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Holod Digital Holographic Reconstruction & Autofocus Toolkit Overview Holod is a Python package for automated focus estimation and amplitude/phase reconstruction of digital holograms. It provides: Dataset preparation: easily build and validate metadata CSV for hologram datasets. Autofocus training: classification or regression models (EfficientNet, ResNet, ViT) to predict depth bins or continuous depth. Evaluation & plotting: residual vs true depth, confusion matrices, density plots, violin plots, and hexbin visualizations. Reconstruction: Fresnel‐based reconstruction of amplitude and phase. Command‐line interface (CLI): easy-to-use commands to train, plot results, reconstruct holograms, and generate metadata. Demonstration of running holo train × Demonstration of running holo train Features Flexible analysis: choose between classification (--bins > 1) or regression (--bins 1). Backbones supported: efficientnet, resnet50, vit. Configurable training: batch size, epochs, learning rate, validation split, device (CPU/GPU). Visualizations: Plotly and Rich functions for analysis. To be Implemented Features Setup an addition to the cli, the option to display real time metrics of a plot being updated in real time, via plotly/nicegui. Option to expand the base of information by artificially creating focus/defocus. Optimization using JIT methods for PyTorch To see a more in depth explanation of the features: Methods page. Installation Downloads ...

Repository Executive Summary When performing Raman spectroscopy the output data is of such complexity, that performing SVD analysis is necessary to reduce the dimensionality of the intensity mapping to get meaningful result. To process this data the boundary from sample and background must be established. My project aims to replicate this detection of the boundary by learning to predict the resulting map from the pre-processing map. The data, in it’s raw form I say is a Raman hyper spectral intensity (HSI) cube. This is because the map can be constructed into a form of a cube, consisting of Raman shift intensities, and the “hyper” refers then to the higher dimensionality of the cube, as it contains more dimensions than just the spatial dimensionality. ...