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*******************
Experiment examples
*******************

The repository contains examples of how to use Malpolon for different scenarios.
The examples are organized by the type of user and the type of dataset used.

.. toctree::
   :maxdepth: 10


I. Custom train scenario
************************

I have a dataset of **pre-extracted image patches** or **raster files** *(e.g. bioclimatic data, satellite rasters...)* of my own and I want to train a **deep image neural network model** on it. I want to be able to easily customize the training process and the model architecture.

- *Drop and play* : I have a file (.csv) of Presence/Absence (PA) or Presence Only (PO) observations and I want to train a model on different environmental variables (rasters, satellite imagery) without having to worry about the data loading and on-the-fly extraction.

- *Custom dataset* : I have my own dataset consisting of pre-extracted image patches and/or rasters and I want to train a model on it.

Sentinel-2A
===========

`See Sentinel-2a (training) GitHub README 🔗 <https://github.com/plantnet/malpolon/tree/main/examples/ecologists/sentinel_2a>`_

MicroGeoLifeCLEF2022
====================

`See MicroGeoLifeCLEF2022 (training) GitHub README 🔗 <https://github.com/plantnet/malpolon/tree/main/examples/ecologists/micro_geolifeclef2022>`_


II. Inference scenario
**********************

I have an observations file (.csv) and I want to predict the presence of species on a given area using a model I trained previously and a selected dataset or a shapefile I would provide.

Sentinel-2A
===========

`See Sentinel-2a (inference) GitHub README 🔗 <https://github.com/plantnet/malpolon/tree/main/examples/inference/sentinel_2a>`_

MicroGeoLifeCLEF2022
====================

`See MicroGeoLifeCLEF2022 (inference) GitHub README 🔗 <https://github.com/plantnet/malpolon/tree/main/examples/inference/micro_geolifeclef2022>`_

GeoLifeClef2024_pre_extracted
=============================

`See GLC24_pre_extracted (inference) GitHub README 🔗 <https://github.com/plantnet/malpolon/tree/main/examples/inference/geolifeclef2024_pre_extracted>`_

III. Benchmarks scenario
************************

I want to compare the performance of different models on a given known dataset; or I am a potential kaggle participant on the GeoLifeClef challenge. I want to train a model on the provided datasets without having to worry about the data loading, starting from a plug-and-play example.

- *GeoLifeClef2022* : contains a fully functionnal example of a model training on the GeoLifeClef2022 dataset, from data download, to training and prediction. Comes with datasets and datamodule.

- *GeoLifeClef2023* : contains dataloaders for the GeoLifeClef2023 dataset (different from the GLC2022 dataloaders). The training and prediction scripts are not provided.

- *GeoLifeClef2024_pre_extracted*: contains a fully functionnal example of a model training on the GeoLifeClef2024 pre-extracted dataset, from data download, to training and prediction. Comes with datasets and datamodule for species prediction as well as habitats prediction using a direct approach (raster-based).

GeoLifeCLEF 2022
================

`See GLC22 (kaggle) GitHub README 🔗 <https://github.com/plantnet/malpolon/tree/main/examples/benchmarks/geolifeclef/geolifeclef2022>`_

GeoLifeCLEF 2023
================

`See GLC23 (kaggle) GitHub README 🔗 <https://github.com/plantnet/malpolon/tree/main/examples/benchmarks/geolifeclef/geolifeclef2023>`_

GeoLifeCLEF 2024 (pre-extracted)
================================

`See GLC24_pre_extracted (kaggle) GitHub README 🔗 <https://github.com/plantnet/malpolon/tree/main/examples/benchmarks/geolifeclef/geolifeclef2024_pre_extracted>`_