git:20261006.e5cd7b8 to git:20261006.acfe53c

26 added, 27 removed. Audit A to A.

---

name: yolo-export

description: >

- 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.

+ 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, Xilinx), FP16/INT8 quantization, benchmarking, and non-Python runtimes. For inference with .pt weights or Platform endpoints, see yolo-inference.

---

# 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](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` | 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`; 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 |

+ | 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 21-target local export matrix with per-format supported args: `format-matrix.md` (this folder).

+ 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 |

- | `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` |

+ | `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)

```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 emits raw `[4+nc, anchors]` heads like YOLO11/YOLOv8; 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.

+ - 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 Apple silicon, macOS 26+, torch>=2.8, and Python 3.11–3.13; use CoreML 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 |

+ | 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 21 local export targets, artifacts produced, and supported args. Read when using any format beyond onnx/engine/openvino/coreml.

+ - `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.