YOLOv8m Defence

Model Overview

YOLOv8m Defence is a specialized object detection model fine-tuned from the ultralytics/YOLOv8 checkpoint for defence and transportation applications. The model was trained for over 50 epochs on a private defence-related dataset to detect 18 categories of aircraft, vehicles, and ships with high accuracy and low latency requirements.

A deployment of the model is available at Hugging Face Spaces for demonstration of its capabilities.

Model Details

Model Type: Object Detection
Base Architecture: YOLOv8m
Framework: PyTorch
Training Epochs: 50+
Number of Classes: 18
Input: RGB Images
Output: Bounding boxes with class predictions and confidence scores

Supported Classes

The model detects the following 18 object categories:

Class ID	Object Type
0	Cargo Aircraft
1	Commercial Aircraft
2	Drone
3	Fighter Jet
4	Fighter Plane
5	Helicopter
6	Light Aircraft
7	Missile
8	Truck
9	Car
10	Tank
11	Bus
12	Van
13	Cargo Ship
14	Yacht
15	Cruise Ship
16	Warship
17	Sailboat

Performance

The model achieved the following performance metrics in its original evaluation environment:

[email protected]:0.05:0.95: 0.598
Speed Score: 0.933

Note: These scores were achieved with the fully optimized TensorRT version. The PyTorch model provided here may have different performance characteristics.

Usage

Installation

pip install ultralytics

Quick Start

from ultralytics import YOLO
from PIL import Image

# Load the model
model = YOLO('path/to/yolov8m_defence.pt')

# Run inference
results = model('path/to/your/image.jpg')

# Process results
for r in results:
    print(f"Detected {len(r.boxes)} objects")
    
    for box in r.boxes:
        # Get bounding box coordinates
        x1, y1, x2, y2 = box.xyxy[0]
        bbox = [int(x1), int(y1), int(x2 - x1), int(y2 - y1)]
        
        # Get class and confidence
        class_id = int(box.cls[0])
        confidence = float(box.conf[0])
        class_name = model.names[class_id]
        
        print(f"  {class_name} (ID: {class_id}): {confidence:.2f} at {bbox}")

Batch Inference

# Run inference on multiple images
results = model(['image1.jpg', 'image2.jpg', 'image3.jpg'])

for i, r in enumerate(results):
    print(f"Image {i+1}: {len(r.boxes)} detections")

Visualization

# Display results with bounding boxes
results = model('image.jpg')
annotated_image = results[0].plot()

# Convert BGR to RGB for display
from PIL import Image
import numpy as np
Image.fromarray(annotated_image[..., ::-1]).show()

Training Details

Dataset

Source: Private defence dataset (proprietary)
Classes: 18 object categories
Augmentations: Mosaic, flips, color adjustments
Annotations: Bounding box format

Training Configuration

Base Model: ultralytics/yolov8m
Training Epochs: 50+
Framework: Ultralytics YOLO
Optimization: Standard YOLOv8 training pipeline

Post-Training Optimization

The original model underwent additional optimization including:

Model pruning
Quantization
TensorRT conversion (for deployment)

This repository contains the pre-optimization PyTorch model for maximum compatibility and ease of use.

Intended Use Cases

Primary Applications

Military and defence object detection
Transportation vehicle monitoring
Surveillance and reconnaissance
Aerial and maritime asset identification

Suitable For

Direct inference on defence-related imagery
Transfer learning for similar detection tasks
Baseline model for military/civilian vehicle detection
Research and development in computer vision

Limitations

Scope: Only detects the 18 trained object categories
Domain: Performance may vary on images significantly different from training data
Speed: This PyTorch version is slower than the optimized TensorRT variant
Hardware: No specific GPU requirements, but GPU acceleration recommended

Ethical Considerations

This model is designed for defence and security applications. Users should:

Ensure compliance with local laws and regulations
Consider privacy implications when processing imagery
Use responsibly and ethically in surveillance applications
Respect international laws regarding military technology

Citation

@misc{yolov8_ultralytics,
  author = {Jocher, Glenn and Chaurasia, Ayush and Qiu, Jing},
  title = {YOLO by Ultralytics},
  year = {2023},
  howpublished = {\url{https://github.com/ultralytics/ultralytics}},
}

License

This model is released under the Apache 2.0 License.

Model Card Contact

For questions about this model card or the model itself, please refer to the repository issues or contact the model authors.

spencercdz
/

YOLOv8m_defence