Update English model card

README.md

@@ -1,19 +1,18 @@
 ---
-frameworks: PyTorch
+license: gpl-3.0
 language:
 - en
-license: apache-2.0
 tags:
 - OneScience
 - Earth Science
-- Remote Sensing
-- Masked Image Modeling
-- Sentinel-2
-- SpectralGPT
-- arxiv:2311.07113
-tasks: []
-datasets: []
+- Hyperspectral Remote Sensing
+- Masked Autoencoder
+frameworks: PyTorch
+datasets:
+- fMoW-Sentinel
+- BigEarthNet
 ---
+
 <p align="center">
   <strong>
     <span style="font-size: 30px;">SpectralGPT</span>
@@ -22,50 +21,48 @@ datasets: []
 
 # Model Introduction
 
-SpectralGPT learns general-purpose representations from large-scale multispectral remote sensing imagery through three-dimensional spatial-spectral masked modeling and multi-objective reconstruction. It is designed for scene classification, semantic segmentation, and change detection under limited-label conditions.
-
-Paper: SpectralGPT: Spectral Remote Sensing Foundation Model
+SpectralGPT is a foundation model for spectral remote sensing imagery. It learns cross-band and spatial structures through spatial-spectral 3D patching, masked autoencoding, and progressive pretraining, and can provide representations for tasks such as classification, segmentation, and change detection.
 
+Paper: SpectralGPT: Spectral Remote Sensing Foundation Model  
 https://arxiv.org/abs/2311.07113
 
 # Model Description
 
-SpectralGPT was proposed by researchers from the Aerospace Information Research Institute, Chinese Academy of Sciences, and related institutions. It is trained with 12-band Sentinel-2 imagery from fMoW-S2 and BigEarthNet-S2 and is suitable for single-label or multi-label scene classification, semantic segmentation, and change detection.
+SpectralGPT was proposed by a research team from Northwestern Polytechnical University and other institutions. The model is first trained on `96x96` fMoW-Sentinel data and then performs second-stage progressive pretraining on `128x128` BigEarthNet data. It is suitable for multispectral image reconstruction, spectral remote sensing representation learning, and transfer to downstream remote sensing tasks.
 
 # Use Cases
 
 | Scenario | Description |
 | :---: | :--- |
-| Multispectral masked pretraining | Train SpectralGPT with normalized 12-band Sentinel-2 multispectral imagery. |
-| Remote sensing scene understanding | Use learned multispectral representations for scene classification, semantic segmentation, and change detection. |
-| Local quick validation | Use synthetic data to validate data loading, masked reconstruction training, inference, evaluation, and visualization. |
-| Hugging Face / OneCode execution | Download the standalone model package, install dependencies, and run the scripts directly. |
-| Multi-GPU training | Launch distributed multi-process training with `torchrun`. |
+| Progressive pretraining | Sequentially perform `96x96` first-stage and `128x128` second-stage training. |
+| Spatial-spectral reconstruction | Perform masked reconstruction of 12-band spatial-spectral Sentinel-2 patches. |
+| Multispectral land-cover classification | Transfer spatial-spectral representations and fine-tune for land-cover classification on datasets such as EuroSAT and BigEarthNet. |
+| Semantic segmentation and change detection | Adapt the pretrained encoder to pixel-level land-cover segmentation and bi-temporal remote sensing change detection tasks. |
+| Local engineering validation | Use a small amount of synthetic data to check the training, inference, and evaluation workflows. |
+| Multi-GPU training | Launch distributed training with `torchrun`. |
 
 # Usage Guide
 
-## 1. OneCode Usage
+## 1. OneCode
 
 Experience intelligent one-click AI4S programming through the OneCode online environment:
 
 [Click to Experience Intelligent One-Click AI4S Programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)
 
-## 2. Manual Installation and Usage
-
-**Hardware Requirements**
-
-- A GPU or DCU is recommended.
-- CPU can be used for import and small-scale connectivity verification; full training and inference will be slower.
-- DCU users must install DTK in advance. DTK 25.04.2 or above, or the OneScience recommended version matching the cluster, is recommended.
-
-### Download the Model Package
+## 2. Download and Installation
 
 ```bash
 hf download OneScience-Group/SpectralGPT --local-dir ./SpectralGPT
 cd SpectralGPT
 ```
 
-### Install the Runtime Environment
+### Environment Dependencies
+
+**Hardware Requirements**
+
+- A GPU or DCU is recommended.
+- CPU can be used for small-configuration connectivity validation; full training and inference will be slow.
+- DCU users must install DTK in advance. DTK 25.04.2 or above, or the OneScience recommended version matching the current cluster, is recommended.
 
 **DCU Environment**
 
@@ -89,69 +86,58 @@ pip install onescience[earth-gpu] -i http://mirrors.onescience.ai:3141/pypi/simp
 
 ### Training Data Introduction
 
-The paper uses 12 Sentinel-2 spectral bands and excludes B10, with each band scaled to `[0,1]`. The fMoW-S2 dataset contains 882,779 images, including 712,874 images used for the first pretraining stage. BigEarthNet-S2 contains 590,326 images, including 354,196 images used for subsequent pretraining. OneScience does not currently provide fMoW-S2, BigEarthNet-S2, or EuroSAT data that can be downloaded directly for this repository, so the default workflow uses a small synthetic multispectral dataset for pipeline validation.
+By default, a small amount of synthetic data is used to validate the two-stage engineering workflow. The first stage uses fMoW-Sentinel-style samples, and the second stage uses BigEarthNet-style samples.
 
