libreyolo export

Converts one checkpoint into one deployment format and writes the artifact under weights/. The format decides which of the arguments below apply.

Command: libreyolo export
Required: model
Output: weights/<checkpoint-stem>[_fp16|_int8]<format-suffix>

Synopsis

bash

libreyolo export model=<name|path> [format=<format>] [key=value ...]

Arguments are key=value pairs, and POSIX form works too, so format=onnx and --format onnx are the same argument.

Arguments

Argument	Default	Meaning
`model`		Model weights `.pt`. Required
`format`	`onnx`	Export format: `onnx`, `torchscript`, `executorch`, `tensorrt`, `openvino`, `paddle`, `mnn`, `rknn`, `ncnn`, `tflite`, `coreml`, `coreai`
`name`		RKNN target platform, currently `rk3588` only. Rejected with any other format
`imgsz`		Input image size: `640` or `480x640` (HxW). `480,640` is also accepted. The model's own size when unset
`batch`	`1`	Export batch size
`half`	`false`	FP16 precision
`int8`	`false`	INT8 quantization
`dynamic`	`false`	Dynamic input shapes (ONNX)
`simplify`	`true`	ONNX graph simplification
`nms`	`false`	Embed NMS in the model. ONNX and CoreML only
`conf`	`0.25`	Confidence threshold for embedded NMS
`iou`	`0.45`	IoU threshold for embedded NMS
`max_det`	`300`	Maximum detections for ONNX embedded NMS
`opset`		ONNX opset version. Chosen automatically when unset
`data`		Calibration data for INT8
`fraction`	`1.0`	Fraction of calibration data to use
`device`	`auto`	Device for tracing
`allow_download_scripts`	`false`	Allow embedded Python in dataset YAML download blocks
`json`	`false`	JSON output to stdout
`quiet`	`false`	Suppress stderr
`verbose`	`false`	Verbose export logging
`verify`	`false`	Run the RKNN Toolkit2 PC simulator and compare against ONNX Runtime. RKNN only
`help_json`	`false`	Dump command schema as JSON and exit

engine is an alias for tensorrt and litert an alias for tflite. Both resolve to the canonical name before anything is written, so the JSON output and the log line always report tensorrt or tflite.

Examples

Basic

# Writes weights/LibreYOLO9s.onnxlibreyolo export model=LibreYOLO9s.pt format=onnx imgsz=640

NMS inside the graph

libreyolo export model=LibreYOLO9s.pt format=onnx \  nms=true conf=0.25 iou=0.45 max_det=300

Run the artifact

libreyolo export model=LibreYOLO9s.pt format=onnx imgsz=640 # The factory routes on the file suffix, so the export loads like a checkpoint.libreyolo predict model=weights/LibreYOLO9s.onnx \  source=https://raw.githubusercontent.com/LibreYOLO/libreyolo/release/libreyolo/assets/parkour.jpg

Notes

Where the file lands

The command takes no output path. The artifact is written to weights/, named after the source checkpoint's stem plus the format's suffix, with _fp16 or _int8 inserted when one of those precisions was requested. LibreYOLO9s.pt exported to ONNX at FP16 becomes weights/LibreYOLO9s_fp16.onnx. The JSON result carries the resolved output_path, the file size in MB, and the input shape as [batch, 3, height, width].

Combinations that are refused

nms=true is accepted for ONNX and CoreML and refused for every other format with nms_unsupported_format. On ONNX it forces dynamic off, since the embedded graph is fixed at batch 1, and says so on stderr. On CoreML it takes conf and iou but not max_det, so a non-default max_det alongside format=coreml nms=true exits with config_unsupported.

half=true together with int8=true is not an error. INT8 wins, half is dropped, and a warning goes to stderr.

name and verify are RKNN options today. Passing either with another format exits with config_unsupported rather than being ignored.

Which formats a family supports

Support is per family and per task, not global. libreyolo formats family=<family> task=<task> prints the tier for each format for that combination, with the reason and any constraint attached. See libreyolo formats for the arguments.

Some formats need an optional install and some need a toolchain. A missing Python dependency exits with export_dep_missing; a precision the format cannot produce exits with format_precision_unsupported.

Running what you exported

Exported artifacts load through the same model factory as checkpoints, keyed on the file suffix, so libreyolo predict model=weights/LibreYOLO9s.onnx works without any further conversion. Three prediction options are the exception and are refused on runtime backends: tiling, overlap_ratio and output_file_format.

Two deployment targets have pages of their own: NVIDIA DeepStream and NVIDIA Jetson.

Output and exit codes

stdout carries the result; progress goes to stderr. The exit code is 0 on success, 2 for a usage or configuration error, 4 when the model cannot be loaded, 5 for an unknown format, a missing export dependency, an unsupported precision or a refused embedded-NMS request, and 1 for other runtime failures.

Related: libreyolo quantize, which stays in PyTorch and writes a checkpoint rather than a deployment artifact.