PHOTONAI is a high level Python API for designing and optimizing Machine Learning Pipelines.


  1. EASY ACCESS TO ML IMPLEMENTATIONS: We pre-registered diverse preprocessing and learning algorithms from state-of-the-art toolboxes e.g. Scikit-learn, Keras and Imbalanced learn, which you can choose to rapidly build custom pipelines.

  2. HYPERPARAMETER OPTIMIZATION: With PHOTONAI you can seamlessly switch between diverse hyperparameter optimization strategies, such as (random) grid-search or Bayesian Optimization (scikit-optimize, smac3).

  3. MODEL SHARING PHOTONAI: provides a standardized format for sharing and loading optimized models and pipelines across platforms with only one line of code.

  4. EXTENDED ML PIPELINE: You can build custom sequences of processing and learning algorithms with a simple syntax. PHOTONAI offers extended pipeline functionality such as parallel sequences, custom callbacks in-between pipeline elements, AND- and OR-Operations, as well as the possibility to flexibly position data augmentation, class balancing or learning algorithms anywhere in the pipeline.

  5. AUTOMATION: While you concentrate on selecting appropriate processing steps, learning algorithms, hyperparameters and training parameters, PHOTONAI automates the nested cross-validated optimization and evaluation loop for any custom pipeline.

  6. RESULT LOGGING PHOTONAI: comes with extensive logging of all information in the training, testing and hyperparameter optimization process. In addition, optimum performances and the hyperparameter optimization progress are visualized in the PHOTONAI Explorer.

Example Video:

Photon can create fantastic Photo Style AI Images. It is also perfect for rendering your LoRA's with this Model. This Video shows some tricks and settings to get the highest quality results. This video also shows you how to get really nice upscales, even for older GPUs

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