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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. |