webAI-Official/yolo26s-mlx

YOLO26s (MLX)

Pure-MLX weights for YOLO26s, ready to run on Apple Silicon with yolo-mlx. No PyTorch at runtime, no cloud calls, no waiting on someone else's API — everything stays on your Mac.

This is the balanced default in the YOLO26 MLX family: a solid mix of accuracy and speed for most use cases.

Quickstart

pip install yolo-mlx huggingface_hub

from huggingface_hub import hf_hub_download
from yolo26mlx import YOLO

weights = hf_hub_download("webAI-Official/yolo26s-mlx", "yolo26s.npz")
model = YOLO(weights)

results = model.predict("https://ultralytics.com/images/bus.jpg", conf=0.25)
results[0].save()

Specs

Variant	mAP@0.5:0.95	FPS (M4 Pro)	Best for
yolo26s	47.6%	105	Balanced default

Other variants in this family: yolo26n-mlx · yolo26m-mlx · yolo26l-mlx · yolo26x-mlx

Requirements

• Apple Silicon Mac (M1, M2, M3, or M4)
• macOS 14.0+
• Python 3.10+

Intel Macs are not supported — the whole point of MLX is Apple Silicon native acceleration.

What's in this repo

File	Description
yolo26s.npz	MLX-format weights, converted from the YOLO26s .pt checkpoint and verified shape-by-shape against the source.
README.md	This card.

Training data

Pretrained on COCO (80 classes). For domain-specific use cases, fine-tune on your own data — see the training guide in the upstream repo.

License

AGPL-3.0, inherited from upstream thewebAI/yolo-mlx. Free to use, fork, modify, and ship for personal projects, research, and prototypes. If you deploy this as a hosted service for real users, AGPL requires you to publish your source under the same license.

About webAI

webAI builds the sovereign AI platform — AI that runs on your infrastructure, stays under your control, and compounds with your knowledge. Every release here reflects a simple belief: open models, owned locally, coordinated intelligently, compound into something no centralized system can match.

🌐 webai.com · 💬 community.webai.com