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

Target	format=	Why
NVIDIA GPU / Jetson	engine (TensorRT)	fastest on NVIDIA; build on the deployment device — engines are not portable across GPUs/TRT versions
Intel CPU/iGPU/NPU	openvino	~3× CPU speedup
Apple iOS/macOS	coreml	broad OS coverage, Vision/iOS/Flutter support
Apple iOS 27+/macOS 27+	coreai	native .aimodel; use coreml for iOS/Flutter SDKs; export on Apple silicon/macOS 26+
Android	litert (renamed from tflite) or ncnn	NCNN strong on ARM
Raspberry Pi	ncnn	best ARM CPU latency
PyTorch Edge	executorch
Cross-platform / unsure	onnx	runs everywhere; start here, specialize when latency demands
NPUs (Rockchip/Qualcomm/Hailo/Huawei/Sony/Axelera/DeepX)	rknn / qnn / hailo / ascend / imx / axelera / deepx	name= selects the exact chip for rknn/qnn/hailo/ascend

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

Key arguments

Arg	Default	Notes
imgsz	model	inherited from the loaded model; set explicitly to the deployment shape
quantize	None	precision request: 16/fp16, 8/int8/w8a8, w8a16, w8a32, or 32/fp32; support, speed, size, and accuracy are backend-dependent — see format-matrix.md and benchmark the target
data	None	representative calibration data when required; use >300 images generally and 500+ for TensorRT. Omission selects a small task default, so pass deployment-representative data explicitly
dynamic	False	variable input shape/batch where supported; check format-matrix.md and benchmark the target
batch	1	max batch baked into the export
simplify	True	simplify ONNX graph
opset	None	compatible ONNX opset selected automatically when unset; pin lower if the consumer runtime complains
end2end	None	preserve the model setting; set False on YOLO26/YOLOv10 when the target needs raw outputs or conventional NMS
nms	False	bake NMS into a raw-output pipeline where supported; for YOLO26/YOLOv10 also set end2end=False
workspace	None	TensorRT builder GiB — lower if the build OOMs
device	None	device=0 required for TensorRT; also speeds INT8 calibration
fraction	1.0	fraction of calibration data used

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

Symptom	Fix
Export crashes on missing package	most backends auto-install on first export; rerun. TensorRT must match your CUDA — install per NVIDIA docs
Unsupported ONNX opset downstream	export with lower opset=, or upgrade the runtime
TensorRT build OOM/slow	lower workspace, batch=1, dynamic=False
Export much less accurate	imgsz mismatch; too little/unrepresentative calibration data; wrong custom pre/post-processing; use a supported higher precision or backend
Engine fails on another machine	TensorRT engines are device+version specific — rebuild on target
CoreML export fails on Windows	export on macOS or Linux
Core AI export is unavailable	requires Apple silicon, macOS 26+, torch>=2.8, and Python 3.11–3.13; use Core ML for broader production support
Deprecation warnings for half/int8/tflite	auto-forwarded (half→quantize=16, int8→quantize=8, tflite→litert) — switch to the new names

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.