---
license: mit
tags:
- yolo
- object-detection
- knowledge-distillation
- quantization
- military
- tank-detection
datasets:
- roboflow/military-object-detection
metrics:
- mAP
---

# YOLO Tank Detection Models

Military object detection models trained with Knowledge Distillation and Post-Training Quantization.

## Models

| Model | mAP50 | mAP50-95 | Size |
|-------|-------|----------|------|
| Teacher (YOLOv8m) | 86.40% | 62.78% | 49.6 MB |
| Student Fine-tuned (YOLOv8n) | 84.61% | 60.64% | 5.96 MB |
| Student KD (YOLOv8n) | 79.28% | 53.38% | ~6 MB |
| Student Quantized (INT8) | 84.31% | 60.50% | 3.20 MB |

## Usage

```python
from huggingface_hub import hf_hub_download

# Download quantized model for web deployment
model_path = hf_hub_download(
    repo_id="Hunjun/yolo-tank-detection",
    filename="optimized/student_quantized.onnx"
)

# Or download teacher model
teacher_path = hf_hub_download(
    repo_id="Hunjun/yolo-tank-detection",
    filename="teacher/yolov8m_tank_best.pt"
)
```

## Classes

- Airplane
- Helicopter
- Person
- Tank
- Vehicle

## Training

- Teacher: YOLOv8m fine-tuned on Military Dataset (20 epochs)
- Student: YOLOv8n fine-tuned / Knowledge Distillation (20 epochs)
- Quantization: INT8 dynamic quantization via ONNX Runtime

## References

- [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics)
- [Knowledge Distillation (Hinton et al., 2015)](https://arxiv.org/abs/1503.02531)