Subh775/Firearm_Detection_Yolov8n

Firearm Detection YOLOv8n

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A high-performance YOLOv8-nano model specifically trained for firearm detection in images and videos. This model achieves exceptional accuracy with 89.0% [email protected] and is optimized for real-time inference applications in security and surveillance systems.

Model Overview

This model is trained on a comprehensive firearm detection (A custom dataset) containing over 7k high-quality images. The model can detect various types of firearms including pistols, rifles, shotguns, and other weapon types unified under a single "Gun" class for detection of fire-armed weapons

Key Features:

• Single-class detection (Gun) for simplified integration.
• High accuracy: 89.0% [email protected], 60.2% [email protected].
• Optimized for real-time inference.
• Comprehensive training on diverse firearm types.
• Robust performance across different lighting and background conditions.

Sample Demo

Here is an example output by the model:

Performance Metrics

The model demonstrates exceptional performance on the validation dataset after 100 epochs of training:

Metric	Value
[email protected]	0.890
[email protected]	0.602
Precision	0.864
Recall	0.824
F1-Score	0.84

Training Results

Training and Validation Curves

The training progression over 100 epochs shows consistent improvement across all metrics:

The graphs demonstrate:

• Loss Reduction: Steady decrease in box_loss, cls_loss, and dfl_loss
• Metric Improvement: Consistent increase in precision, recall, mAP50, and mAP50-95
• Convergence: Stable performance indicating optimal training completion

Dataset Distribution

The dataset contains 6,800 gun instances across 7,068 training images, with balanced spatial distribution and varied object sizes for robust detection capabilities.

Model Performance Analysis

Confusion Matrix

Absolute Counts:

Normalized Values:

The confusion matrices show:

• True Positives: 1,537 correctly identified guns (86% accuracy)
• False Negatives: 249 missed detections (14% miss rate)
• False Positives: 324 background misclassifications
• Strong Performance: High precision with minimal false positive rate

Performance Curves

Precision-Recall Curve	Precision-Confidence Curve
Recall-Confidence Curve	F1-Confidence Curve

These curves demonstrate optimal performance at confidence threshold 0.4, balancing precision and recall for practical deployment.

Dataset Information

Training Dataset Composition:

• Total Images: 7,068 (after quality filtering)
• Training Set: 5,642 images
• Validation Set: 1,426 images
• Total Instances: 6,800 gun annotations
• Split Ratio: 80/20 train/validation

Source Datasets:

• Roboflow: 3,822 images (rifles, shotguns, heavy weapons)
• Kaggle: 1,946 images (mixed firearms including pistols)
• Additional curated sources: 1,444 images

Quality Assurance:

• Duplicate removal: 711 images filtered
• Low-quality filtering: 434 images removed
• Manual verification and annotation correction

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Inferencing instructions

pip install ultralytics huggingface_hub

Basic Inference

from ultralytics import YOLO
from huggingface_hub import hf_hub_download

# Download model from Hugging Face Hub
model_path = hf_hub_download(
    repo_id="Subh775/Firearm_Detection_Yolov8n",
    filename="weights/best.pt"
)

# Load model
model = YOLO(model_path)

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

# Display results
for box in results[0].boxes:
    class_name = model.names[int(box.cls[0])]
    confidence = box.conf[0]
    print(f"Detected: {class_name} (Confidence: {confidence:.3f})")

# Show annotated image
results[0].show()

Batch Processing for Videos

import cv2
from ultralytics import YOLO
from huggingface_hub import hf_hub_download
import torch
from tqdm import tqdm

# Configuration
MODEL_REPO = "Subh775/Firearm_Detection_Yolov8n"
INPUT_VIDEO = "input_video.mp4"
OUTPUT_VIDEO = "output_video.mp4"
CONFIDENCE_THRESHOLD = 0.4
BATCH_SIZE = 32  # Adjust based on GPU memory

# Setup device
device = 0 if torch.cuda.is_available() else "cpu"
print(f"Using device: {'GPU' if device == 0 else 'CPU'}")

# Load model
model_path = hf_hub_download(repo_id=MODEL_REPO, filename="weights/best.pt")
model = YOLO(model_path)

# Process video
cap = cv2.VideoCapture(INPUT_VIDEO)
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))

fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(OUTPUT_VIDEO, fourcc, fps, (frame_width, frame_height))

frames_batch = []
with tqdm(total=total_frames, desc="Processing video") as pbar:
    while cap.isOpened():
        success, frame = cap.read()
        if success:
            frames_batch.append(frame)
            
            if len(frames_batch) == BATCH_SIZE:
                # Batch inference
                results = model(frames_batch, conf=CONFIDENCE_THRESHOLD, 
                              device=device, verbose=False)
                
                # Write annotated frames
                for result in results:
                    annotated_frame = result.plot()
                    out.write(annotated_frame)
                
                pbar.update(len(frames_batch))
                frames_batch = []
        else:
            break

# Process remaining frames
if frames_batch:
    results = model(frames_batch, conf=CONFIDENCE_THRESHOLD, 
                   device=device, verbose=False)
    for result in results:
        annotated_frame = result.plot()
        out.write(annotated_frame)
    pbar.update(len(frames_batch))

cap.release()
out.release()
print(f"Processed video saved to: {OUTPUT_VIDEO}")

Model Architecture

• Base Model: YOLOv8-nano (yolov8n.pt)
• Input Size: 640x640 pixels
• Classes: 1 (Gun)
• Parameters: ~3.2M
• Model Size: ~6.2MB
• Inference Speed: ~4ms per image (GPU)

Training Configuration

• Epochs: 100
• Batch Size: 16
• Image Size: 640x640
• Optimizer: AdamW
• Learning Rate: 0.01 (initial)

Limitations and Considerations

Technical Limitations:

• Performance may vary with image quality, lighting, and occlusion
• Optimized for common firearm types; may require retraining for specialized weapons
• False positives possible with objects resembling firearms (toys, tools)

Recommended Usage:

• Use appropriate confidence thresholds based on application requirements
• Implement human review processes for critical decisions
• Regular model evaluation and updates for optimal performance
• Proper training for operators using the system

License

This model is released under the Apache 2.0 License. See the LICENSE file for details.

Disclaimer: This model is provided for research purposes only. The predictions can not be used to solve real world problems.