Export RF-DETR Model to ONNX¶

RF-DETR supports exporting models to the ONNX format, which enables interoperability with various inference frameworks and can improve deployment efficiency.

Installation¶

To export your model, first install the onnxexport extension:

pip install "rfdetr[onnxexport]"

Basic Export¶

Export your trained model to ONNX format:

Object DetectionImage Segmentation

from rfdetr import RFDETRMedium

model = RFDETRMedium(pretrain_weights="<path/to/checkpoint.pth>")

model.export()

from rfdetr import RFDETRSegMedium

model = RFDETRSegMedium(pretrain_weights="<path/to/checkpoint.pth>")

model.export()

This command saves the ONNX model to the output directory by default.

Export Parameters¶

The export() method accepts several parameters to customize the export process:

Parameter	Default	Description
`output_dir`	`"output"`	Directory where the exported ONNX model will be saved.
`infer_dir`	`None`	Path to an image file to use for tracing. If not provided, a random dummy image is generated.
`simplify`	`False`	Whether to simplify the ONNX model using onnxsim for better compatibility and performance.
`backbone_only`	`False`	Export only the backbone feature extractor instead of the full model.
`opset_version`	`17`	ONNX opset version to use for export. Higher versions support more operations.
`verbose`	`True`	Whether to print verbose export information.
`force`	`False`	Force re-export even if simplified model already exists.
`shape`	`None`	Input shape as tuple `(height, width)`. Must be divisible by 14. If not provided, uses the model's default resolution.
`batch_size`	`1`	Batch size for the exported model.

Advanced Export Examples¶

Export with Custom Output Directory¶

from rfdetr import RFDETRMedium

model = RFDETRMedium(pretrain_weights="<path/to/checkpoint.pth>")

model.export(output_dir="exports/my_model")

Export with Simplification¶

Simplifying the ONNX model can improve inference performance and compatibility with various runtimes:

from rfdetr import RFDETRMedium

model = RFDETRMedium(pretrain_weights="<path/to/checkpoint.pth>")

model.export(simplify=True)

Export with Custom Resolution¶

Export the model with a specific input resolution (must be divisible by 14):

from rfdetr import RFDETRMedium

model = RFDETRMedium(pretrain_weights="<path/to/checkpoint.pth>")

model.export(shape=(560, 560))

Export Backbone Only¶

Export only the backbone feature extractor for use in custom pipelines:

from rfdetr import RFDETRMedium

model = RFDETRMedium(pretrain_weights="<path/to/checkpoint.pth>")

model.export(backbone_only=True)

Output Files¶

After running the export, you will find the following files in your output directory:

inference_model.onnx - The exported ONNX model (or backbone_model.onnx if backbone_only=True)
inference_model.sim.onnx - The simplified ONNX model (if simplify=True)

Using the Exported Model¶

Once exported, you can use the ONNX model with various inference frameworks:

ONNX Runtime¶

import onnxruntime as ort
import numpy as np
from PIL import Image

# Load the ONNX model
session = ort.InferenceSession("output/inference_model.onnx")

# Prepare input image
image = Image.open("image.jpg").convert("RGB")
image = image.resize((560, 560))  # Resize to model's input resolution
image_array = np.array(image).astype(np.float32) / 255.0

# Normalize
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
image_array = (image_array - mean) / std

# Convert to NCHW format
image_array = np.transpose(image_array, (2, 0, 1))
image_array = np.expand_dims(image_array, axis=0)

# Run inference
outputs = session.run(None, {"input": image_array})
boxes, labels = outputs

Next Steps¶

After exporting your model, you may want to:

Deploy to Roboflow for cloud-based inference and workflow integration
Use the ONNX model with TensorRT for optimized GPU inference
Integrate with edge deployment frameworks like ONNX Runtime or OpenVINO