0.14.0

A minor release that moves the tests, docs and examples to YOLO26, defaults YOLOE to YOLOE-26, fixes bugs in the YOLOE, YOLO-World, torchvision, Hugging Face and mmdet wrappers, downloads and scripts, and makes COCO slicing and mask drawing faster with the same output.

💥 Breaking Changes

• YOLOE loads yoloe-26s-seg.pt when no model_path is given (#1499) instead of yoloe-11s-seg.pt. Pass model_path to keep using YOLOE-11.
• The hugging_face_universal_segmentation model type is removed (#1498). It had no module behind it and always failed with ModuleNotFoundError. Use huggingface_segmentation for those models.

🐛 Fixes

• YOLOE and YOLO-World predictions use the class names set with set_classes (#1499) instead of '0', '1' and so on. A category_mapping you pass in is still kept.
• model_type="yolo-world" loads (#1498). The registered class name was misspelled, so it always failed with AttributeError.
• The torchvision wrapper keeps every image of a batch (#1502) with its own shift amount and full shape, instead of only the last image.
• Hugging Face panoptic segmentation keeps batch outputs paired with their slices (#1502). An image with no segments was dropped, so the predictions after it got the wrong slice positions.
• mmdet inference no longer has a path that raises NameError (#1502).
• python -m sahi.scripts.slice_coco runs (#1501). It passed the builtin slice to Fire instead of slicer.
• predict adds up the export time of every image (#1501) instead of keeping only the last one, and no longer raises KeyError with verbose=2 when there are no images.
• predict_fiftyone counts the prediction time of sliced predictions (#1501).
• sahi env shows pillow and every installed OpenCV distribution (#1501), and no longer lists tensorflow, which sahi does not use.
• Downloads write to a temporary file first (#1495, #1504), so an interrupted download never leaves a broken model file that later runs would load. The YOLOv5 helpers used by the YOLOv5 notebook now go through the same download code.

⚡ Performance

All of these give the same output as before (#1503).

Benchmark	v0.13.1	v0.14.0
slice_coco on 50 VisDrone images, 6493 sliced annotations	1.48 s	0.92 s
Drawing 80 masks on a 4K image	2.09 s	0.15 s
Drawing masks on 100 frames of the OpenCV vtest.avi video	1.20 s	0.12 s

• Slicing computes each annotation's bbox and area once per image instead of once per slice.
• Mask drawing blends only the area each mask covers instead of the whole image.
• Sliced prediction and SliceImageResult indexing no longer rebuild the slice lists on every access, and predict converts each image to an array once for all exports.

🧹 Maintenance & CI

• Tests and CI commands use YOLO26 (#1496) for detection, segmentation, OBB and ONNX export.
• CI installs CPU torch (#1494), which makes the CI caches about three times smaller.
• from __future__ import annotations is removed (#1500) now that Python 3.10 is the minimum, and ruff stops it from coming back.

📚 Documentation

• The README, docs and examples use YOLO26 (#1497) in English, Turkish and Chinese, and the Ultralytics notebook runs on CPU by default.
• The YOLOE docs and notebook use YOLOE-26 (#1499) and explain that only the -pf models detect objects without prompts.