Fully convolutional networks with partially segmented data
$begingroup$
I am working on classifying satellite imagery using a fully convolutional approach. The images in question are ~8000x8000 pixels and the training data does not label every pixel in the image. Also, class balance is not present in the dataset.
Currently, I break the 8000x8000 images into smaller tiles that can fit in GPU memory (~600x600), and save the patches that contain training data to disk. If a given image patch contains more than one class, I save two patches, with two different segmentation maps. When training the model, I shuffle all of the patches that I saved and feed them into the model.
When training the model, I first load an image patch and its corresponding segmentation mask for all representative classes. Each segmentation mask has a different amount of labeled pixels, so I find the minimum number of labeled pixels that is present in the image data that's in memory.
I then take a random sample of size minimum number of pixels from the segmentation masks. This step ensures class balance through the images in the batch.
The picture below may clear things up.
The top row is the fully segmented data. The bottom is the
random sample of data, with number of examples determined by the minimum examples in each class. Each column represents a different class. The fourth class (fourth column) in the picture had the minimum number of examples present in this batch.
After doing this random sampling, I feed each example into the network one by one as only one example can fit in GPU memory at a time.
On every batch, the number of training examples (labeled pixels) changes - from 50 to 150,000, depending on how many labeled points there are in the minimally annotated example.
I am wondering if this extreme difference in number of labeled examples between batches is problematic, and if there is a better approach.
machine-learning convnet image-classification
asked yesterday
Tom CTom C
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add a comment |
$begingroup$
I am working on classifying satellite imagery using a fully convolutional approach. The images in question are ~8000x8000 pixels and the training data does not label every pixel in the image. Also, class balance is not present in the dataset.
Currently, I break the 8000x8000 images into smaller tiles that can fit in GPU memory (~600x600), and save the patches that contain training data to disk. If a given image patch contains more than one class, I save two patches, with two different segmentation maps. When training the model, I shuffle all of the patches that I saved and feed them into the model.
When training the model, I first load an image patch and its corresponding segmentation mask for all representative classes. Each segmentation mask has a different amount of labeled pixels, so I find the minimum number of labeled pixels that is present in the image data that's in memory.
I then take a random sample of size minimum number of pixels from the segmentation masks. This step ensures class balance through the images in the batch.
The picture below may clear things up.
The top row is the fully segmented data. The bottom is the
random sample of data, with number of examples determined by the minimum examples in each class. Each column represents a different class. The fourth class (fourth column) in the picture had the minimum number of examples present in this batch.
After doing this random sampling, I feed each example into the network one by one as only one example can fit in GPU memory at a time.
On every batch, the number of training examples (labeled pixels) changes - from 50 to 150,000, depending on how many labeled points there are in the minimally annotated example.
I am wondering if this extreme difference in number of labeled examples between batches is problematic, and if there is a better approach.
machine-learning convnet image-classification
asked yesterday
Tom CTom C
1
New contributor
Tom C 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 am working on classifying satellite imagery using a fully convolutional approach. The images in question are ~8000x8000 pixels and the training data does not label every pixel in the image. Also, class balance is not present in the dataset.
Currently, I break the 8000x8000 images into smaller tiles that can fit in GPU memory (~600x600), and save the patches that contain training data to disk. If a given image patch contains more than one class, I save two patches, with two different segmentation maps. When training the model, I shuffle all of the patches that I saved and feed them into the model.
When training the model, I first load an image patch and its corresponding segmentation mask for all representative classes. Each segmentation mask has a different amount of labeled pixels, so I find the minimum number of labeled pixels that is present in the image data that's in memory.
I then take a random sample of size minimum number of pixels from the segmentation masks. This step ensures class balance through the images in the batch.
The picture below may clear things up.
The top row is the fully segmented data. The bottom is the
random sample of data, with number of examples determined by the minimum examples in each class. Each column represents a different class. The fourth class (fourth column) in the picture had the minimum number of examples present in this batch.
After doing this random sampling, I feed each example into the network one by one as only one example can fit in GPU memory at a time.
On every batch, the number of training examples (labeled pixels) changes - from 50 to 150,000, depending on how many labeled points there are in the minimally annotated example.
I am wondering if this extreme difference in number of labeled examples between batches is problematic, and if there is a better approach.
machine-learning convnet image-classification
asked yesterday
Tom CTom C
1
New contributor
Tom C is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am working on classifying satellite imagery using a fully convolutional approach. The images in question are ~8000x8000 pixels and the training data does not label every pixel in the image. Also, class balance is not present in the dataset.
Currently, I break the 8000x8000 images into smaller tiles that can fit in GPU memory (~600x600), and save the patches that contain training data to disk. If a given image patch contains more than one class, I save two patches, with two different segmentation maps. When training the model, I shuffle all of the patches that I saved and feed them into the model.
When training the model, I first load an image patch and its corresponding segmentation mask for all representative classes. Each segmentation mask has a different amount of labeled pixels, so I find the minimum number of labeled pixels that is present in the image data that's in memory.
I then take a random sample of size minimum number of pixels from the segmentation masks. This step ensures class balance through the images in the batch.
The picture below may clear things up.
The top row is the fully segmented data. The bottom is the
random sample of data, with number of examples determined by the minimum examples in each class. Each column represents a different class. The fourth class (fourth column) in the picture had the minimum number of examples present in this batch.
After doing this random sampling, I feed each example into the network one by one as only one example can fit in GPU memory at a time.
On every batch, the number of training examples (labeled pixels) changes - from 50 to 150,000, depending on how many labeled points there are in the minimally annotated example.
I am wondering if this extreme difference in number of labeled examples between batches is problematic, and if there is a better approach.
machine-learning convnet image-classification
machine-learning convnet image-classification
asked yesterday
Tom CTom C
1
New contributor
Tom C is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
Tom CTom C
1
New contributor
Tom C is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
Tom CTom C
1
New contributor
Tom C is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
Tom CTom C
1
asked yesterday
Tom CTom C
1
1
New contributor
Tom C is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Tom C is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Tom C is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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