import snapshot_download
from PIL import Image, ImageOps

root = Path(snapshot_download(
    repo_id="hugging-mac/yolov8-pose-coreml",
    allow_patterns=["yolov8n-pose.mlpackage/**"],
))
model = ct.models.MLModel(
    root / "yolov8n-pose.mlpackage",
    compute_units=ct.ComputeUnit.ALL,
)
image = ImageOps.pad(
    Image.open("person.jpg").convert("RGB"),
    (640, 640),
    color=(114, 114, 114),
)
predictions = model.predict({"image": image})["predictions"]
print(predictions.shape)  # (1, 56, 8400)

The output is raw. See the Hugging Mac YOLOv8 Pose SDK for complete preprocessing, NMS, keypoint decoding, and coordinate restoration.

Integrity

License

The converted models retain the upstream Ultralytics licensing terms and are published under AGPL-3.0. Review Ultralytics licensing requirements before commercial or closed-source use. Hugging Mac is not affiliated with or endorsed by Ultralytics.