yolo-export

Export, quantization & deployment

Fastest route: export in Platform

Open a completed model's Export tab, select a format, 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. For TensorRT, select the deployment device's GPU family or Jetson target; if its TensorRT/CUDA versions differ from the device's, export locally on that device instead.

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	up to 3× CPU speedup on Intel
Apple iOS/macOS	coreml	broad OS coverage, Vision/iOS/Flutter support
Apple iOS 27+/macOS 27+	coreai	native .aimodel; iOS/Flutter SDKs default to coreml; export on Apple silicon macOS 26+ or x86_64 Linux
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/AMD)	rknn / qnn / hailo / ascend / imx / axelera / deepx / xilinx	name= selects the exact chip for rknn/qnn/hailo/ascend/xilinx

Full 22-target local export matrix with per-format supported args: format-matrix.md (this folder).

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
nms	None	None exports raw outputs for external NMS (YOLO26 too); True embeds NMS where format-matrix.md lists nms; False selects the YOLO26/YOLOv10 NMS-free head, else raw outputs
workspace	None	TensorRT builder GiB — lower if the build OOMs
device	None	TensorRT needs a GPU (auto-set to device=0 when unset); a GPU 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 follows nms: the default nms=None emits raw [4+nc, anchors] heads (xywh boxes + class scores) for every model, YOLO26 included, so you run NMS (imx instead always embeds NMS). nms=False on YOLO26/YOLOv10 emits NMS-free [max_det, 6] rows [x1,y1,x2,y2,conf,cls], and nms=True embeds NMS with the same row layout where supported; some formats fall back to raw heads (format-matrix.md). 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. On Linux, TensorRT auto-installs as tensorrt-cu<torch CUDA major>; elsewhere install it per NVIDIA docs. Hailo (Dataflow Compiler) and Ascend (CANN atc) need their vendor SDK first
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 macOS 26+ on Apple silicon or x86_64 Linux (glibc 2.34+), torch>=2.8, and Python 3.11–3.14; use CoreML for broader production support
Deprecation warnings for half/int8/end2end/tflite	auto-forwarded (half→quantize=16, int8→quantize=8, end2end=True→nms=False, end2end=False→nms=None unless nms=True is also passed, tflite→litert) — switch to the new names

Related pages

• format-matrix.md — all 22 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 (format and quantize errors list valid values) and yolo cfg over this file.