Orange3 Image Classification
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Is the attached workflow correct in case of training and testing with different data sets? Is the attached workflow considered "transfer learning" since my images are not related to the images that the network was pre-trained?
image-classification orange orange3 orange3-imageanalytics
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Is the attached workflow correct in case of training and testing with different data sets? Is the attached workflow considered "transfer learning" since my images are not related to the images that the network was pre-trained?
image-classification orange orange3 orange3-imageanalytics
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bumped to the homepage by Community♦ yesterday
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 |
$begingroup$
Is the attached workflow correct in case of training and testing with different data sets? Is the attached workflow considered "transfer learning" since my images are not related to the images that the network was pre-trained?
image-classification orange orange3 orange3-imageanalytics
$endgroup$
Is the attached workflow correct in case of training and testing with different data sets? Is the attached workflow considered "transfer learning" since my images are not related to the images that the network was pre-trained?
image-classification orange orange3 orange3-imageanalytics
image-classification orange orange3 orange3-imageanalytics
edited Oct 8 '18 at 12:16
Stephen Rauch
1,52551330
1,52551330
asked Sep 16 '18 at 8:23
Nickolas PapanikolaouNickolas Papanikolaou
12
12
bumped to the homepage by Community♦ yesterday
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bumped to the homepage by Community♦ yesterday
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Your workflow is correct and you are doing a type of transfer learning where only the last layer is fine-tuned. Image embedding widget calculates features that are the activations of a penultimate layer of Inception-v3, VGG or Painters network. All of those networks are trained on the different set of images. VGG and Inception-v3 are trained on ImageNet dataset and Painters network is trained on the set of artworks. In the transfer learning you are implementing with the workflow you fine-tune all of the connected classifiers for your images.
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1 Answer
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$begingroup$
Your workflow is correct and you are doing a type of transfer learning where only the last layer is fine-tuned. Image embedding widget calculates features that are the activations of a penultimate layer of Inception-v3, VGG or Painters network. All of those networks are trained on the different set of images. VGG and Inception-v3 are trained on ImageNet dataset and Painters network is trained on the set of artworks. In the transfer learning you are implementing with the workflow you fine-tune all of the connected classifiers for your images.
$endgroup$
add a comment |
$begingroup$
Your workflow is correct and you are doing a type of transfer learning where only the last layer is fine-tuned. Image embedding widget calculates features that are the activations of a penultimate layer of Inception-v3, VGG or Painters network. All of those networks are trained on the different set of images. VGG and Inception-v3 are trained on ImageNet dataset and Painters network is trained on the set of artworks. In the transfer learning you are implementing with the workflow you fine-tune all of the connected classifiers for your images.
$endgroup$
add a comment |
$begingroup$
Your workflow is correct and you are doing a type of transfer learning where only the last layer is fine-tuned. Image embedding widget calculates features that are the activations of a penultimate layer of Inception-v3, VGG or Painters network. All of those networks are trained on the different set of images. VGG and Inception-v3 are trained on ImageNet dataset and Painters network is trained on the set of artworks. In the transfer learning you are implementing with the workflow you fine-tune all of the connected classifiers for your images.
$endgroup$
Your workflow is correct and you are doing a type of transfer learning where only the last layer is fine-tuned. Image embedding widget calculates features that are the activations of a penultimate layer of Inception-v3, VGG or Painters network. All of those networks are trained on the different set of images. VGG and Inception-v3 are trained on ImageNet dataset and Painters network is trained on the set of artworks. In the transfer learning you are implementing with the workflow you fine-tune all of the connected classifiers for your images.
answered Oct 8 '18 at 11:06
PrimozPrimoz
16818
16818
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