I have trained the model in local on Tensorflow and converted that model to .onnx format.
and used these code to inference.
jetson-inference/imagenet-example-python-2.md at master · dusty-nv/jetson-inference · GitHub .
import jetson.inference
import jetson.utils
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(“filename”, type=str, help=“filename of the image to process”)
args = parser.parse_args()
img = jetson.utils.loadImage(args.filename)
net = jetson.inference.imageNet(‘alexnet’,[‘–model=/home/sudhir/Downloads/AJAX/ResNet18.onnx’,‘–input_blob=input_0’,‘–output_blob=output_0’,‘–labels=/home/sudhir/Downloads/AJAX/label.txt’])
class_idx, confidence = net.Classify(img)
class_desc = net.GetClassDesc(class_idx)
print(“image is recognized as ‘{:s}’ (class #{:d}) with {:f}% confidence”.format(class_desc, class_idx, confidence * 100))
and was getting error like these.
failed to parse ONNX model ‘/home/sudhir/Downloads/AJAX/ResNet18.onnx’
[0m [0;31m[TRT] device GPU, failed to load /home/sudhir/Downloads/AJAX/ResNet18.onnx
[0m [0;31m[TRT] failed to load /home/sudhir/Downloads/AJAX/ResNet18.onnx
[0m [0;31m[TRT] imageNet – failed to initialize.
how can i inference my own .onnx model on jetson nano