hugging-mac/yolov8-coreml

YOLOv8 Object Detection — Core ML

Ready-to-use Core ML conversions of Ultralytics YOLOv8 for object detection 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 are used by Hugging Mac, an open-source platform for building and experiencing local AI apps, services, games, plugins, and agents on macOS.

Models

Variant	Parameters	Package size	Best for
yolov8n	3.2M	6.5 MB	Lowest latency and resource usage
yolov8s	11.2M	22.5 MB	Balanced speed and accuracy
yolov8m	25.9M	52.0 MB	Higher accuracy

Provenance and conversion

• Source: Ultralytics/YOLOv8
• Source revision: 8a9e1a5
• Task: COCO 80-class object detection
• Input size: fixed 640 × 640, batch size 1
• Precision: FP16 conversion
• NMS: not embedded; apply confidence filtering and NMS after inference

Input and output

Name	Type	Shape	Description
image	RGB image	640 × 640	Letterboxed model input
predictions	FP32 multi-array	1 × 84 × 8400	Boxes and scores for 80 COCO classes

The first four output channels contain bounding boxes in x, y, width, height form. The remaining 80 channels contain class scores. Resize boxes back to the original image after confidence filtering and class-aware NMS.

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-coreml",
    allow_patterns=["yolov8n.mlpackage/**"],
))

model = ct.models.MLModel(
    root / "yolov8n.mlpackage",
    compute_units=ct.ComputeUnit.ALL,
)

image = Image.open("image.jpg").convert("RGB")
image = ImageOps.pad(image, (640, 640), color=(114, 114, 114))
predictions = model.predict({"image": image})["predictions"]

print(predictions.shape)  # (1, 84, 8400)

For a complete preprocessing, decoding, and NMS implementation, see the Hugging Mac YOLOv8 SDK.

Integrity

Package	Directory SHA-256
yolov8n.mlpackage	c8eb62862dddac01512e47469e5dc15ad707d85c2d713d78a5b17b17d365f4a4
yolov8s.mlpackage	c5178bbaf83f8d4be513e2ce6f8081d44882466ab4a4eb62c704e1c9c95d3eac
yolov8m.mlpackage	d4e4e1a92b891cd8cfc3c41e15a57b92b26abc263deed6dc409874c1fbefe6a5

License

The converted models retain the upstream Ultralytics YOLOv8 licensing terms. They are published under AGPL-3.0. Review Ultralytics licensing requirements before commercial or closed-source use.

Hugging Mac is an independent open-source project and is not affiliated with or endorsed by Ultralytics.