Instructions to use ibm-nasa-geospatial/Prithvi-EO-2.0-300M-BurnScars with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TerraTorch
How to use ibm-nasa-geospatial/Prithvi-EO-2.0-300M-BurnScars with TerraTorch:
from terratorch.registry import BACKBONE_REGISTRY model = BACKBONE_REGISTRY.build("ibm-nasa-geospatial/Prithvi-EO-2.0-300M-BurnScars") - Notebooks
- Google Colab
- Kaggle
update-checkpoint
#2
by morenoj11 - opened
Update Prithvi-v2-BurnScars Checkpoint to Support Fine-Tuning
This PR updates the Prithvi-v2-BurnScars checkpoint to include optimizer_states, which are required for continuing fine-tuning. The previous checkpoint from HuggingFace lacked this field because it was saved with weights_only=True.
- Regenerated compatible checkpoints using the same configuration
- Verified that fine-tuning now works on the custom data module
I removed the epoch, global_step, loops, callbacks, since these fields cause the training to resume from the best previous checkpoint (i.e. if you set max trainer.max_epochs=50, you might only get 10 epochs if the best previous checkpoint was achieved at epoch 40).
morenoj11 changed pull request status to open