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README.md

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+---
+language:
+- en
+
+license: apache-2.0
+
+pretty_name: SSDD SAR Ship Detection Dataset
+
+task_categories:
+- object-detection
+
+tags:
+- object-detection
+- sar
+- synthetic-aperture-radar
+- ship-detection
+- maritime
+- remote-sensing
+- computer-vision
+- ultralytics
+- yolo
+
+size_categories:
+- 1K<n<10K
+
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/images/train/metadata.jsonl
+  - split: validation
+    path: data/images/valid/metadata.jsonl
+  - split: test
+    path: data/images/test/metadata.jsonl
+---
+
+# SSDD: SAR Ship Detection Dataset (Object Detection)
+
+<p align="center">
+  <img src="ssdd_banner.jpg" alt="SSDD Dataset Banner"/>
+</p>
+
+![Task](https://img.shields.io/badge/Task-Object%20Detection-blue?style=flat-square)
+![Dataset](https://img.shields.io/badge/Dataset-SSDD-0aa1a7?style=flat-square)
+![Format](https://img.shields.io/badge/Format-YOLO%20Compatible-orange?style=flat-square)
+![Classes](https://img.shields.io/badge/Classes-1-success?style=flat-square)
+![Splits](https://img.shields.io/badge/Splits-Train%20%7C%20Val%20%7C%20Test-blueviolet?style=flat-square)
+![License](https://img.shields.io/badge/License-Apache--2.0-lightgrey?style=flat-square)
+
+> **Unofficial redistribution of SSDD (SAR Ship Detection Dataset), reformatted into a standardized YOLO-compatible directory layout with a seeded validation split.**
+
+## Disclaimer
+
+This repository is **not** an official release of SSDD.
+
+SSDD was created by Tianwen Zhang, Xiaoling Zhang, Jianwei Li, and co-authors, who retain all copyright. This repository does **not** claim ownership of any images, annotations, or metadata.
+
+This repository exists for two purposes:
+
+1. To reorganize the official `BBox_SSDD/coco_style` release into a standardized YOLO/Ultralytics-compatible directory structure.
+2. To provide a defined train/val/test split, since the official release ships train/test only (see [Changes from the Official Release](#changes-from-the-official-release) below).
+
+**Direct provenance.** This redistribution is sourced directly from the [official GitHub repository](https://github.com/TianwenZhang0825/Official-SSDD) (`TianwenZhang0825/Official-SSDD`), which links to the actual data on Google Drive / Baidu Cloud. This repository converts the already-COCO-formatted `BBox_SSDD/coco_style` variant to YOLO format and applies a seeded validation split.
+
+---
+
+# Dataset Description
+
+SSDD (SAR Ship Detection Dataset) is a benchmark for ship detection in Synthetic Aperture Radar imagery: **1,160 images with 2,587 ship instances**, composited from **RadarSat-2, TerraSAR-X, and Sentinel-1** at resolutions from 1m to 15m, across multiple polarizations (HH/VV/VH/HV) and both inshore and offshore scenes. It is used to benchmark SAR ship detection, where speckle noise and side-lobe artifacts make optical-trained detectors unreliable -- the same problem [HRSID](https://huggingface.co/datasets/dronefreak/HRSID) targets, from a different sensor mix.
+
+This repository preserves every image and box while re-encoding the labels for YOLO compatibility and adding a reproducible validation split (see [Changes from the Official Release](#changes-from-the-official-release) below).
+
+---
+
+# Changes from the Official Release
+
+- **Validation split carved out of train.** The official release provides only `train` (928 images) and `test` (232 images). This repository holds out a **seeded random 15%** of `train` as `valid` (139 images), leaving 789 for `train`; `test` is kept as-is, unchanged.
+- **Format converted.** The official release already ships COCO-format annotations (`annotations/{train,test}.json`, single class `ship` already at id 0); this repository additionally provides a YOLO-format export (normalized `class x_center y_center width height` `.txt` files), alongside a re-split canonical COCO layout.
+- **`test_inshore` / `test_offshore` not separately included.** These are filtered *subsets* of `test` provided by the official release for scene-specific analysis, not additional data -- everything in both subsets is already covered by `test`.
