Compatibility range of conversion

You can use LaunchX converter to automatically convert the AI model's framework to the target framework.

Converting case

  1. ONNX to TensorRT
  2. ONNX to TensorFlow Lite
  3. ONNX to OpenVINO
  4. TensorFlow-Keras to TensorFlow Lite

Compatible model

The input layer of the uploaded model should be as follows.

  • Only single-input models are supported.
  • The four-dimensional array structure of images should be organized Batch, Number of Channels, Height, and Width.
  1. Batch size: The number of combined input datasets that the model processes simultaneously.
  2. Channel: 3 for RGB or BGR and 1 for Grayscale.
  3. Input size: In computer vision tasks, input size refers to the size of the input images.

ONNX to TensorRT

Supported JetPack-ONNX version

Target DeviceJetPack versionInput datatypeBatch sizeChannelInput sizeOutput datatype
NVIDIA Jetson Nano4.6, 4.4.1FP321~4 (Static), Dynamic1~4height, widthFP16
NVIDIA Jetson Xavier NX5.0.2, 4.6FP321~4 (Static), Dynamic1~4height, widthFP16
NVIDIA Jetson TX24.6FP321~4 (Static), Dynamic1~4height, widthFP16
NVIDIA Jetson AGX Xavier4.6FP321~4 (Static), Dynamic1~4height, widthFP16
NVIDIA Jetson AGX Orin5.0.1FP321~4 (Static), Dynamic1~4height, widthFP16
NVIDIA Jetson Orin Nano6.0FP321~4 (Static), Dynamic1~4height, widthFP16
NVIDIA T4-FP321~4 (Static), Dynamic1~4height, widthFP16

ONNX to TFlite

Input datatypeBatch sizeChannelInput sizeOutput datatype
FP321~4 (Static), Dynamic1~4height, widthFP16
INT8

ONNX to OpenVino

Input datatypeBatch sizeChannelInput sizeOutput datatype
FP321~4 (Static), Dynamic1~4height, widthFP16

TensorFlow to TensorFlowLite

Input datatypeBatch sizeChannelInput sizeOutput datatype
FP321~4 (Static, Dynamic)1~4height, widthFP16
INT8