AgriFM

--- license: apache-2.0 library_name: transformers pipeline_tag: image-segmentation tags: - remote-sensing - agriculture - multispectral - temporal - segmentation --- > [!WARNING] we do not have a full checkpoint conversion validation, if you encounter pipeline loading failure and unsidered output, please contact me via [email protected] # AgriFM ## Model Description This repository provides a Hugging Face-compatible checkpoint for **AgriFM**, a multi-source temporal remote sensing model for agricultural mapping. The checkpoint was converted from a legacy PyTorch `.pth` file into standard `transformers`-style artifacts: - `config.json` - `model.safetensors` ## Model Sources - Paper: [AgriFM: A Multi-source Temporal Remote Sensing Foundation Model for Agriculture Mapping](https://arxiv.org/abs/2505.21357) - Original codebase used for conversion: [Bili-Sakura/AgriFM-transformers](https://github.com/Bili-Sakura/AgriFM-transformers) - Raw source checkpoint: `path/to/raw/AgriFM.pth` ## Conversion Notes - The raw checkpoint uses a legacy key layout (`encoder.*`), which was remapped to Hugging Face `PreTrainedModel` state dict conventions. - The raw checkpoint is **encoder-only**. - Fusion neck and segmentation head parameters are initialized from model defaults in this converted package. ## Intended Use This checkpoint is intended for: - feature extraction from multi-source temporal remote sensing inputs - fine-tuning for segmentation tasks in agricultural or land-cover settings ## Input Format Expected inputs are a dict of tensors with shape `(B, T, C, H, W)`: - `HLSL30`: `C=6` - `S2`: `C=10` - `Modis`: `C=7` ## Usage ```python import torch from AgriFM.models import AgriFMConfig, AgriFMModel model_dir = "path/to/AgriFM" config = AgriFMConfig.from_pretrained(model_dir) model = AgriFMModel.from_pretrained(model_dir, config=config) model.eval() B, T, H, W = 1, 32, 256, 256 pixel_values = { "HLSL30": torch.randn(B, T, 6, H, W), "S2": torch.randn(B, T, 10, H, W), "Modis": torch.randn(B, T, 7, H, W), } with torch.no_grad(): outputs = model(pixel_values=pixel_values) features = outputs.features print(features.shape) ``` ## Limitations - This is a converted checkpoint and not an official upstream release. - Since the source is encoder-only, task heads may require downstream fine-tuning before production use. - Performance depends...