icolbert/upsampling
Algorithmic solutions to optimize inference for convolution-based image upsampling. Coded for clarity, not speed.
This project helps optimize the process of increasing image resolution using deep learning. It takes in trained image upsampling models and converts their internal workings to perform the same task more efficiently. This is designed for engineers or researchers deploying image processing systems on devices with limited memory or power.
No commits in the last 6 months.
Use this if you need to run high-quality image upsampling models on resource-constrained devices like embedded systems or edge computing platforms.
Not ideal if your primary concern is the absolute fastest training time or if you have ample computational resources for inference.
Stars
10
Forks
2
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Aug 26, 2022
Commits (30d)
0
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