Update script and add output from examples to readme.

examples/onnxruntime/resnet50/README.md

@@ -50,7 +50,63 @@ $ pip list | grep onnxruntime
 $ python resnet50.py
 ```
 
-example output:
+## 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 guitars we show three different variants of the same item in a class with different backgrounds as well as background shapes
+
+
+using scope.jpg. Image of an oscilliscope which is in the imagenet class labels
+
+oscilloscope 0.99869776
+radio 0.00051740184
+screen 0.00044122382
+monitor 8.556517e-05
+tape player 5.1727424e-05
+resnet50, time = 61.98 ms
+
+
+using screwdrivers.jpg. Image of screwdrivers which are in the imagenet class labels
+
+screwdriver 0.96512324
+carpenter's kit 0.03258186
+hammer 0.0007571124
+ballpoint 0.00058690814
+can opener 0.00026641905
+resnet50, time = 109.67 ms
+
+
+using guitar.jpg. Image of an non traditional electric guitar shape (Telecaster) which is in the imagenet class labels
+
+electric guitar 0.4413027
+acoustic guitar 0.14725313
+Band Aid 0.14059556
+pick 0.076821454
+rule 0.020968033
+resnet50, time = 77.01 ms
+
+
+using guitar2.jpg. Image of a les paul stype electric guitar which is in the imagenet classes
+
+electric guitar 0.7114628
+acoustic guitar 0.23226906
+banjo 0.044191252
+pick 0.0056983875
+stage 0.0013321621
+
+
+using guitar3.jpg. Image of a super strat 7 string style electric guitar which is in the imagenet classes
+
+electric guitar 0.71952045
+prayer rug 0.08914763
+banjo 0.078091994
+acoustic guitar 0.056061186
+violin 0.021640636
+resnet50, time = 71.71 ms
+
+
+using bird.jpg. Image of a cockatiel not contained in the imagenet class labels
 
 African grey 0.5883207
 kite 0.06284781

examples/onnxruntime/resnet50/resnet50.py

@@ -57,7 +57,7 @@ torch.onnx.export(
 
 # Pre-processing for ResNet-50 Inferencing, from https://pytorch.org/hub/pytorch_vision_resnet/
 resnet50.eval()
-filename = 'bird.jpg'  # change to your filename
+filename = 'guitar3.jpg'  # change to your filename
 
 input_image = Image.open(filename)
 preprocess = T.Compose([
@@ -102,6 +102,6 @@ def run_sample(session, image_file, categories, inputs):
     return ort_outputs
 
 
-ort_output = run_sample(session_fp32, 'bird.jpg', categories, input_batch)
+ort_output = run_sample(session_fp32, 'guitar3.jpg', categories, input_batch)
 print("resnet50, time = {} ms".format(
     format(sum(latency) * 1000 / len(latency), '.2f')))