---
license: mit
tags:
  - object-detection
  - SAR
  - maritime
  - vessel-detection
  - yolov8
  - embedded-ai
  - tinyml
datasets:
  - SAR-Ship
metrics:
  - mAP
---

# YOLOv8n -- SAR Vessel Detection

YOLOv8 nano model trained for vessel detection in Synthetic Aperture Radar (SAR) imagery.
Quantized and exported for embedded edge deployment (Jetson, Google Coral).

## Model Details

- **Base model**: YOLOv8n
- **Task**: Object Detection (vessel/ship)
- **Dataset**: SAR-Ship (Roboflow)
- **Input size**: 640x640
- **Target hardware**: Jetson Orin Nano, Google Coral Edge TPU, RTX GPUs

## Performance

| Model | mAP50 | mAP50-95 | Precision | Recall | Size (MB) |
|---|---|---|---|---|---|
| PyTorch FP32 | 0.9168 | 0.6646 | 0.9183 | 0.84 | 6.26 |
| TorchScript FP16 | 0.9124 | 0.6271 | 0.9278 | 0.8274 | 12.43 |


## Repository Structure

```
unquantized/
  best.pt               (PyTorch FP32 - original trained weights)

quantized/
  best.onnx             (ONNX FP32 - cross-platform inference)
  best.torchscript      (TorchScript FP16 - Jetson / RTX GPUs)
  best_int8.tflite      (TFLite INT8 - Google Coral / MCU, Linux only)

evaluation_results.json
evaluation_results.csv
```

## Available Formats

| File | Format | Use case |
|---|---|---|
| `unquantized/best.pt` | PyTorch FP32 | Training / fine-tuning / full accuracy inference |
| `quantized/best.onnx` | ONNX FP32 | Cross-platform CPU/GPU inference |
| `quantized/best.torchscript` | TorchScript FP16 | Jetson Orin / RTX GPU deployment |
| `quantized/best_int8.tflite` | TFLite INT8 | Google Coral Edge TPU / microcontrollers |

## Usage

### PyTorch (unquantized)
```python
from ultralytics import YOLO

model = YOLO("unquantized/best.pt")
results = model("your_sar_image.png")
results[0].show()
```

### ONNX (quantized)
```python
from ultralytics import YOLO

model = YOLO("quantized/best.onnx")
results = model("your_sar_image.png")
results[0].show()
```

### TorchScript FP16 (quantized, RTX / Jetson)
```python
from ultralytics import YOLO

model = YOLO("quantized/best.torchscript")
results = model("your_sar_image.png")
results[0].show()
```

## Training Details

- Optimizer: AdamW
- Epochs: 100 (early stopping, patience=20)
- Augmentation: mosaic, rotation (+-15 degrees), flip -- color aug disabled (SAR is grayscale)
- Quantization: Post-Training Quantization (PTQ) via TorchScript FP16 and TFLite INT8