1 repo
Frameworks for optimizing model weights using ground truth datasets.
Distinguishing note: Focuses on the training methodology rather than inference.
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This project is a command-line tool designed for image super-resolution and noise reduction, with a primary focus on anime-style illustrations. It utilizes convolutional neural network inference to reconstruct missing pixel data and remove digital artifacts, allowing users to upscale images and reduce noise either independently or in a single simultaneous processing pass. Beyond its core image restoration capabilities, the software provides a comprehensive suite for machine learning model training. Users can prepare custom datasets and optimize neural networks for specific restoration tasks,
Optimizes network weights by comparing generated outputs against ground truth image pairs.