Update English model card

README.md

@@ -1,22 +1,19 @@
 ---
-frameworks: PyTorch
+license: apache-2.0
 language:
 - en
-license: apache-2.0
 tags:
 - OneScience
 - Earth Science
-- Remote Sensing
+- Remote Sensing Image-Text Retrieval
 - Vision-Language Model
-- Image-Text Retrieval
-- RemoteCLIP
-- arxiv:2306.11029
-tasks: []
+frameworks: PyTorch
 datasets:
 - RSITMD
 - RSICD
 - UCM-Captions
 ---
+
 <p align="center">
   <strong>
     <span style="font-size: 30px;">RemoteCLIP</span>
@@ -25,50 +22,48 @@ datasets:
 
 # Model Introduction
 
-RemoteCLIP is a vision-language foundation model for remote sensing. It learns semantically rich visual representations by aligning remote sensing images with text descriptions and supports image-text retrieval, zero-shot classification, few-shot classification, linear probing, k-NN classification, and remote sensing object counting.
-
-Paper: RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
+RemoteCLIP is a vision-language foundation model for remote sensing imagery and text. It aligns remote sensing visual semantics with natural-language descriptions through CLIP dual encoders and bidirectional contrastive learning, supporting cross-modal retrieval and transfer to downstream remote sensing tasks.
 
+Paper: RemoteCLIP: A Vision Language Foundation Model for Remote Sensing  
 https://arxiv.org/abs/2306.11029
 
 # Model Description
 
-RemoteCLIP was proposed by researchers from Hohai University, the Hong Kong University of Science and Technology, Nanjing Forestry University, the Chinese Academy of Forestry, and Griffith University. It is continually pretrained on RSITMD, RSICD, UCM-Captions, and remote sensing detection, segmentation, and UAV imagery converted into image-text pairs through B2C and M2B, and is suitable for remote sensing image-text retrieval and open-vocabulary recognition.
+RemoteCLIP was proposed by a research team from the National University of Defense Technology and other institutions. The model performs continual pretraining with RSITMD, RSICD, UCM-Captions, and remote sensing image-text data constructed by converting task datasets. It is suitable for remote sensing image-text retrieval, zero-shot classification, and vision-language representation learning.
 
 # Use Cases
 
 | Scenario | Description |
 | :---: | :--- |
-| Remote sensing image-text contrastive training | Train RemoteCLIP with paired remote sensing images and text descriptions. |
-| Remote sensing image-text retrieval | Retrieve text from images or remote sensing images from text queries. |
-| Local quick validation | Use synthetic image-text pairs to validate 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`. |
+| Remote sensing image-text retrieval | Compute cross-modal similarities between image and text features. |
+| Multi-positive contrastive learning | Use `pair_ids` to express positive relationships such as one image paired with multiple texts. |
+| Zero-shot scene classification | Match natural-language class prompts with image features to recognize remote sensing scenes without training an additional classification head. |
+| Few-shot visual recognition | Transfer vision-language representations and perform few-shot fine-tuning or linear probing for remote sensing classification with limited labels. |
+| 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 slow.
-- 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/RemoteCLIP --local-dir ./RemoteCLIP
 cd RemoteCLIP
 ```
 
-### 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**
 
@@ -92,45 +87,37 @@ pip install onescience[earth-gpu] -i http://mirrors.onescience.ai:3141/pypi/simp
 
 ### Training Data Introduction
 
-The paper unifies three groups of heterogeneous remote sensing datasets into paired image-text data. RET-3 contains RSITMD, RSICD, and UCM-Captions; DET-10 contains satellite and UAV object detection datasets; SEG-4 contains the iSAID, LoveDA, Potsdam, and Vaihingen semantic segmentation datasets. Detection annotations are converted into descriptions through Box-to-Caption, while segmentation masks are converted to boxes through Mask-to-Box before caption generation. The resulting pretraining collection is approximately 12 times larger than the original remote sensing image-text datasets combined.
+By default, 8 training and 4 test synthetic image-text samples are used to validate the engineering workflow. They are saved as `data/train.npz` and `data/test.npz`, respectively. Tokens follow the OpenAI CLIP BPE vocabulary of 49,408 entries and the SOT, EOT, and padding sequence constraints.
+
+The synthetic data preserves the official model input specifications of 3-channel `224x224` images and CLIP text sequences of length 77.
 
-The paper datasets remain subject to their respective download and usage licenses. The authors' official code and model resources are available at:
+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.
 
