---
title: HRSID Ship Detection Demo
emoji: 🚢
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false

license: apache-2.0
tags:
  - object-detection
  - synthetic-aperture-radar
  - detectron2
  - faster-rcnn
  - ship-detection
datasets:
  - HRSID
metrics:
  - mAP
---

# 🚢 Ship Detection in SAR Imagery — Faster R-CNN (HRSID)

A **Faster R-CNN** model trained on the [HRSID](https://github.com/chaozhong2010/HRSID) dataset for robust ship detection in high-resolution Synthetic Aperture Radar (SAR) imagery. Upload a SAR image to detect ships in real time.

---

## 🧠 Model Architecture

| Component  | Detail                           |
|------------|----------------------------------|
| Framework  | Detectron2                       |
| Detector   | Faster R-CNN                     |
| Backbone   | ResNet-50 + FPN                  |
| Classes    | 1 (Ship)                         |
| Input Size | 1400 × 1400                      |
| Inference  | CPU-compatible (HF Spaces free)  |

---

## 📊 Training Configuration

| Parameter          | Value                        |
|--------------------|------------------------------|
| Dataset            | HRSID (train / val / test)   |
| Train Images       | 2,914                        |
| Val Images         | 728                          |
| Test Images        | 5,604                        |
| Total Instances    | 16,951 ships                 |
| Max Iterations     | 5,000                        |
| Batch Size         | 2                            |
| Base Learning Rate | 0.00025                      |
| Optimizer          | SGD                          |
| Eval Period        | Every 500 iterations         |
| Checkpoint Period  | Every 500 iterations         |
| Score Threshold    | 0.5                          |
| GPU                | Tesla T4 (15.6 GB VRAM)      |

**Augmentations:** Resize to 1400 × 1400, random horizontal flip.



## 📈 Evaluation Results

Evaluated on the HRSID **test split** (5,604 images) using COCO-style metrics.

| Metric                        | Value  |
|-------------------------------|--------|
| **AP (IoU=0.50:0.95)**        | **47.39** |
| **AP50 (IoU=0.50)**           | **71.88** |
| **AP75 (IoU=0.75)**           | **56.53** |
| APs (small objects)           | 48.54  |
| APm (medium objects)          | 48.55  |
| APl (large objects)           | 23.17  |
| AR @ maxDets=1                | 24.30  |
| AR @ maxDets=10               | 49.20  |
| AR @ maxDets=100              | 52.40  |
| AR small @ maxDets=100        | 50.60  |
| AR medium @ maxDets=100       | 66.70  |
| AR large @ maxDets=100        | 44.60  |

---

## 🛰 Dataset

[HRSID (High-Resolution SAR Images Dataset)](https://github.com/chaozhong2010/HRSID) is a benchmark for ship detection and instance segmentation in SAR imagery.

- **5,604** SAR image crops
- **16,951** annotated ship instances (bounding boxes + segmentation masks)
- Multi-scale vessel distribution across coastal and open-sea scenarios
- Derived from Sentinel-1, TerraSAR-X, and TanDEM-X satellite imagery

---

## ⚙️ Inference
````python
from detectron2.config import get_cfg
from detectron2.engine import DefaultPredictor
import cv2

cfg = get_cfg()
cfg.merge_from_file("config.yaml")
cfg.MODEL.WEIGHTS = "model_final.pth"
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
cfg.MODEL.DEVICE = "cpu"  # or "cuda"

predictor = DefaultPredictor(cfg)

image = cv2.imread("test_sar.jpg")
outputs = predictor(image)

instances = outputs["instances"]
print(f"Detected {len(instances)} ships")
print(f"Boxes:  {instances.pred_boxes}")
print(f"Scores: {instances.scores}")
````

---

## 📦 Repository Files

| File              | Description                          |
|-------------------|--------------------------------------|
| `model_final.pth` | Trained Faster R-CNN weights         |
| `config.yaml`     | Detectron2 model configuration       |
| `labels.json`     | Class label mapping `{0: "ship"}`    |
| `app.py`          | Gradio inference app                 |
| `requirements.txt`| Python dependencies                  |

---

## 🚀 Run Locally
````bash
git clone https://huggingface.co/PUSHPENDAR/hrsid-ship-detection
cd hrsid-ship-detection
pip install -r requirements.txt
python app.py
````

---

## 📝 Citation
````bibtex
@article{wei2020hrsid,
  title={HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation},
  author={Wei, Shunjun and Zeng, Xiangfeng and Qu, Qizhe and Wang, Mou and Su, Hao and Shi, Jun},
  journal={IEEE Access},
  volume={8},
  pages={96962--96980},
  year={2020},
  publisher={IEEE}
}
````

---

## 📄 License

Apache 2.0 — see [LICENSE](LICENSE) for details.