-
Notifications
You must be signed in to change notification settings - Fork 836
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Colab end-to-end runnable version #56
Conversation
- Showing time left - Showing image progression - Simplified and remove unused variables
- Simplified cell usage - Added configuration section at top
Improvements to Colab notebook (Better code, but broken interpolation because of the python file.)
- Improved Collab notebook readme cell - Added configuration for `FRAME_INPUT_DIR` - Added configuration for `FRAME_OUTPUT_DIR` - Showing a bit more info about the GPU Card (some drivers might actually not run) - Fixed git clone directory path - Passing `start_frame` and `end_frame` as parameters to the python script (to allow for resuming runs) - Avoid outputs middle-steps
Added parameters, fixed interpolation estimation, cleaner code, updated readme
I downloaded I imagine that once merged, the repository URL inside the notebook should be updated to point to this repository again. The only issue I had was mere oversight on my part: not paying attention to partial frames, e.g. a 12.5 FPS video does not upconvert in an integer fashion to 60 FPS. 63 FPS is closer to ideal. |
Colab end-to-end runnable version
Hi!
Along with the help of @styler00dollar, we've created a Python Notebook that can be run in Google Colaboratory for free. We also integrated it with Google Drive and ffmpeg, which means that we can do quick end-to-end tests with input and output files that we carry on.
We're basing our tests from the
best
model and the DAIN_slowmotion network, but with this, making modifications it's very easy.We believe that this is a good addition to the original repository, both as a demonstration of how to use the existing code (in real world scenarios) and also as a way for experimenters to run it without the need of a local setup.
We welcome any feedback!