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mickeyvanolst/yolo-coreml
yolo-coreml is a image classification model from mickeyvanolst. Use it when you need a label for an image. It is set up for coreml. The card lists the license as agpl-3.0.
Ready-to-run Core ML exports of four Ultralytics models, converted for the AML operator family for TouchDesigner on Apple silicon. Nothing in the models was changed: each is the official Ultralytics checkpoint exporte…
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From the Hugging Face model README
Ready-to-run Core ML exports of four Ultralytics models, converted for the
AML operator family for TouchDesigner
on Apple silicon. Nothing in the models was changed: each is the official
Ultralytics checkpoint exported with ultralytics 8.4.116 and
coremltools 9.0 to a .mlpackage (fp16, NMS left to the caller), with
Ultralytics' own metadata (task, class names, input size, licence) intact.
| Package | Task | Input | Classes | Source weights |
|---|---|---|---|---|
yolo26n-seg.mlpackage | instance segmentation | 640×640 | 80 (COCO) | yolo26n-seg.pt |
yolo11n-pose.mlpackage | pose, 17 keypoints | 640×640 | person | yolo11n-pose.pt |
yolo11n-cls.mlpackage | classification | 224×224 | 1000 (ImageNet) | yolo11n-cls.pt |
FastSAM-s.mlpackage | segment anything (prompt-free masks) | 640×640 | object | FastSAM-s.pt |
Export command, per model:
yolo export model=<weights>.pt format=coreml imgsz=<size> half=True
These files are derived from Ultralytics weights and are distributed under
the GNU Affero General Public License v3.0, the licence Ultralytics
publishes them under; the full text is in LICENSE. In short: you may use,
share and modify them, and anything you distribute or serve that is built on
them must be released under the AGPL as well. Ultralytics offers an
Enterprise License for use outside
those terms. Ultralytics, YOLO and FastSAM are the work of
Ultralytics and the FastSAM
authors; this repository only hosts a conversion.
The AML Model Manager downloads these directly. Any Core ML consumer can use them: the input is an RGB image, the outputs are the raw YOLO tensors (detections plus mask prototypes for the segmentation models), so the consumer decodes boxes, applies NMS and assembles masks itself.