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hugging-mac/
yolov8-coreml

Name: yolov8-coreml
Availability: InStock

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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

python

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.