How should I collect data for an “identifier” rather than classifier?
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I'm trying to build a CNN to identify whether or not a picture contains a lion. One of my classes would obviously be filled with pictures of lions, and pictures that have lions.
What about the data I put in my not_lion class? Should I fill it with images of random things (for example, take a few images from each class in ImageNet)? Should I put in pictures of things similar to lions, like tigers, so the network better learns what a lion actually is?
My idea is to make 50% of the not_lion class pictures of tigers, pumas, etc, and 50% random stuff - I think that this would make the network learn what a lion actually is, and also ensure the network doesn't classify a laptop as a lion.
cnn data
asked 2 hours ago
Nikhil MuraliNikhil Murali
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$begingroup$
I'm trying to build a CNN to identify whether or not a picture contains a lion. One of my classes would obviously be filled with pictures of lions, and pictures that have lions.
What about the data I put in my not_lion class? Should I fill it with images of random things (for example, take a few images from each class in ImageNet)? Should I put in pictures of things similar to lions, like tigers, so the network better learns what a lion actually is?
My idea is to make 50% of the not_lion class pictures of tigers, pumas, etc, and 50% random stuff - I think that this would make the network learn what a lion actually is, and also ensure the network doesn't classify a laptop as a lion.
cnn data
asked 2 hours ago
Nikhil MuraliNikhil Murali
1
New contributor
Nikhil Murali is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
I'm trying to build a CNN to identify whether or not a picture contains a lion. One of my classes would obviously be filled with pictures of lions, and pictures that have lions.
What about the data I put in my not_lion class? Should I fill it with images of random things (for example, take a few images from each class in ImageNet)? Should I put in pictures of things similar to lions, like tigers, so the network better learns what a lion actually is?
My idea is to make 50% of the not_lion class pictures of tigers, pumas, etc, and 50% random stuff - I think that this would make the network learn what a lion actually is, and also ensure the network doesn't classify a laptop as a lion.
cnn data
asked 2 hours ago
Nikhil MuraliNikhil Murali
1
New contributor
Nikhil Murali is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm trying to build a CNN to identify whether or not a picture contains a lion. One of my classes would obviously be filled with pictures of lions, and pictures that have lions.
What about the data I put in my not_lion class? Should I fill it with images of random things (for example, take a few images from each class in ImageNet)? Should I put in pictures of things similar to lions, like tigers, so the network better learns what a lion actually is?
My idea is to make 50% of the not_lion class pictures of tigers, pumas, etc, and 50% random stuff - I think that this would make the network learn what a lion actually is, and also ensure the network doesn't classify a laptop as a lion.
cnn data
cnn data
asked 2 hours ago
Nikhil MuraliNikhil Murali
1
New contributor
Nikhil Murali is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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asked 2 hours ago
Nikhil MuraliNikhil Murali
1
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asked 2 hours ago
Nikhil MuraliNikhil Murali
1
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asked 2 hours ago
Nikhil MuraliNikhil Murali
1
asked 2 hours ago
Nikhil MuraliNikhil Murali
1
1
New contributor
Nikhil Murali is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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New contributor
Nikhil Murali is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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It depends on the use you are giving. The $not.lion$ class should contain sufficient data of different things, but also should contain tigers, cats, african landscapes without lions, etc.
Your idea of 50-50 is good.
Add this: The weights $w$ could help you define where your model should focus: Lesser weights when there is something easily distinguishable from a lion like a computer, but larger weights when there is an african landscape.
answered 2 hours ago
Juan Esteban de la CalleJuan Esteban de la Calle
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$begingroup$
It depends on the use you are giving. The $not.lion$ class should contain sufficient data of different things, but also should contain tigers, cats, african landscapes without lions, etc.
Your idea of 50-50 is good.
Add this: The weights $w$ could help you define where your model should focus: Lesser weights when there is something easily distinguishable from a lion like a computer, but larger weights when there is an african landscape.
answered 2 hours ago
Juan Esteban de la CalleJuan Esteban de la Calle
35811
New contributor
Juan Esteban de la Calle is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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add a comment |
$begingroup$
It depends on the use you are giving. The $not.lion$ class should contain sufficient data of different things, but also should contain tigers, cats, african landscapes without lions, etc.
Your idea of 50-50 is good.
Add this: The weights $w$ could help you define where your model should focus: Lesser weights when there is something easily distinguishable from a lion like a computer, but larger weights when there is an african landscape.
answered 2 hours ago
Juan Esteban de la CalleJuan Esteban de la Calle
35811
New contributor
Juan Esteban de la Calle is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
It depends on the use you are giving. The $not.lion$ class should contain sufficient data of different things, but also should contain tigers, cats, african landscapes without lions, etc.
Your idea of 50-50 is good.
Add this: The weights $w$ could help you define where your model should focus: Lesser weights when there is something easily distinguishable from a lion like a computer, but larger weights when there is an african landscape.
answered 2 hours ago
Juan Esteban de la CalleJuan Esteban de la Calle
35811
New contributor
Juan Esteban de la Calle is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
It depends on the use you are giving. The $not.lion$ class should contain sufficient data of different things, but also should contain tigers, cats, african landscapes without lions, etc.
Your idea of 50-50 is good.
Add this: The weights $w$ could help you define where your model should focus: Lesser weights when there is something easily distinguishable from a lion like a computer, but larger weights when there is an african landscape.
answered 2 hours ago
Juan Esteban de la CalleJuan Esteban de la Calle
35811
New contributor
Juan Esteban de la Calle is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 2 hours ago
Juan Esteban de la CalleJuan Esteban de la Calle
35811
New contributor
Juan Esteban de la Calle is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 2 hours ago
Juan Esteban de la CalleJuan Esteban de la Calle
35811
answered 2 hours ago
Juan Esteban de la CalleJuan Esteban de la Calle
35811
35811
New contributor
Juan Esteban de la Calle is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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New contributor
Juan Esteban de la Calle is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Juan Esteban de la Calle is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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