Simple question about prediction classes of item in question vs not item in question The 2019 Stack Overflow Developer Survey Results Are In Unicorn Meta Zoo #1: Why another podcast? Announcing the arrival of Valued Associate #679: Cesar Manara 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsConfidence intervals for binary classification probabilitiesMultilabel image classification: is it necessary to have traning data for each combination of labels?Fine-tuning a CNN for recognizing two classes, but also being able to tell if none of them is present in an imageUnsupervised Anomaly Detection in ImagesGTX 1080t ti rans out of memoryTransfer learning by concatenating the last classification layerTraining a model for object detectionHow does the multi-input deep learning work?Neural Network Model using Transfer Learning not learningOutput range of BERT model shrinks after fine-tuning on domain specific dataset
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Simple question about prediction classes of item in question vs not item in question
The 2019 Stack Overflow Developer Survey Results Are In
Unicorn Meta Zoo #1: Why another podcast?
Announcing the arrival of Valued Associate #679: Cesar Manara
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsConfidence intervals for binary classification probabilitiesMultilabel image classification: is it necessary to have traning data for each combination of labels?Fine-tuning a CNN for recognizing two classes, but also being able to tell if none of them is present in an imageUnsupervised Anomaly Detection in ImagesGTX 1080t ti rans out of memoryTransfer learning by concatenating the last classification layerTraining a model for object detectionHow does the multi-input deep learning work?Neural Network Model using Transfer Learning not learningOutput range of BERT model shrinks after fine-tuning on domain specific dataset
$begingroup$
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♦ 33 mins ago
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♦ 33 mins ago
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♦ 33 mins ago
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♦ 33 mins ago
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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$begingroup$
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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