ReproModel Website

ReproModel: Open Source Toolbox for Boosting the AI Research Efficiency
ReproModel Toolbox revolutionizes research efficiency by providing standardized models, dataloaders, and processing procedures. It features a comprehensive suite of pre-existing experiments, a code extractor, and an LLM descriptor. This toolbox allows researchers to focus on new datasets and model development, significantly reducing time and computational costs.
Check it out
Current Research Workflow
Implementing Benchmark and State-of-the-Art Models
Developing and comparing benchmark and state-of-the-art models for evaluation.
Implementing Known and New Data Loaders
Redevelopment needed due to different data splits, input types, specific input logic, etc.
Implementing Procedures for Pre- and Post-Processing
Establishing uniform protocols for data pre- and post-processing.
Rerunning All Models on All Datasets
Retraining all models across all datasets and folds for comprehensive evaluation.
Ready to have all of the above implemented and available in one place?
ReproModel Workflow
All Conventional Steps - Automated
Unlock the potential of our automation technology. With our no-code, click-and-select solution,  you'll have access to a collection of benchmark and SOTA models and datasets. 

Dive into training visualizations, effortlessly extract code for publication,  and let our LLM-powered writer do the heavy lifting on writing your methodology description (supports LaTeX).
The Highlights
Config File Generator
Every Experiment has a defined config file, making the results reproducible with any machine, from anywhere, without any effort.
AI Experiment Description Generator
Save time writing your paper with LLM generated description of the specific experiment conducted.
Code Extractor
Extract all the code, and only the code of your experiment specific code as a zip file or directly 
to your GitHub repository.
Custom Script Editor
Make your own custom models, datasets, 
preprocessors, augmentations, loss functions, etc. 
based on the ReproModel templates.
… and all the basics as well
Standard Models Included                                   ✅ 
Known Datasets                                                         ✅ 
Metrics (100+)                                                              ✅ 
Losses (20+)                                                                  ✅ 
Data Splitting                                                               ✅ 
Augmentations                                                           ✅ 
Optimizers (10+)                                                        ✅
Learning Rate Schedulers                                     ✅
Early Stopping Criterion                                         ✅
Training Device Selection                                      ✅
Logging (TensorBoard, W&B, ...)                         ✅
AI Experiment Description Generator              ✅
Code Extractor                                                              ✅
Custom Script Editor                                                 ✅
 Docker Image                                                                ✅
What we are working on next

Parallel runs of multiple small trainings        ⏳
Research field specific standards                      ⏳
Data & Result Visualization                                  ⏳
🏦 Cloud Training                                                     ⏳
🏦 Web-monitoring of training runs               ⏳
… and many more!
Get Started Today
Subscribe to the waiting list. Not anymore!

Try it out on Github (there is also a live preview)!
GitHub