📋 Overview

🚀 Model Collection

🎯 Detection Models

• best_0327.engine (131MB)
  • Type: Object Detection (TensorRT Optimized)
  • Purpose: Safety equipment detection (caps, masks, gloves, boots, etc.)
  • Input: RGB images
  • Output: Bounding boxes with class predictions

👤 Human Analysis Models

• yolo11n-seg.engine (10.2MB)
  • Type: Instance Segmentation (TensorRT Optimized)
  • Purpose: Person segmentation for workspace analysis
  • Architecture: YOLOv11 Nano
  • Output: Pixel-level person masks
• yolo11n-pose.engine (10.1MB)
  • Type: Pose Estimation (TensorRT Optimized)
  • Purpose: Human pose detection for safety compliance
  • Architecture: YOLOv11 Nano
  • Output: 17-point keypoint coordinates

🔍 Face Recognition Models

• facedet_fp16.trt (10.3MB)
  • Type: Face Detection (TensorRT FP16)
  • Purpose: Real-time face detection in workplace
  • Optimization: Half-precision for speed
  • Output: Face bounding boxes with landmarks
• adaface_ir101.trt (132MB)
  • Type: Face Recognition (TensorRT Optimized)
  • Purpose: Employee identification and access control
  • Architecture: AdaFace with ResNet-101 backbone
  • Output: 512-dimensional face embeddings

🛠️ Usage

Prerequisites

🏗️ System Requirements

• GPU: NVIDIA GPU with Compute Capability 6.0+
• VRAM: 8GB+ recommended
• CUDA: 11.0+
• TensorRT: 8.0+
• Python: 3.8+

📁 Model Files

models/
├── best_0327.engine          # Main detection model
├── yolo11n-seg.engine        # Person segmentation
├── yolo11n-pose.engine       # Pose estimation
├── facedet_fp16.trt          # Face detection
└── adaface_ir101.trt         # Face recognition

🔧 Integration

1. Face Detection → Face Recognition for employee identification
2. Detection → Safety equipment compliance checking
3. Segmentation → Workspace occupancy analysis
4. Pose Estimation → Safety posture monitoring

🏢 About XIILAB