# Yolo Export > Use when exporting or deploying Ultralytics YOLO models in Platform or code — the Platform Export tab and yolo export/model.export() for ONNX, TensorRT, CoreML, Core AI, OpenVINO, LiteRT, NCNN, ExecuTorch, and NPUs (RKNN, QNN, Hailo, Ascend, IMX, Axelera, DeepX), FP16/INT8 quantization, benchmarking, and non-Python runtimes. For inference with .pt weights or Platform endpoints, see yolo-inference. - Skill: `ultralytics/yolo-export` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add ultralytics/yolo-export` - Raw SKILL.md: https://api.skillmd.com/api/skills/ultralytics/yolo-export/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: ultralytics (https://skillmd.com/u/ultralytics) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/ultralytics/yolo-export --- # 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](https://docs.ultralytics.com/platform/train/models#export-model). ## Quickstart ```python from ultralytics import YOLO model = YOLO("runs/detect/train/weights/best.pt") path = model.export(format="onnx") # returns the exported file/dir path ``` ```bash yolo export model=best.pt format=onnx ``` Exports load straight back into `YOLO()` for predict/val — same API: ```python 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) ```bash 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 ```bash 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.