This dataset encapsulates the Checkpoints obtained during the training process of the Deep Learning model, which can be used for new estimations. The aim of the DeepWealth package is to provide a generalizable Deep Learning framework for the use of remote sensing in poverty estimation. The combination of Deep Learning and Earth Observation data is increasingly being used to estimate socioeconomic conditions at regional and global scales. The proposed framework aligns with the Sustainable Development Goal SDG1 of ending poverty. The framework provides open-source data, code, and training models (checkpoints) for reproducibility and replicability. The source code can be found in https://github.com/PARSECworld/DeepWealth The metadata from source code can be found in https://github.com/PARSECworld/DeepWealth/blob/main/metadata.pdf The paper describing the development of this framework can be found at: Ben Abbes, A., Machicao, J., Corrêa, P. L. P., Specht, A., Devillers, R., Ometto, J. P., Kondo, Y., & Mouillot, D. (2024). DeepWealth: A generalizable open-source deep learning framework using satellite images for well-being estimation. SoftwareX, 27, 101785. https://doi.org/10.1016/j.softx.2024.101785