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This repository hosts released checkpoints for MIAM from the ICLR 2026 paper:
Model source
Source description
This repository hosts released checkpoints for MIAM from the ICLR 2026 paper:
Sources
1 sourceVerified Aug 5
Model artifacts
22 artifactsSource excerpts
2 excerptsMIAM 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.
geoplant_miam.ptgeoplant_opm.ptgeoplant_dropout.ptgeoplant_constant.ptgeoplant_dirichlet.ptgeoplant_uniform.ptgeoplant_pretraining_miam.ptgeoplant_pretraining_opm.ptgeoplant_pretraining_dropout.ptgeoplant_pretraining_constant.ptgeoplant_pretraining_dirichlet.ptgeoplant_pretraining_uniform.pttaxabench_miam.pttaxabench_dirichlet.pttaxabench_dropout.pttaxabench_opm.pttaxabench_uniform.pttaxabench_embeds_loc_env_img_aud_sat.pttaxabench_num_species_10.csvsatbird_miam.pthsatbird_opm.pthsatbird_dropout.pthsatbird_dirichlet.pthhf CLIhf download zbirobin/MIAM geoplant_miam.pt
hf download zbirobin/MIAM taxabench_miam.pt
hf download zbirobin/MIAM satbird_miam.pth
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")
Please follow dataset licenses, terms of use, and any access restrictions from the original providers.
Use the benchmark READMEs in the source repository for exact folder structure, commands, and evaluation scripts:
maskSDM/README.mdtaxabench/README.mdsatbird/README.mdThe model weights and associated files are licensed under the MIT License.
@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}
}
For issues and questions, please open a ticket in the source repository.
geoplant_dropout.pt
pt · 10.0 MB · SHA-256 1b789c00499b…fb63 · Hugging Face
taxabench_embeds_loc_env_img_aud_sat.pt
pt · 47.6 MB · SHA-256 790ccdb3d786…7ef6 · Hugging Face
Download--- language: - en tags: - ecology - multimodal - missing-modality - masking - species-distribution-modeling - species-classification library_name: pytorch pipeline_tag: image-classification datasets: - MVRL/TaxaBench-8k metrics: - accuracy - auc license: mit --- # 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 ecol...
Source context: 0 downloads · 2 likes · Pipeline image-classification · Library pytorch · Repo zbirobin/MIAM