This example demonstrates how to perform an MIGraphX Python API inference through onnxruntime. The model used here is from Torchvision's pretrained resnet50 model
This example demonstrates how to perform an MIGraphX Python API resnet50 inference through onnxruntime. The model used here is from Torchvision's pretrained resnet50 model
## Content
-[Basic Setup](#Basic-Setup)
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@@ -53,10 +53,14 @@ $ python resnet50.py
## Example Output:
We changes the target image to what's found in the example folder which contains three inclass images and one out of class image.
For each run the target image was changed. Stock images in the example folder which contains three in-class images types and one out of class image.
For guitars we show three different variants of the same item in a class with different backgrounds as well as background shapes
For the tools (scope.jpg and screwdrivers.jpg) these are both in-class images
For the bird.jpg image, the imagenet_classes.txt generated doesn't contain that of a cockatiel and thus the model attempts to find the closet
match to the animal found in the image
using scope.jpg. Image of an oscilliscope which is in the imagenet class labels
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@@ -95,6 +99,7 @@ acoustic guitar 0.23226906
banjo 0.044191252
pick 0.0056983875
stage 0.0013321621
resnet50, time = 43.09 ms
using guitar3.jpg. Image of a super strat 7 string style electric guitar which is in the imagenet classes