 ```text
-https://github.com/ChenDelong1999/RemoteCLIP
+images: float32 [N,3,224,224]
+tokens: int64 [N,77]
+pair_ids: int64 [N]
 ```
 
-The complete paper pretraining dataset is not included in this model repository. Generate compact synthetic data for local validation with:
+`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
 ```
 
-This command creates 32 training pairs and 8 test pairs in `data/remoteclip.npz`. Synthetic data only validates image and text encoding, bidirectional InfoNCE training, checkpoint loading, retrieval inference, evaluation, and similarity-matrix visualization. The complete synthetic pipeline has been verified successfully, but synthetic training values are not reported as model performance.
-
-For real-data training, organize remote sensing images and text descriptions into paired samples following the paper or the official repository and convert them to `data/remoteclip.npz`. Do not run `scripts/fake_data.py` for real-data training. Set `data.protocol` in `conf/config.yaml` to `real_remoteclip` and update the image size, vocabulary size, context length, and training parameters for the converted dataset.
-
 ### Training
 
-Generate synthetic data before local quick validation:
-
-```bash
-python scripts/fake_data.py
-```
-
-Single GPU or CPU:
-
 ```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 optimizes the image and text dual encoders with a multi-positive bidirectional contrastive objective and saves a checkpoint and aggregate training metrics. The default configuration is intended for quick workflow validation. Formal experiments should use the image-text data scale, model configuration, and training duration corresponding to the paper.
 
 ```text
 result/checkpoints/remoteclip.pt
@@ -139,7 +126,7 @@ result/training/metrics.json
 
 ### Training Weights
 
-Pretrained weights based on paired remote sensing image-text data will be provided under `weight/`. The weight file will be uploaded in a future update. Checkpoints produced from synthetic data validate the pipeline only and do not provide real remote sensing image-text retrieval capability.
+This repository will provide RemoteCLIP training weights in the `weight/` folder. The weight files will be uploaded soon and are expected to be available in the near future.
 
 ### Inference
 
@@ -147,9 +134,7 @@ Pretrained weights based on paired remote sensing image-text data will be provid
 python scripts/inference.py
 ```
 
-Inference extracts image features, text features, and the cross-modal similarity matrix from the test pairs.
-
-Prediction output:
+Inference loads the training checkpoint, computes test-set image and text features and their similarities, and saves the results to:
 
 ```text
 result/output/retrieval.npz
@@ -161,15 +146,13 @@ result/output/retrieval.npz
 python scripts/result.py
 ```
 
-Evaluation outputs:
+Using `pair_ids`, evaluation reports bidirectional retrieval R@1, R@5, R@10, and mean recall, and generates a similarity heatmap. Results on synthetic data are only for engineering workflow validation and do not represent full-paper performance.
 
 ```text
 result/evaluation/metrics.json
 result/evaluation/similarity_matrix.png
 ```
 
-The evaluation script computes image-to-text and text-to-image R@1, R@5, and mean recall, and renders the image-text similarity matrix. Synthetic data is used only to confirm that evaluation and visualization outputs can be generated successfully; its numerical values are not reported as model performance.
-
 # Official OneScience Resources
 
 | Platform | OneScience Main Repository | Skills Repository |
@@ -179,5 +162,6 @@ The evaluation script computes image-to-text and text-to-image R@1, R@5, and mea
 
 # Citation and License
 
-- This repository is a reproduction of the original RemoteCLIP paper.
-- The reproduction code is released under Apache License 2.0. The original datasets and official model weights remain subject to their respective source licenses.
+This repository is a reproduction of the original RemoteCLIP paper.
+
+Use of the code and data in this repository remains subject to the licenses and terms of use of their respective projects.