Lama | Awesome Repository

Lama is an image restoration framework and deep learning model designed for image inpainting and object removal. It provides the tools necessary to train and evaluate neural networks that fill masked areas and repair corrupted visual data.

The system utilizes a Fourier convolution neural network to maintain global image structure and reconstruct periodic patterns. This architecture allows for resolution-independent inference, enabling the processing of high-resolution images without increasing memory or computational requirements.

The project includes a synthetic dataset generator that creates randomized training masks over raw images. It also features quantitative evaluation tools that compare predicted restoration results against ground truth data to calculate accuracy metrics.

Features

Image Inpainting - Provides a deep learning framework specifically designed for filling missing regions in static images.
Fourier Convolutional Networks - Implements a Fourier convolution neural network to maintain global image structure and periodic patterns.
Custom Model Training - Provides a framework for developing and optimizing image restoration models using custom training masks.
Deep Learning Training Pipelines - Implements an end-to-end workflow for training neural networks to map masked images to restored versions.

Features

Image Inpainting - Provides a deep learning framework specifically designed for filling missing regions in static images.
Fourier Convolutional Networks - Implements a Fourier convolution neural network to maintain global image structure and periodic patterns.
Custom Model Training - Provides a framework for developing and optimizing image restoration models using custom training masks.
Deep Learning Training Pipelines - Implements an end-to-end workflow for training neural networks to map masked images to restored versions.