---
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:
- code
- deeplearning
- torch
- objectDetection/segmentation
---

# 🚢 HRSID Ship Detection Demo

This Gradio Space demonstrates a Faster R-CNN model trained on HRSID dataset for ship detection in SAR imagery.

Upload a SAR image to detect ships.


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

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

## Abstract

Synthetic Aperture Radar (SAR) imagery enables all-weather, day-and-night maritime monitoring. However, ship detection in SAR images is challenging due to speckle noise, varying vessel scales, and complex coastal backgrounds.

This work presents a **Faster R-CNN (ResNet-50 FPN)** based object detection model trained on the **HRSID dataset** for robust ship detection in high-resolution SAR imagery.

---

## 📷 Qualitative Results

### Detection Output
![Output](./im.png)

The model successfully detects ships of varying scales in cluttered maritime environments.

---

## 🧠 Model Architecture

- **Framework:** Detectron2
- **Detector:** Faster R-CNN
- **Backbone:** ResNet-50 + Feature Pyramid Network (FPN)
- **Classes:** 1 (Ship)
- **Inference Device:** CPU compatible

---

## 📊 Training Configuration

| Parameter | Value |
|-----------|--------|
| Dataset | HRSID |
| Image Resolution | 1400 × 1400 |
| Iterations | {MAX_ITER} |
| Score Threshold | {SCORE_THRESH} |
| Optimizer | SGD |
| Learning Rate | 0.0025 |

---

## 📈 Evaluation

| Metric | Value |
|--------|--------|
| mAP@0.5 | (Add your value) |
| Precision | (Add value) |
| Recall | (Add value) |

> Note: Metrics computed on validation split of HRSID dataset.

---

## 🛰 Dataset Description

HRSID (High-Resolution SAR Images Dataset) contains:

- 5000+ SAR images
- Thousands of annotated ship bounding boxes
- Multi-scale vessel distribution
- Coastal and open-sea scenarios

---

## ⚙ Inference Code

```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

predictor = DefaultPredictor(cfg)

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