-Official EuroSAT data resources:
-
-```text
-Official Zenodo page:
-https://zenodo.org/records/7711810
-
-Multispectral data download:
-https://madm.dfki.de/files/sentinel/EuroSATallBands.zip
-```
+The synthetic data preserves the official progressive pretraining input specifications of 12 bands, `96x96` in the first stage, and `128x128` in the second stage.
 
-After extracting the EuroSAT multispectral data, use the following command to exclude B10, scale the bands to `[0,1]`, and convert the 13-band TIFF files into the 12-band NPZ data used by this repository:
+Real data must be preprocessed and converted to the following NPZ training protocol. This protocol is consistent with the model input specification but is not the download format of the original datasets.
 
-```bash
-python scripts/fake_data.py --real-dir ./data/EuroSAT_MS
+```text
+stage1:
+images: float32 [N,12,96,96]
+band_order: string [12]
+normalization: string scalar
+scale_factors: float32 [N]
+stage: string scalar = stage1
+
+stage2:
+images: float32 [N,12,128,128]
+band_order: string [12]
+normalization: string scalar
+scale_factors: float32 [N]
+stage: string scalar = stage2
 ```
 
-Synthetic data is saved to `data/fake_spectralgpt.npz` and is used only to validate data loading, masked reconstruction training, checkpoint saving and loading, inference, evaluation, and visualization. It does not represent real Sentinel-2 imagery and cannot reproduce the paper results. Real-data training should not run `scripts/fake_data.py` without `--real-dir`, and the input TIFF files must use a consistent band order, spatial size, and value scaling method.
-
-### Training
-
-Local quick validation with synthetic data:
+`fake_data.py` automatically writes the `protocol` and `data_source` protocol metadata. These fields must be retained when using real data.
 
 ```bash
 python scripts/fake_data.py
-python scripts/train.py
 ```
 
-Training with real EuroSAT multispectral TIFF files:
-
-```bash
-python scripts/fake_data.py --real-dir ./data/EuroSAT_MS
-python scripts/train.py
-```
-
-Synthetic and real data use the same training script. Adjust `data.samples`, `model.image_size`, `training.epochs`, and `training.batch_size` in `conf/config.yaml` as needed.
-
-Single GPU or CPU:
+### Training
 
 ```bash
 python scripts/train.py
 ```
 
-Multi-GPU:
+For multi-GPU training, use:
 
 ```bash
-torchrun --nproc_per_node=8 --nnodes=1 --rdzv_id=1000 --rdzv_backend=c10d --max_restarts=0 --master_addr="localhost" --master_port=29500 scripts/train.py
+torchrun --nproc_per_node=8 scripts/train.py
 ```
 
-Training outputs:
+Training first completes the 96-size first stage, then interpolates the spatial positional encoding and completes the 128-size second stage, saving stage checkpoints, a final checkpoint, and aggregate training metrics. The default configuration is intended for quick workflow validation. Formal experiments should use the two-stage data scale, model configuration, and training duration corresponding to the paper.
 
 ```text
-result/checkpoints/best.pth
-result/checkpoints/last.pth
-Each epoch prints reconstruction_loss.
+result/checkpoints/stage1.pth
+result/checkpoints/stage2.pth
+result/checkpoints/final.pth
+result/training/metrics.json
 ```
 
-The `data_source` and `protocol` fields in the training logs and checkpoints identify whether synthetic or real data was used. Checkpoints generated from synthetic data are only for pipeline validation and do not provide real remote sensing representations.
-
 ### Training Weights
 
-Multispectral Sentinel-2 training weights will be provided under `weight/` in a future update. No trained weights are included in the current package.
+This repository will provide SpectralGPT training weights in the `weight/` folder. The weight files will be uploaded soon and are expected to be available in the near future.
 
 ### Inference
 
@@ -159,9 +145,7 @@ Multispectral Sentinel-2 training weights will be provided under `weight/` in a
 python scripts/inference.py
 ```
 
-Inference reads the configured dataset and the checkpoint generated during training, then produces multispectral image reconstruction results.
-
-Prediction output:
+Inference loads the final second-stage checkpoint, performs masked reconstruction on the `128x128` test data, and saves the results to:
 
 ```text
 result/output/reconstruction.npz
@@ -173,15 +157,13 @@ result/output/reconstruction.npz
 python scripts/result.py
 ```
 
-Evaluation and visualization outputs:
+Evaluation reports masked-region MSE, MAE, PSNR, spectral angle, and per-band RMSE, and generates a figure containing the input, visible region, prediction, and composite result. Results on synthetic data are only for engineering workflow validation and do not represent full-paper performance.
 
 ```text
 result/output/metrics.json
-result/output/reconstruction.ppm
+result/output/reconstruction.png
 ```
 
-The metrics include reconstruction MSE, MAE, PSNR, and per-band RMSE. The visualization displays input and reconstructed false-color composites. Synthetic data is used only to confirm that the evaluation and visualization pipeline runs successfully; its numerical values are not reported as model performance. Real-data metrics represent masked reconstruction on the user-provided imagery.
-
 # Official OneScience Resources
 
 | Platform | OneScience Main Repository | Skills Repository |
@@ -191,5 +173,6 @@ The metrics include reconstruction MSE, MAE, PSNR, and per-band RMSE. The visual
 
 # Citation and License
 
-- This repository is a reproduction of the original SpectralGPT paper.
-- The model package is released under Apache License 2.0. The original datasets and model weights remain subject to their respective source licenses.
+This repository is a reproduction of the original SpectralGPT paper.
+
+Use of the code and data in this repository remains subject to the licenses and terms of use of their respective projects.