Instructions to use GNSFoundation/Prithvi-EO-2.0-300M-TL-Sen1Floods11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TerraTorch
How to use GNSFoundation/Prithvi-EO-2.0-300M-TL-Sen1Floods11 with TerraTorch:
from terratorch.registry import BACKBONE_REGISTRY model = BACKBONE_REGISTRY.build("GNSFoundation/Prithvi-EO-2.0-300M-TL-Sen1Floods11") - Notebooks
- Google Colab
- Kaggle
| # lightning.pytorch==2.4.0 | |
| seed_everything: 0 | |
| trainer: | |
| accelerator: auto | |
| strategy: auto | |
| devices: auto | |
| num_nodes: 1 | |
| precision: 16-mixed | |
| logger: true | |
| callbacks: | |
| - class_path: lightning.pytorch.callbacks.RichProgressBar | |
| init_args: | |
| refresh_rate: 1 | |
| leave: false | |
| theme: | |
| description: white | |
| progress_bar: '#6206E0' | |
| progress_bar_finished: '#6206E0' | |
| progress_bar_pulse: '#6206E0' | |
| batch_progress: white | |
| time: grey54 | |
| processing_speed: grey70 | |
| metrics: white | |
| metrics_text_delimiter: ' ' | |
| metrics_format: .3f | |
| - class_path: lightning.pytorch.callbacks.LearningRateMonitor | |
| init_args: | |
| logging_interval: epoch | |
| log_momentum: false | |
| log_weight_decay: false | |
| - class_path: lightning.pytorch.callbacks.EarlyStopping | |
| init_args: | |
| monitor: val/loss | |
| min_delta: 0.0 | |
| patience: 20 | |
| verbose: false | |
| mode: min | |
| strict: true | |
| check_finite: true | |
| log_rank_zero_only: false | |
| fast_dev_run: false | |
| max_epochs: 50 | |
| max_steps: -1 | |
| overfit_batches: 0.0 | |
| check_val_every_n_epoch: 2 | |
| log_every_n_steps: 10 | |
| enable_checkpointing: true | |
| accumulate_grad_batches: 1 | |
| inference_mode: true | |
| use_distributed_sampler: true | |
| detect_anomaly: false | |
| barebones: false | |
| sync_batchnorm: false | |
| reload_dataloaders_every_n_epochs: 0 | |
| default_root_dir: /dccstor/geofm-finetuning/benchmark-geo-bench-paolo/ | |
| model: | |
| class_path: terratorch.tasks.SemanticSegmentationTask | |
| init_args: | |
| model_args: | |
| backbone_pretrained: true | |
| backbone: prithvi_eo_v2_300_tl | |
| decoder: UperNetDecoder | |
| decoder_channels: 256 | |
| decoder_scale_modules: true | |
| num_classes: 2 | |
| rescale: true | |
| backbone_bands: | |
| - BLUE | |
| - GREEN | |
| - RED | |
| - NIR_NARROW | |
| - SWIR_1 | |
| - SWIR_2 | |
| head_dropout: 0.1 | |
| necks: | |
| - name: SelectIndices | |
| indices: | |
| - 5 | |
| - 11 | |
| - 17 | |
| - 23 | |
| - name: ReshapeTokensToImage | |
| model_factory: EncoderDecoderFactory | |
| loss: ce | |
| ignore_index: -1 | |
| lr: 0.001 | |
| freeze_backbone: false | |
| freeze_decoder: false | |
| plot_on_val: 10 | |
| data: | |
| class_path: terratorch.datamodules.Sen1Floods11NonGeoDataModule | |
| init_args: | |
| data_root: /dccstor/geofm-finetuning/datasets/sen1floods11 | |
| batch_size: 16 | |
| num_workers: 8 | |
| bands: | |
| - BLUE | |
| - GREEN | |
| - RED | |
| - NIR_NARROW | |
| - SWIR_1 | |
| - SWIR_2 | |
| train_transform: | |
| - class_path: albumentations.RandomCrop | |
| init_args: | |
| height: 224 | |
| width: 224 | |
| p: 1.0 | |
| - class_path: albumentations.HorizontalFlip | |
| init_args: | |
| p: 0.5 | |
| - class_path: albumentations.VerticalFlip | |
| init_args: | |
| p: 0.5 | |
| - class_path: albumentations.pytorch.ToTensorV2 | |
| init_args: | |
| transpose_mask: false | |
| p: 1.0 | |
| val_transform: | |
| - class_path: albumentations.pytorch.ToTensorV2 | |
| init_args: | |
| transpose_mask: false | |
| p: 1.0 | |
| test_transform: | |
| - class_path: albumentations.pytorch.ToTensorV2 | |
| init_args: | |
| transpose_mask: false | |
| p: 1.0 | |
| drop_last: true | |
| constant_scale: 0.0001 | |
| no_data_replace: 0.0 | |
| no_label_replace: -1 | |
| use_metadata: false | |
| out_dtype: int16 | |
| deploy_config_file: true | |
| optimizer: | |
| class_path: torch.optim.AdamW | |
| init_args: | |
| lr: 5.0e-05 | |
| betas: | |
| - 0.9 | |
| - 0.999 | |
| eps: 1.0e-08 | |
| weight_decay: 0.05 | |
| amsgrad: false | |
| maximize: false | |
| capturable: false | |
| differentiable: false | |
| lr_scheduler: | |
| class_path: torch.optim.lr_scheduler.CosineAnnealingLR | |
| init_args: | |
| T_max: 50 | |
| eta_min: 0 | |
| last_epoch: -1 | |