Simple question about prediction classes of item in question vs not item in question
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Let's say I wanted to use transfer learning to train a model to detect object A vs everything else. In this case, do I provide 2 types of input, images of object A and images of everything else, and then have the final layer of the model output either object A or not-object A?
What about in the case where I want object A vs object B vs everything else. Would it make sense in this case to provide images of A and B and then have only two output classes, but based on the confidence of the output, interpret it as 3 classes? Say that it's object A if the confidence in that is > 50%, object B if the confidence in that is > 50%, and anything else if neither of those two conditions are met?
machine-learning transfer-learning
asked Aug 14 '18 at 22:22
John AllardJohn Allard
1164
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bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
Let's say I wanted to use transfer learning to train a model to detect object A vs everything else. In this case, do I provide 2 types of input, images of object A and images of everything else, and then have the final layer of the model output either object A or not-object A?
What about in the case where I want object A vs object B vs everything else. Would it make sense in this case to provide images of A and B and then have only two output classes, but based on the confidence of the output, interpret it as 3 classes? Say that it's object A if the confidence in that is > 50%, object B if the confidence in that is > 50%, and anything else if neither of those two conditions are met?
machine-learning transfer-learning
asked Aug 14 '18 at 22:22
John AllardJohn Allard
1164
$endgroup$
bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
Let's say I wanted to use transfer learning to train a model to detect object A vs everything else. In this case, do I provide 2 types of input, images of object A and images of everything else, and then have the final layer of the model output either object A or not-object A?
What about in the case where I want object A vs object B vs everything else. Would it make sense in this case to provide images of A and B and then have only two output classes, but based on the confidence of the output, interpret it as 3 classes? Say that it's object A if the confidence in that is > 50%, object B if the confidence in that is > 50%, and anything else if neither of those two conditions are met?
machine-learning transfer-learning
asked Aug 14 '18 at 22:22
John AllardJohn Allard
1164
$endgroup$
Let's say I wanted to use transfer learning to train a model to detect object A vs everything else. In this case, do I provide 2 types of input, images of object A and images of everything else, and then have the final layer of the model output either object A or not-object A?
What about in the case where I want object A vs object B vs everything else. Would it make sense in this case to provide images of A and B and then have only two output classes, but based on the confidence of the output, interpret it as 3 classes? Say that it's object A if the confidence in that is > 50%, object B if the confidence in that is > 50%, and anything else if neither of those two conditions are met?
machine-learning transfer-learning
machine-learning transfer-learning
asked Aug 14 '18 at 22:22
John AllardJohn Allard
1164
asked Aug 14 '18 at 22:22
John AllardJohn Allard
1164
asked Aug 14 '18 at 22:22
John AllardJohn Allard
1164
asked Aug 14 '18 at 22:22
John AllardJohn Allard
1164
asked Aug 14 '18 at 22:22
John AllardJohn Allard
1164
1164
bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
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It depends if the percentages are required to sum to 100%. Typically when training on only 2 classes, the model will make predicts that sum to 100% for 2 classes. There will be no chance for out-of-class predictions.
answered Aug 15 '18 at 3:11
Brian SpieringBrian Spiering
4,2681129
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1 Answer
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$begingroup$
It depends if the percentages are required to sum to 100%. Typically when training on only 2 classes, the model will make predicts that sum to 100% for 2 classes. There will be no chance for out-of-class predictions.
answered Aug 15 '18 at 3:11
Brian SpieringBrian Spiering
4,2681129
$endgroup$
add a comment |
$begingroup$
It depends if the percentages are required to sum to 100%. Typically when training on only 2 classes, the model will make predicts that sum to 100% for 2 classes. There will be no chance for out-of-class predictions.
answered Aug 15 '18 at 3:11
Brian SpieringBrian Spiering
4,2681129
$endgroup$
add a comment |
$begingroup$
It depends if the percentages are required to sum to 100%. Typically when training on only 2 classes, the model will make predicts that sum to 100% for 2 classes. There will be no chance for out-of-class predictions.
answered Aug 15 '18 at 3:11
Brian SpieringBrian Spiering
4,2681129
$endgroup$
It depends if the percentages are required to sum to 100%. Typically when training on only 2 classes, the model will make predicts that sum to 100% for 2 classes. There will be no chance for out-of-class predictions.
answered Aug 15 '18 at 3:11
Brian SpieringBrian Spiering
4,2681129
answered Aug 15 '18 at 3:11
Brian SpieringBrian Spiering
4,2681129
answered Aug 15 '18 at 3:11
Brian SpieringBrian Spiering
4,2681129
answered Aug 15 '18 at 3:11
Brian SpieringBrian Spiering
4,2681129
4,2681129
add a comment |
add a comment |
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