Upload README.md with huggingface_hub

README.md

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+---
+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)