yolo-export · diff
git:20260904.9c3b8ec to git:20261006.e5cd7b8
24 added, 48 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), 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 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.
+ 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).
+ 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 |
+ | 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 |
- Full 21-target local export matrix with per-format supported args: `format-matrix.md`
- (this folder). Platform currently offers 20 deployment formats.
+ Full 21-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` |
| `workspace` | None | TensorRT builder GiB — lower if the build OOMs |
- | `device` | None | `device=0` required for TensorRT; also speeds INT8 calibration |
+ | `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`.
+ 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.
+ 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.
+ - 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.
+ - 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 |
+ | 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 |
## 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 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.
+ If the installed version rejects an argument, trust the error text (format and quantize errors list valid values) and `yolo cfg` over this file.