How to visualize segmentation result with CrossEntropyLoss
$begingroup$
I know this question already has a clear answer about how to visualize segmentation result. My question is if I'm using the CrossEntropyLoss as the loss function, after training. The predicted classification (segmentation) result doesn't represent the class 0,1,2. It represents the unnormalized Log probability as told in cs231n standford class.
So if I want to visualize the segmentaion result, should I use the
- nn.LogSoftmax()
to get the classes and then plot them out?
or should I do a maximum function over the first dimension of my output.
For example, if the predicted output is size 3x1024x1024, how could I visualize the segmenation result?
Thank you in advance.
neural-network image-classification
asked 2 mins ago
xysongxysong
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$begingroup$
I know this question already has a clear answer about how to visualize segmentation result. My question is if I'm using the CrossEntropyLoss as the loss function, after training. The predicted classification (segmentation) result doesn't represent the class 0,1,2. It represents the unnormalized Log probability as told in cs231n standford class.
So if I want to visualize the segmentaion result, should I use the
- nn.LogSoftmax()
to get the classes and then plot them out?
or should I do a maximum function over the first dimension of my output.
For example, if the predicted output is size 3x1024x1024, how could I visualize the segmenation result?
Thank you in advance.
neural-network image-classification
asked 2 mins ago
xysongxysong
1
New contributor
xysong is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I know this question already has a clear answer about how to visualize segmentation result. My question is if I'm using the CrossEntropyLoss as the loss function, after training. The predicted classification (segmentation) result doesn't represent the class 0,1,2. It represents the unnormalized Log probability as told in cs231n standford class.
So if I want to visualize the segmentaion result, should I use the
- nn.LogSoftmax()
to get the classes and then plot them out?
or should I do a maximum function over the first dimension of my output.
For example, if the predicted output is size 3x1024x1024, how could I visualize the segmenation result?
Thank you in advance.
neural-network image-classification
asked 2 mins ago
xysongxysong
1
New contributor
xysong is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I know this question already has a clear answer about how to visualize segmentation result. My question is if I'm using the CrossEntropyLoss as the loss function, after training. The predicted classification (segmentation) result doesn't represent the class 0,1,2. It represents the unnormalized Log probability as told in cs231n standford class.
So if I want to visualize the segmentaion result, should I use the
- nn.LogSoftmax()
to get the classes and then plot them out?
or should I do a maximum function over the first dimension of my output.
For example, if the predicted output is size 3x1024x1024, how could I visualize the segmenation result?
Thank you in advance.
neural-network image-classification
neural-network image-classification
asked 2 mins ago
xysongxysong
1
New contributor
xysong is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 mins ago
xysongxysong
1
New contributor
xysong is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 mins ago
xysongxysong
1
New contributor
xysong is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 mins ago
xysongxysong
1
asked 2 mins ago
xysongxysong
1
1
New contributor
xysong is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
xysong is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
xysong 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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