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yolo-tank-detection
Hunjun/yolo-tank-detection
yolo-tank-detection is a object detection model from Hunjun. Use it when you need objects located in an image. The card lists the license as mit.
Military object detection models trained with Knowledge Distillation and Post-Training Quantization.
Access
Public
Updated Dec 1, 2025
Repo size
67.9 MB
Likes
1
Public
Repo makeup
Click a slice to open those files.
.pt64.5 MB · 95%
At a glance
- Task
- Object Detection
- License
- mit
- Access
- Public
- Created
- Dec 1, 2025
- Updated
- Dec 1, 2025
- SHA
- 2b0f956a
Model card
From the Hugging Face model README
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
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
- Task
- Object Detection
- License
- mit
- Created
- Dec 1, 2025
- Updated
- Dec 1, 2025