CNN: Should I include null examples in the training set for image segmentation The 2019 Stack Overflow Developer Survey Results Are InHow do CNNs use a model and find the object(s) desired?Supervised learning for image segmentationBest approach for image recognition/classification with few training dataWhat principle is behind semantic segmenation with CNNs?Type of images used to train a neural networkInterpreting confusion matrix and validation results in convolutional networksImage segmentation training set labelingKeras intuition/guidelines for setting epochs and batch sizeRelationship between objects - ConvNetsHow to properly rotate image and labels for semantic segmentation data augmentation in Tensorflow?
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CNN: Should I include null examples in the training set for image segmentation
The 2019 Stack Overflow Developer Survey Results Are InHow do CNNs use a model and find the object(s) desired?Supervised learning for image segmentationBest approach for image recognition/classification with few training dataWhat principle is behind semantic segmenation with CNNs?Type of images used to train a neural networkInterpreting confusion matrix and validation results in convolutional networksImage segmentation training set labelingKeras intuition/guidelines for setting epochs and batch sizeRelationship between objects - ConvNetsHow to properly rotate image and labels for semantic segmentation data augmentation in Tensorflow?
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I am new to CNNs and was trying to use the Spacenet Building footprints data for some semantic image segmentation. However, in looking at the training images I found that a large number of images have no buildings at all. I mean depending on which Spacenet file you are looking at, it seems like over half if not 75% of the Las Vegas images have no buildings--not even sure why these images are included in the data. I would call these null examples since they have no labeled geometries on them for building footprints.
My sense is that I would not need to include these in the training set, since they don't really contribute anything to the training of the model. It just seems really odd that the Spacenet folks would include so many images with no buildings in their training set. I mean it is a really high percentage.
Can anyone tell me if I should include all of the images with no geometries in the training set for the model? I guess I would have to set the label to all zeros for images like these.
Thanks.
machine-learning deep-learning convnet
asked Mar 30 at 20:03
krishnabkrishnab
1012
$endgroup$
add a comment |
$begingroup$
I am new to CNNs and was trying to use the Spacenet Building footprints data for some semantic image segmentation. However, in looking at the training images I found that a large number of images have no buildings at all. I mean depending on which Spacenet file you are looking at, it seems like over half if not 75% of the Las Vegas images have no buildings--not even sure why these images are included in the data. I would call these null examples since they have no labeled geometries on them for building footprints.
My sense is that I would not need to include these in the training set, since they don't really contribute anything to the training of the model. It just seems really odd that the Spacenet folks would include so many images with no buildings in their training set. I mean it is a really high percentage.
Can anyone tell me if I should include all of the images with no geometries in the training set for the model? I guess I would have to set the label to all zeros for images like these.
Thanks.
machine-learning deep-learning convnet
asked Mar 30 at 20:03
krishnabkrishnab
1012
$endgroup$
$begingroup$
First try to train with and without null images to see whether performance suffers. If suffered, they might had something in mind.
$endgroup$
– Esmailian
Mar 30 at 20:30
add a comment |
$begingroup$
I am new to CNNs and was trying to use the Spacenet Building footprints data for some semantic image segmentation. However, in looking at the training images I found that a large number of images have no buildings at all. I mean depending on which Spacenet file you are looking at, it seems like over half if not 75% of the Las Vegas images have no buildings--not even sure why these images are included in the data. I would call these null examples since they have no labeled geometries on them for building footprints.
My sense is that I would not need to include these in the training set, since they don't really contribute anything to the training of the model. It just seems really odd that the Spacenet folks would include so many images with no buildings in their training set. I mean it is a really high percentage.
Can anyone tell me if I should include all of the images with no geometries in the training set for the model? I guess I would have to set the label to all zeros for images like these.
Thanks.
machine-learning deep-learning convnet
asked Mar 30 at 20:03
krishnabkrishnab
1012
$endgroup$
I am new to CNNs and was trying to use the Spacenet Building footprints data for some semantic image segmentation. However, in looking at the training images I found that a large number of images have no buildings at all. I mean depending on which Spacenet file you are looking at, it seems like over half if not 75% of the Las Vegas images have no buildings--not even sure why these images are included in the data. I would call these null examples since they have no labeled geometries on them for building footprints.
My sense is that I would not need to include these in the training set, since they don't really contribute anything to the training of the model. It just seems really odd that the Spacenet folks would include so many images with no buildings in their training set. I mean it is a really high percentage.
Can anyone tell me if I should include all of the images with no geometries in the training set for the model? I guess I would have to set the label to all zeros for images like these.
Thanks.
machine-learning deep-learning convnet
machine-learning deep-learning convnet
asked Mar 30 at 20:03
krishnabkrishnab
1012
asked Mar 30 at 20:03
krishnabkrishnab
1012
asked Mar 30 at 20:03
krishnabkrishnab
1012
asked Mar 30 at 20:03
krishnabkrishnab
1012
asked Mar 30 at 20:03
krishnabkrishnab
1012
1012
$begingroup$
First try to train with and without null images to see whether performance suffers. If suffered, they might had something in mind.
$endgroup$
– Esmailian
Mar 30 at 20:30
add a comment |
$begingroup$
First try to train with and without null images to see whether performance suffers. If suffered, they might had something in mind.
$endgroup$
– Esmailian
Mar 30 at 20:30
$begingroup$
First try to train with and without null images to see whether performance suffers. If suffered, they might had something in mind.
$endgroup$
– Esmailian
Mar 30 at 20:30
$begingroup$
First try to train with and without null images to see whether performance suffers. If suffered, they might had something in mind.
$endgroup$
– Esmailian
Mar 30 at 20:30
add a comment |
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$begingroup$
First try to train with and without null images to see whether performance suffers. If suffered, they might had something in mind.
$endgroup$
– Esmailian
Mar 30 at 20:30