yolo26-kit 0.1.0

YOLO26 ↔ YOLOv8-pipeline bridge: shape adapters, decoder, letterbox, and ORT wrappers.

yolo26-kit

Drop YOLO26 into your existing YOLOv8 pipeline. Bridge library for the NMS-free, end-to-end era.

Why

YOLO26 default ONNX export is end2end=True, producing (N, 300, 6) already-decoded detections. That's great — but it breaks every Triton / DeepStream / OpenCV DNN config that expects YOLOv8-style (1, 4+nc, N). It also leaves you to write letterbox-coord undo math by hand. And some targets (RKNN, certain TFLite quant paths) can't fuse the e2e head, so they ship raw (1, 4+nc, N) you have to decode yourself.

yolo26-kit is a small, pure-functional library that bridges that gap — and nothing else.

Install

# Python (planned for PyPI)
pip install yolo26-kit            # core
pip install "yolo26-kit[ort]"     # + ORT wrapper

# TypeScript / JavaScript (planned for npm)
npm i yolo26-kit                 # core
npm i yolo26-kit onnxruntime-web # + ORT wrapper

Pre-release: install via Git clone for now. PyPI / npm coming with v0.1.0 release.

Use

Python — e2e default (most users)

import onnxruntime as ort
import yolo26_kit

sess = ort.InferenceSession("yolo26n.onnx")
out = sess.run(None, {"images": pre})[0]   # (1, 300, 6)
out = yolo26_kit.normalize_output(out)
dets = yolo26_kit.filter_e2e(out, conf=0.25)
# [{'box': [...], 'score': ..., 'class': ..., 'label': '...'}]

Python — drop into existing YOLOv8 pipeline

out_v8 = yolo26_kit.e2e_to_v8_shape(out)   # (1, 84, 300)
# Feed out_v8 to your existing v8 Triton / DeepStream / OpenCV DNN parser.

Python — convenience wrapper (image in, dets in original coords out)

from yolo26_kit import from_ort

decoder = from_ort("yolo26n.onnx")
dets = decoder.predict("bus.jpg", conf=0.25)

TypeScript

import { filterE2E, fromOrt } from "yolo26-kit/ort";
import * as ort from "onnxruntime-web";

const session = await ort.InferenceSession.create("yolo26n.onnx");
const decoder = fromOrt(session);
const dets = await decoder.predict(canvas, { conf: 0.25 });

TypeScript — non-e2e raw export

import { decodeDetect, normalizeOutput } from "yolo26-kit";

const out = normalizeOutput(rawTensor);
const dets = decodeDetect(out, [1, 84, 8400], { conf: 0.25, numClasses: 80 });

What's in the box (v0.1.0)

Function	Purpose
filter_e2e / filterE2E	Filter the default (N, 300, 6) e2e output by confidence + classes + min-area
decode_detect / decodeDetect	Decode raw (1, 4+nc, N) non-e2e exports (NPU / quantized targets)
e2e_to_v8_shape / e2eToV8Shape	Adapter to drop YOLO26 into legacy YOLOv8 pipelines
v8_shape_to_e2e / v8ShapeToE2E	Reverse adapter
letterbox_unmap / letterboxUnmap	Undo letterbox padding back to original image coords
normalize_output / normalizeOutput	Dtype workaround for upstream YOLO26 fp16 export issue (#23645)
Decoder.predict()	Image in → detections in original-image coords out (auto e2e/non-e2e routing)

Compatibility

yolo26-kit	ultralytics (verified)	onnxruntime
0.1.0	8.4.x (yolo26n.pt)	1.17+

A nightly live-diff workflow installs the latest ultralytics and re-validates fixtures. Drift opens an auto-tagged drift issue.

Status

v1 (this release): Detect task only. e2e + non-e2e paths. Both Python and TypeScript, cross-language equivalent (verified via golden fixtures).

Roadmap: Seg, pose, cls, OBB tasks. Reproduction kit (Docker + COCO train/eval). ONNX schema standardizer. NPU ports (RKNN, Hailo, RDK, Qualcomm SNPE/QNN).

Development

# Python
cd python
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest tests/ -v
mypy src/yolo26_kit
ruff check src tests

# TypeScript
cd js
npm install
npm test
npm run typecheck
npm run lint

License

Apache-2.0 for the code in this repository. Model weights are not redistributed; users fetch them from the official Ultralytics distribution under its own terms (AGPL-3.0).

See fixtures/LICENSES.md for fixture asset attribution.