TensorFlow For Poets - Optimal image size
I am trying to use the Tensorflow For Poets Google CodeLab as a template for a image classification project.
I use tens (maybe hundreds) of thousands of images with varying (relatively high) resolutions for retraining, but they are taking up too much disk space (over 10 GB) and I would like to downscale them to save some space.
As far as I understand, image resolution is not much of a concern here and it should not be an issue to scale down all the images (from roughly 4000x3000 to something much smaller).
I tried using 224x224 resolution and everything worked fine, but then I noticed some existing SO questions mentioning that the input images are being scaled to 299x299 rather than 224x224.
This made me wonder: What is the optimal input image resolution when using the code from the said CodeLab to make sure the images take up as little space as possible without making any sacrifice to the performance of the retrained model?
Was I sabotaging the process by overly downscaling the images? I use the mobilenet_v1_0.50_224 model, which is why I thought using 224x224 images for retraining would be the best way to go.
Given all my images have a high enough resolution, would I benefit from modifying the scripts to accept a larger image size?
tensorflow
asked Dec 27 '18 at 20:08
natiiix
5819
add a comment |
I am trying to use the Tensorflow For Poets Google CodeLab as a template for a image classification project.
I use tens (maybe hundreds) of thousands of images with varying (relatively high) resolutions for retraining, but they are taking up too much disk space (over 10 GB) and I would like to downscale them to save some space.
As far as I understand, image resolution is not much of a concern here and it should not be an issue to scale down all the images (from roughly 4000x3000 to something much smaller).
I tried using 224x224 resolution and everything worked fine, but then I noticed some existing SO questions mentioning that the input images are being scaled to 299x299 rather than 224x224.
This made me wonder: What is the optimal input image resolution when using the code from the said CodeLab to make sure the images take up as little space as possible without making any sacrifice to the performance of the retrained model?
Was I sabotaging the process by overly downscaling the images? I use the mobilenet_v1_0.50_224 model, which is why I thought using 224x224 images for retraining would be the best way to go.
Given all my images have a high enough resolution, would I benefit from modifying the scripts to accept a larger image size?
tensorflow
asked Dec 27 '18 at 20:08
natiiix
5819
add a comment |
I am trying to use the Tensorflow For Poets Google CodeLab as a template for a image classification project.
I use tens (maybe hundreds) of thousands of images with varying (relatively high) resolutions for retraining, but they are taking up too much disk space (over 10 GB) and I would like to downscale them to save some space.
As far as I understand, image resolution is not much of a concern here and it should not be an issue to scale down all the images (from roughly 4000x3000 to something much smaller).
I tried using 224x224 resolution and everything worked fine, but then I noticed some existing SO questions mentioning that the input images are being scaled to 299x299 rather than 224x224.
This made me wonder: What is the optimal input image resolution when using the code from the said CodeLab to make sure the images take up as little space as possible without making any sacrifice to the performance of the retrained model?
Was I sabotaging the process by overly downscaling the images? I use the mobilenet_v1_0.50_224 model, which is why I thought using 224x224 images for retraining would be the best way to go.
Given all my images have a high enough resolution, would I benefit from modifying the scripts to accept a larger image size?
tensorflow
asked Dec 27 '18 at 20:08
natiiix
5819
I am trying to use the Tensorflow For Poets Google CodeLab as a template for a image classification project.
I use tens (maybe hundreds) of thousands of images with varying (relatively high) resolutions for retraining, but they are taking up too much disk space (over 10 GB) and I would like to downscale them to save some space.
As far as I understand, image resolution is not much of a concern here and it should not be an issue to scale down all the images (from roughly 4000x3000 to something much smaller).
I tried using 224x224 resolution and everything worked fine, but then I noticed some existing SO questions mentioning that the input images are being scaled to 299x299 rather than 224x224.
This made me wonder: What is the optimal input image resolution when using the code from the said CodeLab to make sure the images take up as little space as possible without making any sacrifice to the performance of the retrained model?
Was I sabotaging the process by overly downscaling the images? I use the mobilenet_v1_0.50_224 model, which is why I thought using 224x224 images for retraining would be the best way to go.
Given all my images have a high enough resolution, would I benefit from modifying the scripts to accept a larger image size?
tensorflow
tensorflow
asked Dec 27 '18 at 20:08
natiiix
5819
asked Dec 27 '18 at 20:08
natiiix
5819
asked Dec 27 '18 at 20:08
natiiix
5819
asked Dec 27 '18 at 20:08
natiiix
5819
asked Dec 27 '18 at 20:08
natiiix
5819
5819
add a comment |
add a comment |
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