+- No image pixel content was modified. No boxes were added or removed.
+
+---
+
+# Dataset Structure
+
+```text
+<repo>/
+├── README.md
+├── ssdd_banner.jpg
+└── data/
+    ├── data.yaml
+    ├── images/
+    │   ├── train/   (789 *.jpg)
+    │   ├── valid/   (139 *.jpg)
+    │   └── test/    (232 *.jpg)
+    └── labels/
+        ├── train/   (789 *.txt)
+        ├── valid/
+        └── test/
+```
+
+* `data/images/<split>/` contains the SAR ship-detection images for each split.
+* `data/labels/<split>/` contains one YOLO-format `.txt` annotation file per image (`class x_center y_center width height`, normalized; empty for images with no annotated ship).
+* `data/data.yaml` is the Ultralytics dataset configuration file.
+* Splits: **train** 789 images / 1,756 boxes &middot; **valid** 139 images / 285 boxes &middot; **test** 232 images / 546 boxes (1,160 images / 2,587 boxes total).
+
+### Classes (1)
+
+`ship` -- single class.
+
+Full split summary, class-distribution chart, and box geometry: see the [dataset statistics report](https://github.com/dronefreak/DetectionBench/blob/main/docs/datasets/ssdd/README.md).
+
+---
+
+# Dataset Sources
+
+## Original Paper
+
+**SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis**
+
+Tianwen Zhang, Xiaoling Zhang, Jianwei Li, Xiaowo Xu, Baoyou Wang, Xu Zhan, Yanqin Xu, Xu Ke, Tianjiao Zeng, Hao Su, and others
+
+*Remote Sensing*, 13(18):3690, 2021.
+
+## Official Resources
+
+- **GitHub Repository:** https://github.com/TianwenZhang0825/Official-SSDD
+
+---
+
+# Attribution
+
+**All credit for the dataset belongs entirely to the original SSDD authors: Tianwen Zhang, Xiaoling Zhang, Jianwei Li, and their co-authors.**
+
+This repository only reformats the official release into a YOLO layout and adds a seeded validation split.
+
+If you use this dataset in your research, **please cite the original publication below.**
+
+---
+
+# License
+
+The [official GitHub repository](https://github.com/TianwenZhang0825/Official-SSDD) carries an explicit **Apache License 2.0** (`LICENSE` file, confirmed via GitHub's own license detection -- not just a badge claim). This is a real, unambiguous grant, unlike HRSID's software-only GPL-3.0 situation.
+
+**A caveat worth knowing.** SSDD's imagery is composited from RadarSat-2, TerraSAR-X, and Sentinel-1. TerraSAR-X / TanDEM-X data is distributed by DLR under a scientific-use license that does not, in general, permit free redistribution of derived image products -- independent of what the repackager's own repo license grants. It is not possible to determine from public information which images derive from which sensor, or whether the SSDD authors obtained separate redistribution rights for the TerraSAR-X-derived portion specifically. This is the same second-order sensor-rights situation documented on [HRSID's card](https://huggingface.co/datasets/dronefreak/HRSID).
+
+Given the repo's own license is explicit (unlike HRSID's), this repository is tagged `apache-2.0` rather than `unknown`. If you plan to use this dataset for anything beyond personal research experimentation, we recommend being aware of the TerraSAR-X provenance question above.
+
+Accordingly:
+
+- Attribution to the original creators is required.
+- Commercial and non-commercial use are both permitted per the stated Apache-2.0 terms.
+- No share-alike obligation.
+
+---
+
+# Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@article{zhang2021sar,
+  title={SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis},
+  author={Zhang, Tianwen and Zhang, Xiaoling and Li, Jianwei and Xu, Xiaowo and Wang, Baoyou and Zhan, Xu and Xu, Yanqin and Ke, Xu and Zeng, Tianjiao and Su, Hao and others},
+  journal={Remote Sensing},
+  volume={13},
+  number={18},
+  pages={3690},
+  year={2021}
+}
+```
+
+---
+
+# Acknowledgements
+
+We sincerely thank Tianwen Zhang, Xiaoling Zhang, Jianwei Li, and their co-authors for creating and publicly releasing this valuable SAR ship-detection benchmark.

data/data.yaml

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+path: /home/neural_debugger/Downloads/datasets/ObjectDetectionDatasets/SSDD/yolo_dataset
+train: images/train
+val: images/val
+test: images/test
+nc: 1
+names:
+  0: ship