Create README.md

README.md

@@ -0,0 +1,136 @@
+---
+language:
+  - en
+tags:
+  - ecology
+  - multimodal
+  - missing-modality
+  - masking
+  - species-distribution-modeling
+  - image-classification
+library_name: pytorch
+pipeline_tag: image-classification
+---
+
+# MIAM: Modality Imbalance-Aware Masking for Multimodal Ecological Applications
+
+This repository hosts released checkpoints for **MIAM** from the ICLR 2026 paper:
+
+- Paper: https://openreview.net/forum?id=oljjAkgZN4
+- Project page: https://zbirobin.github.io/publications/miam/
+- Source code: https://github.com/zbirobin/MIAM
+
+MIAM is a dynamic masking strategy for multimodal ecological learning. During training, it adapts masking probabilities using modality-specific performance and learning-speed signals to reduce modality imbalance and improve robustness to missing inputs.
+
+## Model details
+
+- **Model family**: Multimodal ecological models trained with MIAM and masking baselines
+- **Modalities covered**:
+  - GeoPlant (MaskSDM): satellite + environmental tabular + climate time series
+  - TaxaBench-8k: location + environmental tabular + natural image + audio + satellite
+  - SatBird: satellite + environmental tabular
+- **Framework**: PyTorch
+
+## Available files
+
+### GeoPlant (MaskSDM)
+
+- `geoplant_miam.pt`
+- `geoplant_opm.pt`
+- `geoplant_dropout.pt`
+- `geoplant_constant.pt`
+- `geoplant_dirichlet.pt`
+- `geoplant_uniform.pt`
+- `geoplant_pretraining_miam.pt`
+- `geoplant_pretraining_opm.pt`
+- `geoplant_pretraining_dropout.pt`
+- `geoplant_pretraining_constant.pt`
+- `geoplant_pretraining_dirichlet.pt`
+- `geoplant_pretraining_uniform.pt`
+
+### TaxaBench-8k
+
+- `taxabench_miam.pt`
+- `taxabench_dirichlet.pt`
+- `taxabench_dropout.pt`
+- `taxabench_opm.pt`
+- `taxabench_uniform.pt`
+- `taxabench_embeds_loc_env_img_aud_sat.pt`
+- `taxabench_num_species_10.csv`
+
+### SatBird
+
+- `satbird_miam.pth`
+- `satbird_opm.pth`
+- `satbird_dropout.pth`
+- `satbird_dirichlet.pth`
+
+## Quick start
+
+### Download with `hf` CLI
+
+```bash
+hf download zbirobin/MIAM geoplant_miam.pt
+hf download zbirobin/MIAM taxabench_miam.pt
+hf download zbirobin/MIAM satbird_miam.pth
+```
+
+### Download in Python
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+
+ckpt_path = hf_hub_download(repo_id="zbirobin/MIAM", filename="geoplant_miam.pt")
+state = torch.load(ckpt_path, map_location="cpu")
+```
+
+## Intended use
+
+- Research on multimodal ecological modeling
+- Robustness to missing-modality settings
+- Benchmark comparison of masking strategies
+- Modality contribution analysis and interpretability studies
+
+## Out-of-scope use
+
+- Safety-critical ecological decisions without domain validation
+- Deployment outside the data distributions represented in GeoPlant, TaxaBench-8k, and SatBird
+- Applications requiring calibrated uncertainty without additional validation
+
+## Training and evaluation data
+
+- **GeoPlant**: species distribution modeling benchmark used in the paper
+- **TaxaBench-8k**: multimodal species classification benchmark
+- **SatBird-USA-summer**: bird species distribution benchmark
+
+Please follow dataset licenses, terms of use, and any access restrictions from the original providers.
+
+## Limitations
+
+- Transferability across regions/taxa/modalities may be limited
+
+## Reproducibility
+
+Use the benchmark READMEs in the source repository for exact folder structure, commands, and evaluation scripts:
+
+- `maskSDM/README.md`
+- `taxabench/README.md`
+- `satbird/README.md`
+
+## Citation
+
+```bibtex
+@inproceedings{
+  zbinden2026miam,
+  title={{MIAM}: Modality Imbalance-Aware Masking for Multimodal Ecological Applications},
+  author={Robin Zbinden and Wesley Monteith-Finas and Gencer Sumbul and Nina van Tiel and Chiara Vanalli and Devis Tuia},
+  booktitle={International Conference on Learning Representations (ICLR)},
+  year={2026},
+  url={https://openreview.net/forum?id=oljjAkgZN4}
+}
+```
+
+## Contact
+
+For issues and questions, please open a ticket in the source repository.