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Subh775/
Threat-Detection-RFDETR

Name: Threat-Detection-RFDETR
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RF-DETR based Threat Detection Model

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Transformers for Object Detection

The paradigm has shifted! While CNNs traditionally dominated object detection with faster inference times, RF-DETR (Roboflow's Detection Transformer) has revolutionized the field. This transformer-based architecture not only outperforms CNNs in accuracy but also delivers faster inference for real-time applications.

This repository contains a fine-tuned RF-DETR Nano model specifically trained for threat detection, capable of identifying four critical threat categories with high precision and speed.

Predicted Results

Video Inferencing

Model Overview

RF-DETR Threat Detection is a specialized computer vision model designed for security and surveillance applications. Built on Roboflow's cutting-edge RF-DETR architecture, this model can accurately detect and classify potential threats in real-time scenarios.

The threat categories are as:

Class ID	Threat Type	Description
1	Gun	Any type of firearm weapon including pistols, rifles, and other firearms
2	Explosive	Fire, explosion scenarios, and explosive devices
3	Grenade	Hand grenades and similar explosive devices
4	Knife	Bladed weapons including knives, daggers, and sharp objects

Training Dataset

Our custom threat detection dataset was meticulously curated and annotated to ensure robust model performance across diverse scenarios.

Class Distribution

Sample Annotations (Actual)

The model is trained to detect threats across various scales, from small concealed weapons to larger explosive devices.

Performance Metrics

Training Performance

The training process demonstrates excellent convergence with:

Consistent loss reduction over 50 epochs
Stable validation performance indicating good generalization
Balanced precision and recall across all threat categories

Validation Results

Metric	Gun	Explosive	Grenade	Knife	Overall
mAP@50:95	62.3%	47.2%	80.5%	54.4%	61.1%
mAP@50	90.1%	69.6%	93.7%	85.8%	84.8%
Precision	92.4%	54.6%	97.2%	91.1%	83.8%
Recall	85.0%	85.0%	85.0%	85.0%	85.0%

Test Results

Metric	Gun	Explosive	Grenade	Knife	Overall
mAP@50:95	65.3%	35.7%	83.2%	49.8%	58.5%
mAP@50	93.1%	60.5%	91.1%	79.7%	81.1%
Precision	96.7%	49.7%	93.1%	86.5%	81.5%
Recall	83.0%	83.0%	83.0%	83.0%	83.0%

Key Performance Highlights

84.8% mAP@50 on validation set
Fast inference with RF-DETR Nano architecture
Excellent precision for Gun (96.7%) and Grenade (93.1%) detection
Consistent recall of 83-85% across all threat categories
Robust generalization from validation to test performance

Model Architecture

Base Architecture: RF-DETR Nano
Input Resolution: 640×640 pixels
Backbone: Optimized transformer encoder
Detection Head: Custom 4-class threat detection
Inference Speed: ~50ms per image (GPU)
Model Size: Lightweight for edge deployment

Training Details

Training Configuration

Epochs: 50
Batch Size: Optimized for available GPU memory
Optimizer: AdamW with learning rate scheduling
Data Augmentation: Advanced augmentation pipeline for robust training
Loss Function: Multi-scale detection loss with class balancing

Training Strategy

Progressive Training: Started with lower resolution, gradually increased
Class Balancing: Weighted loss to handle class imbalance
Data Augmentation: Extensive augmentation to improve generalization
Early Stopping: Monitored validation mAP to prevent overfitting

Model Files

checkpoint_best_total.pth - Main model weights

Inference Instructions

pip install -q rfdetr==1.2.1 supervision==0.26.1

You can use: video_processing.py to process large videos
Below is the script to process a single image

python

import numpy as np
import supervision as sv
import torch
import requests
from PIL import Image
import os

from rfdetr import RFDETRNano

THREAT_CLASSES = {
    1: "Gun",
    2: "Explosive", 
    3: "Grenade",
    4: "Knife"
}

image = Image.open("Path_to_image")

# pre-trained weights
weights_url = "https://huggingface.co/Subh775/Threat-Detection-RFDETR/resolve/main/checkpoint_best_total.pth"
weights_filename = "checkpoint_best_total.pth"

# Download weights if not already present
if not os.path.exists(weights_filename):
    print(f"Downloading weights from {weights_url}")
    response = requests.get(weights_url, stream=True)
    response.raise_for_status()
    with open(weights_filename, 'wb') as f:
        for chunk in response.iter_content(chunk_size=8192):
            f.write(chunk)
    print("Download complete.")

model = RFDETRNano(resolution=640, pretrain_weights=weights_filename)
model.optimize_for_inference()

detections = model.predict(image, threshold=0.5)

color = sv.ColorPalette.from_hex([
    "#1E90FF", "#32CD32", "#FF0000", "#FF8C00"
])

text_scale = sv.calculate_optimal_text_scale(resolution_wh=image.size)
thickness = sv.calculate_optimal_line_thickness(resolution_wh=image.size)

bbox_annotator = sv.BoxAnnotator(color=color, thickness=thickness)
label_annotator = sv.LabelAnnotator(
    color=color,
    text_color=sv.Color.BLACK,
    text_scale=text_scale,
    smart_position=True
)

labels = []
for class_id, confidence in zip(detections.class_id, detections.confidence):
    class_name = THREAT_CLASSES.get(class_id, f"unknown_class_{class_id}")
    labels.append(f"{class_name} {confidence:.2f}")

annotated_image = image.copy()
annotated_image = bbox_annotator.annotate(annotated_image, detections)
annotated_image = label_annotator.annotate(annotated_image, detections, labels)
annotated_image.thumbnail((800, 800))
annotated_image

Acknowledgments

Roboflow for the RF-DETR architecture
Hugging Face for model hosting and distribution
PyTorch ecosystem for deep learning framework
Supervision library for computer vision utilities

Disclaimer: This model is designed for research purpose only. It's predictions cannot be taken into account for deployment right now.