Add model card

README.md

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 ---
-license: apache-2.0
+license: agpl-3.0
+library_name: pytorch
+pipeline_tag: object-detection
+tags:
+- remote-sensing
+- visual-grounding
+- sar
+- optical
+- cross-domain
+- mixture-of-experts
+- contrastive-learning
+- benchmark
 ---
+
+# OptiSAR-Net++ — Official Weights
+
+Official trained weights (`best.pt`) of **OptiSAR-Net++: A Large-Scale Benchmark and Transformer-Free Framework for Cross-Domain Remote Sensing Visual Grounding**.
+
+OptiSAR-Net++ is a **transformer-free** framework for Cross-Domain Remote Sensing Visual Grounding (CD-RSVG): a single unified model localizes targets described by free-form natural language in **both optical and SAR** remote sensing imagery, replacing a heavy Transformer decoder with **contrastive region–text matching**.
+
+- 🤗 **Dataset**: [JunDong-dev/OptSAR-RSVG](https://huggingface.co/datasets/JunDong-dev/OptSAR-RSVG)
+- 💻 **Code**: [GitHub — JunDong-dev/OptiSAR-Net-PlusPlus](https://github.com/JunDong-dev/OptiSAR-Net-PlusPlus)
+
+## 🏗️ Architecture
+
+The checkpoint follows a YOLOE-style single-stage detector extended with:
+
+| Component | Location | Role |
+|---|---|---|
+| **PLoRA-MoE** | Backbone | Patch-level low-rank-adaptation Mixture-of-Experts for optical/SAR feature disentanglement |
+| **TGDG-SSA** | Neck (×3) | Language-guided multi-scale fusion of visual features with text embeddings |
+| **OptiSARNetPlusPlusDetect** | Head | Region–text contrastive matching head with region-aware auxiliary supervision |
+| MobileCLIP2-B (frozen) | Text encoder | Encodes referring expressions |
+
+Key config of this checkpoint: `nc: 16` classes, `scale: m`, `reg_max: 16`.
+
+## 📦 Files
+
+| File | Description |
+|---|---|
+| `best.pt` | Trained weights (≈ 129 MB). The model architecture/modules are defined in the [GitHub repository](https://github.com/JunDong-dev/OptiSAR-Net-PlusPlus). |
+
+## 📈 Results — OptSAR-RSVG Test Split
+
+Evaluated on the [OptSAR-RSVG](https://huggingface.co/datasets/JunDong-dev/OptSAR-RSVG) test split (4,434 images / 8,103 referring expressions). All values in %.
+
+| Domain | Samples | Pr@0.5 | Pr@0.6 | Pr@0.7 | Pr@0.8 | Pr@0.9 | meanIoU | cumIoU |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|
+| **All** | 8,103 | **93.61** | 93.19 | 91.94 | 87.75 | 66.43 | **85.94** | **92.11** |
+| Optical | 6,027 | 93.01 | 92.58 | 91.67 | 88.58 | 72.99 | 86.48 | 92.25 |
+| SAR | 2,076 | 95.33 | 94.94 | 92.73 | 85.31 | 47.40 | 84.38 | 83.21 |
+
+Benchmark comparisons against TransVG, LQVG, TACMT, CSDNet, Grounding DINO, GLIP, etc. are reported in the [paper](https://arxiv.org/abs/2603.24876) and the [GitHub README](https://github.com/JunDong-dev/OptiSAR-Net-PlusPlus).
+
+## 🚀 Usage
+
+`best.pt` contains **custom modules** (`PLoRA_MoE`, `TGDG_SSA`, `OptiSARNetPlusPlusDetect`), so it must be loaded with the project code rather than a stock Ultralytics install:
+
+```bash
+# 1. Clone the official repository (defines the custom modules & inference entry points)
+git clone https://github.com/JunDong-dev/OptiSAR-Net-PlusPlus.git
+cd OptiSAR-Net-PlusPlus
+pip install -r requirements.txt
+
+# 2. Download the checkpoint and the dataset
+hf download JunDong-dev/OptiSAR-Net-PlusPlus best.pt --local-dir .
+hf download JunDong-dev/OptSAR-RSVG --repo-type dataset --local-dir OptSAR-RSVG
+
+# 3. Run evaluation / inference with the scripts provided in the repository README
+```
+
+Evaluation metrics follow the standard CD-RSVG protocol: Pr@{0.5–0.9}, meanIoU, cumIoU, with per-domain (`optical` / `sar`) reporting.
+
+## 🎯 Intended Use
+
+- Cross-domain (optical ↔ SAR) referring expression comprehension / visual grounding in remote sensing
+- Research on multi-modal fusion, parameter-efficient MoE adaptation, and contrastive region–text matching
+
+## ⚠️ Limitations
+
+- Trained and evaluated only on the 16 categories of OptSAR-RSVG; out-of-distribution categories/imagery may degrade
+- SAR performance at strict IoU thresholds (Pr@0.9) is notably lower than optical, reflecting SAR imaging characteristics
+
+## ⚖️ License
+
+Code and weights are released under **AGPL-3.0**. The companion dataset is subject to its source datasets' licenses (see the dataset card).
+
+## 📚 Citation
+
+```bibtex
+@article{tang2026optisar,
+  title={OptiSAR-Net++: A Large-Scale Benchmark and Transformer-Free Framework for Cross-Domain Remote Sensing Visual Grounding},
+  author={Tang, Xiaoyu and Dong, Jun and Cheng, Jintao and Fan, Rui},
+  journal={arXiv preprint arXiv:2603.24876},
+  year={2026}
+}
+```