Comments (2)
@albertyou2 I've seen a lot of Ostagram.ru results and they are really impressive. They are using a newer method to acheive these results in a few seconds. This repository on the other hand uses the older method which has more fine grained control over the final result, but takes far longer.
If you are using the Script Helper, it is really easy to acheive Ostagram's result :
- Change content layer to conv3_3 (instead of default conv5_2)
- Style weight to 300
- Number of Iterations to 25 (50 or 100 if you want more precise results)
- Image size to 512
- If you want Ostagram's color preservation then check the "Preserve Color" checkbox.
That's it. This should result in similar results in comparison to Ostagram.
Here is a comparison using the bamboo art style :
As you can see, the ostagram results are very good in comparison to this repo, and it is generated much faster. However, upon closer inspection, you can see that this repo produces slightly more detail. Also, the color isn't as dark as in Ostragram's result. That means that this repository still hasn't finished its processing. Perhaps with 100 iterations it could get even better results, at the cost of execution time.
And here is a comparison using the patterned leaf style :
As you can see, the leaves are far more detailed in this repo after 100 iterations and content layer of conv4_2 instead of conv3_3
from neural-style-transfer.
@albertyou2
Thank you very much for reply!
I 'll follow your instructions !
Have a nice day!
from neural-style-transfer.
Related Issues (20)
- [Question] Content mask format HOT 5
- how to run the script on multiples GPU?
- ImportError: cannot import name 'imread' HOT 5
- Google colab neural style transfer HOT 2
- error in MRFNetwork.py HOT 1
- could we apply fast-style-transfer to image deformation? HOT 2
- Update documentation to reflect that tensorflow works on Windows now
- Replace tf.gradients with tf.GradientTape : RuntimeError HOT 2
- Bug using preserve color option HOT 3
- ValueError: Tensor conversion requested dtype float32 for Tensor with dtype int32: 'Tensor("strided_slice_8:0", shape=(), dtype=int32)' HOT 1
- script helper
- script helper
- Saving model
- RuntimeError: tf.gradients is not supported when eager execution is enabled. Use tf.GradientTape instead. HOT 5
- convert_all_kernels_in_model removed from tf2 HOT 1
- RuntimeError: tf.gradients is not supported when eager execution is enabled. Use tf.GradientTape instead. HOT 2
- neural doodle
- broken and unusable HOT 1
- Tensorflow 1 is unsupported in Colab
- Hello, I'm sorry to bother you. I need help
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from neural-style-transfer.