Cuvis.Ai: An Open-Source, Low-Code Software Ecosystem for Hyperspectral Processing and Classification

• URL: http://arxiv.org/abs/2411.11324v1
• Date: Mon, 18 Nov 2024 06:33:40 GMT
• Title: Cuvis.Ai: An Open-Source, Low-Code Software Ecosystem for Hyperspectral Processing and Classification
• Authors: Nathaniel Hanson, Philip Manke, Simon Birkholz, Maximilian Mühlbauer, Rene Heine, Arnd Brandes,
• Abstract summary: cuvis.ai is an open-source and low-code software ecosystem for data acquisition, preprocessing, and model training. The package is written in Python and provides wrappers around common machine learning libraries.
• Score: 0.4038539043067986
• License:
• Abstract: Machine learning is an important tool for analyzing high-dimension hyperspectral data; however, existing software solutions are either closed-source or inextensible research products. In this paper, we present cuvis.ai, an open-source and low-code software ecosystem for data acquisition, preprocessing, and model training. The package is written in Python and provides wrappers around common machine learning libraries, allowing both classical and deep learning models to be trained on hyperspectral data. The codebase abstracts processing interconnections and data dependencies between operations to minimize code complexity for users. This software package instantiates nodes in a directed acyclic graph to handle all stages of a machine learning ecosystem, from data acquisition, including live or static data sources, to final class assignment or property prediction. User-created models contain convenient serialization methods to ensure portability and increase sharing within the research community. All code and data are available online: https://github.com/cubert-hyperspectral/cuvis.ai

Related papers

• Deep Fast Machine Learning Utils: A Python Library for Streamlined Machine Learning Prototyping [0.0]
  The Deep Fast Machine Learning Utils (DFMLU) library provides tools designed to automate and enhance aspects of machine learning processes. DFMLU offers functionalities that support model development and data handling. This manuscript presents an overview of DFMLU's functionalities, providing Python examples for each tool.
  arXiv  Detail & Related papers  (2024-09-14T21:39:17Z)
• Relational Deep Learning: Graph Representation Learning on Relational Databases [69.7008152388055]
  We introduce an end-to-end representation approach to learn on data laid out across multiple tables. Message Passing Graph Neural Networks can then automatically learn across the graph to extract representations that leverage all data input.
  arXiv  Detail & Related papers  (2023-12-07T18:51:41Z)
• PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series [0.0]
  PyPOTS is an open-source Python library dedicated to data mining and analysis on partially-observed time series. It provides easy access to diverse algorithms categorized into four tasks: imputation, classification, clustering, and forecasting.
  arXiv  Detail & Related papers  (2023-05-30T07:57:05Z)
• SequeL: A Continual Learning Library in PyTorch and JAX [50.33956216274694]
  SequeL is a library for Continual Learning that supports both PyTorch and JAX frameworks. It provides a unified interface for a wide range of Continual Learning algorithms, including regularization-based approaches, replay-based approaches, and hybrid approaches. We release SequeL as an open-source library, enabling researchers and developers to easily experiment and extend the library for their own purposes.
  arXiv  Detail & Related papers  (2023-04-21T10:00:22Z)
• Scalable Collaborative Learning via Representation Sharing [53.047460465980144]
  Federated learning (FL) and Split Learning (SL) are two frameworks that enable collaborative learning while keeping the data private (on device) In FL, each data holder trains a model locally and releases it to a central server for aggregation. In SL, the clients must release individual cut-layer activations (smashed data) to the server and wait for its response (during both inference and back propagation). In this work, we present a novel approach for privacy-preserving machine learning, where the clients collaborate via online knowledge distillation using a contrastive loss.
  arXiv  Detail & Related papers  (2022-11-20T10:49:22Z)
• A Library for Representing Python Programs as Graphs for Machine Learning [39.483608364770824]
  We introduce an open source Python library python_graphs that applies static analysis to construct graph representations of Python programs. We present the capabilities and limitations of the library, perform a case study applying the library to millions of competitive programming submissions, and showcase the library's utility for machine learning research.
  arXiv  Detail & Related papers  (2022-08-15T22:36:17Z)
• Few-Shot Non-Parametric Learning with Deep Latent Variable Model [50.746273235463754]
  We propose Non-Parametric learning by Compression with Latent Variables (NPC-LV) NPC-LV is a learning framework for any dataset with abundant unlabeled data but very few labeled ones. We show that NPC-LV outperforms supervised methods on all three datasets on image classification in low data regime.
  arXiv  Detail & Related papers  (2022-06-23T09:35:03Z)
• Kubric: A scalable dataset generator [73.78485189435729]
  Kubric is a Python framework that interfaces with PyBullet and Blender to generate photo-realistic scenes, with rich annotations, and seamlessly scales to large jobs distributed over thousands of machines. We demonstrate the effectiveness of Kubric by presenting a series of 13 different generated datasets for tasks ranging from studying 3D NeRF models to optical flow estimation.
  arXiv  Detail & Related papers  (2022-03-07T18:13:59Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• Benchpress: A Scalable and Versatile Workflow for Benchmarking Structure Learning Algorithms [1.7188280334580197]
  Probabilistic graphical models are one common approach to modelling the data generating mechanism. We present a novel Snakemake workflow called Benchpress for producing scalable, reproducible, and platform-independent benchmarks. We demonstrate the applicability of this workflow for learning Bayesian networks in five typical data scenarios.
  arXiv  Detail & Related papers  (2021-07-08T14:19:28Z)
• giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration [4.8353738137338755]
  giotto-tda is a Python library that integrates high-performance topological data analysis with machine learning. The library's ability to handle various types of data is rooted in a wide range of preprocessing techniques.
  arXiv  Detail & Related papers  (2020-04-06T10:53:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.