Comments (3)
abhiskk
commented on August 12, 2024
1
@glesperance you can get good results with around 10K images. I would also suggest to monitor the loss to get good quality results.
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abhiskk
commented on August 12, 2024
It should be able train quite quickly on 5000 images, if you want to see if it starts training or not modify the --log-interval [code] parameter to 1 and see if output messages are printed. Also run the training command using the unbuffered parameter position: python -u train.py..., you can test training with a batch size of 1 on your local laptop/desktop to check if everything is working correctly before running on AWS.
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glesperance
commented on August 12, 2024
@abhiskk How many images should we use when training a new model?
Is it worth it to go over 10K, 20K, 80K images?
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Related Issues (20)
- Check usage of 'subtract_imagenet_mean', with new version of pytorch inplace subtraction might be different. HOT 1
- About adding Tanh() at the end of the model HOT 5
- How to set output_image_size when Eval HOT 18
- Runtime error while trying out the toy example HOT 2
- trained model not working HOT 1
- difference with jcjohnson HOT 1
- Problem with training
- download_styling_models.sh error HOT 1
- some question in utils.py HOT 1
- content loss calculation is wrong? HOT 1
- train doesn't run: ValueError: not enough values to unpack (expected 3, got 2) HOT 5
- Query Regarding Transforms used for input image and style image HOT 5
- download_styling_models.sh error
- Could you upload vgg16.weight file? HOT 1
- Python error: <stdin> is a directory, cannot continue HOT 1
- RuntimeError: The expanded size of the tensor (700) must match the existing size (32) at non-singleton dimension 2 HOT 1
- Use Reflection Padding
- Use median filter
- stylize using GPU HOT 19
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