Update README.md

README.md

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 ---
 license: agpl-3.0
+tags:
+  - object-detection
+  - pose-estimation
+  - sar
+  - synthetic-aperture-radar
+  - remote-sensing
+  - earth-observation
+  - vessel-detection
+  - dark-vessel-detection
+  - yolo
+  - sentinel-1
+library_name: ultralytics
+pipeline_tag: object-detection
 ---
-# 📋 Vessel Detection Model
 
-This folder contains the inference model for detection of vessels in range-compressed SAR data, including:
+# Vessel Detection Model
 
-* An unrestricted YOLO26s-Pose model used as the main or teacher model
-* A lightweight YOLO26n-Pose model for faster deployment
+Inference models for detecting vessels (including dark/non-cooperative vessels not broadcasting AIS) in Sentinel-1 SAR data, developed as part of the [OpenSAR Insight](https://huggingface.co/opensar-insight) project. See the [organization card](https://huggingface.co/opensar-insight) for full project background, funding, and consortium details.
 
-These models were trained based on the filtered, higher quality subset of opensar-insight/vessel-detection-dataset.
+- Codebase: https://github.com/ESA-PhiLab/OpenSARInsight
+- Project website: https://opensarinsightweb.web.uah.es/
+- Training dataset: [opensar-insight/vessel-detection-dataset](https://huggingface.co/opensar-insight/vessel-detection-dataset)
 
-## 🧩 Detailed List of Models
-The main models available are:
-* Unrestricted teacher model: yolo26s-pose_opensar_true_vessel_filtered_dataset_640px_unrestricted.pt
-* Lightweight student model: yolo26n-pose_feature_kd_from_s_640_300ep_feat4_lightweight.pt
+## Model description
 
-## 💻 Codebase
-The full codebase, which includes training and inference capabalities, as well as script for further dataset generation, will soon be made open-source.
+This repository contains the inference models for vessel detection in range-compressed Sentinel-1 SAR data:
+
+- Teacher model (unrestricted): `yolo26s-pose_opensar_true_vessel_filtered_dataset_640px_unrestricted.pt` — a YOLO26s-Pose model used as the main/teacher model.
+- Student model (lightweight): `yolo26n-pose_feature_kd_from_s_640_300ep_feat4_lightweight.pt` — a YOLO26n-Pose model, distilled from the teacher via feature-based knowledge distillation, intended for faster/onboard deployment.
+
+Both models were trained on the filtered, higher-quality subset of [opensar-insight/vessel-detection-dataset](https://huggingface.co/opensar-insight/vessel-detection-dataset). Vessels are detected as pose keypoints (bounding box plus orientation), supporting downstream estimation of heading in addition to location.
+
+## Files
+
+| File | Role |
+|---|---|
+| `yolo26s-pose_opensar_true_vessel_filtered_dataset_640px_unrestricted.pt` | Teacher model (YOLO26s-Pose, 640px) |
+| `yolo26n-pose_feature_kd_from_s_640_300ep_feat4_lightweight.pt` | Distilled student model (YOLO26n-Pose, 640px, 300 epochs, feature KD) |
+
+## Usage
+
+These are Ultralytics YOLO-Pose checkpoints:
+
+```python
+from ultralytics import YOLO
+
+model = YOLO("yolo26s-pose_opensar_true_vessel_filtered_dataset_640px_unrestricted.pt")
+results = model.predict("path/to/sar_patch.png", imgsz=640)
+```
+
+For dataset generation, training, and the full processing pipeline (Level-0 to range-compressed to inference), see `backend/pipeline/dvd_use_case/` in the [OpenSARInsight GitHub repository](https://github.com/ESA-PhiLab/OpenSARInsight).
+
+## License
+
+These model weights are distributed under AGPL-3.0, since the vessel-detection pipeline (`backend/pipeline/dvd_use_case/`) depends on Ultralytics YOLO, which is itself AGPL-3.0 licensed. Other components of the OpenSAR Insight codebase (dataset generation, RFI pipeline, geocoding, SARFI) are MIT-licensed — see the [repository LICENSE](https://github.com/ESA-PhiLab/OpenSARInsight/blob/main/LICENSE) for the full breakdown.
+
+## Citation
+
+If you use this model, please cite the OpenSAR Insight project:
+
+```bibtex
+@misc{opensarinsight,
+  title        = {OpenSAR Insight: ML-ready datasets and models for direct insight generation from raw SAR data},
+  author       = {{Indra Space} and {INTA} and {Universidad de Alcal\'a de Henares}},
+  howpublished = {\url{https://github.com/ESA-PhiLab/OpenSARInsight}},
+  note         = {Funded by ESA \(\Phi\)-lab}
+}
+```