Export, quantization & deployment

Fastest route: export in Platform

Open a completed model's Export tab, select one of the 20 formats, configure its arguments, and click Start Export. Platform runs CPU exports directly and asks for a target GPU where the format requires one (notably TensorRT); download the artifact when the job completes. Match TensorRT's selected GPU family and software environment to the deployment target, just as with a local engine build.

Use Platform when you do not want to install each exporter toolchain locally. Use the Python/CLI path below for custom calibration, repeatable automation, local hardware builds, or immediate parity validation. See Platform model export.

Quickstart

from ultralytics import YOLO

model = YOLO("runs/detect/train/weights/best.pt")
path = model.export(format="onnx")  # returns the exported file/dir path

yolo export model=best.pt format=onnx

Exports load straight back into YOLO() for predict/val — same API:

model = YOLO("best.onnx")  # or best.engine, best_openvino_model/, ...

Choose format by target hardware

Full 21-target local export matrix with per-format supported args: format-matrix.md (this folder). Platform currently offers 20 deployment formats.

Key arguments

Verify parity after export (always)

yolo val model=best.pt data=data.yaml   # baseline
yolo val model=best.onnx data=data.yaml # compare the same task metric with the baseline

Acceptable differences depend on the task, model, backend, precision, and calibration data. Investigate unexpected gaps by matching imgsz and pre/post-processing and, where required, using representative calibration data. Also compare one prediction with .pt.

Benchmark all formats empirically

yolo benchmark model=best.pt data=data.yaml imgsz=640                                    # all formats at default precision
yolo benchmark model=best.pt data=data.yaml format=engine quantize=16 device=0 imgsz=640 # targeted FP16

Produces the task metric + latency per exportable format on this machine. Repeat for each supported precision and benchmark on deployment hardware, not your dev box.

Consuming exports outside Python

• In raw runtimes (C++, mobile, JS) you own preprocessing (letterbox resize,

BGR→RGB, /255) and output decoding.

• Detect output layout differs: end-to-end YOLO26 emits final

[x1,y1,x2,y2,conf,cls] rows. If export disables end-to-end, YOLO26—like YOLO11/v8—emits raw [4+nc, anchors] heads; where supported, nms=True wraps them. Set end2end=False nms=True to request that path explicitly. Segment, pose, and OBB add task-specific outputs. Check export warnings and shapes.

• Class names travel in export metadata where supported; otherwise ship the names

map alongside the model.

• Serving: ultralytics.utils.triton.TritonRemoteModel for Triton;

examples/ in the ultralytics repo has ONNXRuntime C++/Rust/Python references.

Troubleshooting

Related pages

• format-matrix.md — all 21 local export targets, artifacts produced, and supported

args. Read when using any format beyond onnx/engine/openvino/coreml.

If the installed version rejects an argument, trust the error text (it lists valid values) and yolo cfg over this file.