There are 2 repositories under fitting topic.
End-to-end, automatic face swapping pipeline
This is a implementation of the 3D FLAME model in PyTorch
Apache Commons Math
A 3DMM fitting framework using Pytorch.
Jupyter-friendly Python interface for C++ MINUIT2
Python code for Stellar Population Inference from Spectra and SEDs
Basic (mathematical) operations for B-spline functions and related things with julia
A library for discrete-time Markov chains analysis.
Curve fitting method in JavaScript
A Software to interactively edit data in a graphical manner
A collection of general Fortran modules in the categories Computational, Date and Time, Input / Output, Math / Numerics, Screening, Sensitivity Analysis and Optimising / Fitting, and Miscellaneous.
MATLAB GUI for Magic Formula Tyre Modeling
Computer Vision implementation
MATLAB library for magic formula tyre modeling
Data analysis of the COVID-19 outbreak in Italy updated daily from official sources
RepTate (Rheology of Entangled Polymers: Toolkit for Analysis of Theory & Experiment)
Bayesian Extinction And Stellar Tool
Visual inspection of bridges is customarily used to identify and evaluate faults. However, current procedures followed by human inspectors demand long inspection times to examine large and difficult to access bridges. To address these limitations, we investigate a computer vision‐based approach that employs SIFT keypoint matching on collected images of defects against a pre-existing reconstructed 3D point cloud of the bridge. We also investigate methods of reducing computation time with ML-based and conventional CV methods of segmentation to eliminate redundant keypoints. Our project successfully localizes the defect images and achieves a savings in runtime from filtering keypoints.
📊📈🔬 SpectraFit is a command-line and Jupyter-notebook tool for quick data-fitting based on the regular expression of distribution functions.
Fit a partial point cloud with a superquadric
An open source Julia library for active learning of interatomic potentials in atomistic simulations of materials. It incorporates elements of bayesian inference, machine learning, differentiable programming, software composability, and high-performance computing.
Polyscope application demonstrating the Ponca library