Comments (17)
Hey @bemoregt,
Can you please attach a screenshot of the same?
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@bemoregt
Can you tell me the prediction score for an image with just the horizontal lines (background) without any diagonal line?
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@bemoregt
"line or hole classes Accuarcys are same."
I don't understand what you mean by this
Can you answer a few questions:
- Have you changed the architecture of resnet to have only three output units?
- Have you trained the changed resnet with your own training data? If so, how many samples did you train it on?
- The sum of all predictions should be 100%. Are you passing the output prediction scores through a softmax?
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What is the classification label for this image? @bemoregt
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@sar-gupta, label is "line".
in lables, 0:"normal", 1:"line", 2:"hole"
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@bemoregt
Can you check running gradcam with an image that has just one line, instead of multiple lines, and share the result?
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Those Horizontal lines are just background.
Diagonal line is a only defect object which I want to find.
Is that background lines are critical obstacle for real object heat mapping?
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I using target layer 4.2 or 4.1:
CONFIG = {
'resnet34': {
'target_layer': 'layer4.2',
'input_size': 224
},
# Add your model
}.get(arch)
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@bemoregt
Can you tell me the prediction acore for an image with just the horizontal lines (background) without any diagonal line?
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Normal class Classification Accuray Score may be 99.8% ..
line or hole classes Accuarcys are same.
GuidedBackpropagation mapping method is more capable than grad-CAM for my image data?
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Yes,
Can you answer a few questions:
1.Have you changed the architecture of resnet to have only three output units?
- I have using transfer learning with resnet(3-outpou FC layer) + pretrained imagenet.
2.Have you trained the changed resnet with your own training data? If so, how many samples did you train it on?
- About 5000 images per classes. Data augmented.
3.The sum of all predictions should be 100%. Are you passing the output prediction scores through a softmax?
- Yes, of course. all prediction sum is 100%. AUC of (line correct)/total is about 99.8%, I mean.
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@bemoregt
Okay
I need two more things for now to help you with troubleshooting.
Softmax scores of all three classes for the following:
- Image with diagonal line in it.
- Image without diagonal line.
Both of these should have the horizontal lines as background.
Thanks
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1.Image with diagonal line in it. >> Line Class
2.Image without diagonal line. >> Nomal class
Both scores are nearly 0.998 (softmax).
Thanks.
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@bemoregt Kindly provide the softmax scores for all three classes in both the cases.
To clarify, by softmax score, I mean the three outputs from softmax layer.
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Closing because the issue is not related to this project. However, discussion can be continued here.
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