RIFE 4.25 Fine Tunning
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- Dominant language
- Python
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Description
I am fine tunning RIFE on MODD dataset (water surface movement videos) in order for rife to be better in interpolating videos that contain waves in sea.
I need to know:
a) the fps of the dataset it was trained on, number of images in each dataset and number of epochs it was trained for?
b)when fine tunning which batch size should i choose?
c)i will use the model for sea view 4x interpolation so is it worthy to update the model in each training loop for t=0.25, 0.5 and 0.75 with their gt images, or keep training on a random t selected? if so then what should be the minimum fps of the training dataset to do so? and how much frames per each video sequence?
d)for the learning rate scheduler, can you conclude and equation to adjust its parameters based on the number of images in the dataset and epochs the model will be trained on?
Thanks in advance.
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First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
No files, tests, or entry points are named. Start by locating the repository's RIFE training and fine-tuning entry points, then review how dataset frame rates, batch size, interpolation times, and learning-rate scheduling are configured. Done means the MODD-specific training questions have documented, maintainer-confirmed answers.
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Assessment
- Tech stack
- python
- Domain
- computer-vision, machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100