Model Card: SIVED YOLOv8m-OBB
Model Details
| Property | Value |
|---|---|
| Architecture | YOLOv8m-OBB (Oriented Bounding Box) |
| Parameters | ~25.9M |
| Base Weights | YOLOv8m-OBB pretrained on DOTAv1 |
| Fine-Tuned On | SIVED (SAR Image Vehicle Detection) |
| Task | Oriented object detection |
| Classes | 1 (Vehicle) |
| Input Size | 640 x 640 px |
| Framework | Ultralytics 8.4.37, PyTorch 2.10.0 |
| File | sived_yolov8m_obb_best.pt (~53 MB) |
Intended Use
Detection of vehicles in Synthetic Aperture Radar (SAR) imagery using oriented bounding boxes. Designed for remote sensing applications including surveillance, defense, disaster response, and urban monitoring.
Training
| Parameter | Value |
|---|---|
| Optimizer | AdamW |
| Learning Rate | 0.001 (cosine decay to 0.01x) |
| Batch Size | 16 |
| Epochs | 100 max (early stop at 70, best at ~50) |
| Patience | 20 |
| Mosaic | 1.0 |
| Copy-Paste | 0.2 |
| Rotation | 15 degrees |
| HSV Hue / Saturation | 0.0 / 0.0 (disabled for grayscale SAR) |
| HSV Value | 0.4 |
| Hardware | Tesla T4 (16GB VRAM), Google Colab |
Performance (Test Set, 103 images)
| Metric | Value |
|---|---|
| [email protected] | 0.988 |
| [email protected]:0.95 | 0.816 |
| Precision | 0.970 |
| Recall | 0.979 |
| F1 (peak) | 0.97 at conf=0.494 |
Baseline Comparison
The pretrained DOTAv1 model scored 0.0 on all metrics when evaluated on SIVED test data. DOTAv1 contains optical aerial categories with no SAR vehicle mapping, confirming that domain-specific fine-tuning is essential.
Limitations
- Trained on a single class (Vehicle) only.
- Optimized for 512 x 512 grayscale SAR imagery. Performance on optical images or different resolutions has not been evaluated.
- The SIVED dataset contains 1,044 images from three radar sources (FARAD, MiniSAR, MSTAR). Generalization to other SAR sensors or bands is not guaranteed.
Citation
If you use this model, please cite the original SIVED dataset:
@Article{rs15112825,
author = {Lin, Xin and Zhang, Bo and Wu, Fan and Wang, Chao and Yang, Yali and Chen, Huiqin},
title = {SIVED: A SAR Image Dataset for Vehicle Detection Based on Rotatable Bounding Box},
journal = {Remote Sensing},
volume = {15},
number = {11},
pages = {2825},
year = {2023},
doi = {10.3390/rs15112825}
}