Comments (4)
Your suggestion makes sense, if you try it out please share your results.
from deepsim.
Sure, I will keep you updated with my results.
from deepsim.
The main factors are the GPU you are using and the resolution of the training image, in addition, depending on the primitive used for training and the image itself, one may change the number of epochs, 16k is what we found works very well for any image we have tried, that being said, many images did just as well with 8k epochs.
Based on our setup it takes around 2 hours for a 256 image and up to 7 hours for a 1024 image.
Also, note that pix2pixHD is a relatively large architecture. For faster training, it is possible to use a smaller architecture (we’ve had some success with other smaller architectures although the setting was a bit different than this code).
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Thanks for you quick respond. I find your idea really interesting. I guess the only drawback is the training time you have to spend for one image before inference. Do you think it is possible to pre-train the network with a bunch of similar images in advance, and we only need to finetune the network on the new image?
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Related Issues (13)
- Batch Size? HOT 1
- Are there similarity between the DeepSIM method and Image Registration method? HOT 1
- How to train model if label input is "rgb"?
- How many Augmented images are required to generate good result?
- Implementation
- Infinite Loop Error (keeps starting train.py for some reason) HOT 2
- OOM due to unactive loadsize/finesize settings HOT 4
- Train on dataset with several samples HOT 2
- unrecognized arguments: --online_tps 0 HOT 1
- Pretrained models HOT 3
- Guys, are you planning a google colab laptop for this? Very necessary HOT 6
- Retain synthesized output structure from different labels.
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