Compatible Model Scope
Convert
You can automatically convert the AI model's framework to the target framework.
- Only single-input models are supported.
- The supported channel ordering format is 'Channel First'. The three-dimensional array structure of images should be organized in the order of Batch, Number of Channels, Height, and Width.
- Batch size: The number of combined input datasets that the model processes simultaneously.
- Channel: enter 3 for RGB channel and 1 for gray channel.
- Input size: In computer vision tasks, input size refers to the size of the input images.
ONNX to TensorRT
Target Device | SW version | Input datatype | Batch size | Channel | Input size | Output datatype |
---|---|---|---|---|---|---|
NVIDIA Jetson Nano | JetPack 4.4.1, JetPack 4.6 | FP32 | 1~4 (Static, Dynamic) | 1~4 | height, width | FP16 |
NVIDIA Jetson Xavier NX | JetPack 5.0.2, JetPack 4.6 | FP32 | 1~4 (Static, Dynamic) | 1~4 | height, width | FP16 |
NVIDIA Jetson TX2 | JetPack 4.6 | FP32 | 1~4 (Static, Dynamic) | 1~4 | height, width | FP16 |
NVIDIA Jetson AGX Xavier | JetPack 4.6 | FP32 | 1~4 (Static, Dynamic) | 1~4 | height, width | FP16 |
NVIDIA Jetson AGX Orin | JetPack 5.0.1 | FP32 | 1~4 (Static, Dynamic) | 1~4 | height, width | FP16 |
NVIDIA T4 | - | FP32 | 1~4 (Static, Dynamic) | 1~4 | height, width | FP16 |
ONNX to OpenVino
Input datatype | Batch size | Channel | Input size | Output datatype |
---|---|---|---|---|
FP32 | 1~4 (Static, Dynamic) | 1~4 | height, width | FP16 |
ONNX to TFlite
Input datatype | Batch size | Channel | Input size | Output datatype |
---|---|---|---|---|
FP32 | 1~4 (Static, Dynamic) | 1~4 | height, width | FP16 INT8 |
TensorFlow to TensorFlowLite
Input datatype | Batch size | Channel | Input size | Output datatype |
---|---|---|---|---|
FP32 | 1~4 (Static, Dynamic) | 1~4 | height, width | FP16 INT8 |
Benchmark
Measure the performance of the AI model on the target device. You can measure the actual performance on the devices listed below without the need to purchase or install the device.
Below is a table for benchmarking compatible model frameworks.
Arm MCU
Target Device | .tflite |
---|---|
Renesas RA8D1 (Arm Cortex-M85) | O (only INT8) |
Renesas RA8D1 (Arm Cortex-M85) with helium | O (only INT8) |
Alif Ensemble DevKit Gen2 (Arm Cortex-M85 + Ethos-U55) | O (only INT8) |
Alif Ensemble DevKit Gen2 (Arm Cortex-M85 + Ethos-U55) with helium | O (only INT8) |
NVIDIA
- When benchmarking on Jetson, it is essential for the model file and target device to match the Jetpack version.
Target Device | .tflite | .engine | .tflite | .onnx |
---|---|---|---|---|
Jetson Nano JetPack 4.4.1 | O | O | O | O |
Jetson Nano JetPack 4.6 | O | O | O | O |
Jetson Xavier NX JetPack 4.6 | O | O | O | O |
Jetson Xavier NX JetPack 5.0.2 | O | O | O | O |
Jetson TX2 JetPack 4.6 | O | O | O | O |
Jetson AGX Xavier JetPack 4.6 | O | O | O | O |
Jetson AGX Orin JetPack 5.0.1 | O | O | O | O |
AWS-T4 | O | O | O | O |
Raspberry Pi
Target Device | .tflite | .onnx |
---|---|---|
Raspberry Pi ZeroW | O | O |
Raspberry Pi Zero2W | O | O |
Raspberry Pi 2B | O | O |
Raspberry Pi 3B | O | O |
Raspberry Pi 3B+ | O | O |
Raspberry Pi 4B | O | O |
Intel
Target Device | .zip(bin+xml) |
---|---|
Xeon W-2223 | O |
Updated 16 days ago