Udemy - Ai Human Intrusion and Object Detection With Yolov7, Python and Cv

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size652.1 MB
  • Uploaded Byfreecoursewb
  • Downloads44
  • Last checkedMay. 30th '25
  • Date uploadedMay. 29th '25
  • Seeders 9
  • Leechers9

Infohash : E22C88A98F6BE87D32C0E7D60C6B29E8C81BC8C1

Ai Human Intrusion & Object Detection With Yolov7, Python&Cv

https://WebToolTip.com

Published 5/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 628.69 MB | Duration: 0h 42m

Intelligent Human Intrusion & Object Detection System using YOLOv7, Python & Computer Vision

What you'll learn
Learn object detection fundamentals and its applications in intrusion detection, surveillance, and real-world domains using AI and computer vision.
Set up a Python environment with essential libraries like Tkinter, OpenCV, and PyTorch for computer vision and object detection tasks.
Understand object detection concepts and how they’re used in monitoring unauthorized intrusions via video streams in real-time scenarios.
Use YOLOv8 and YOLOv7-Tiny models for accurate, real-time object and human intrusion detection using lightweight and efficient algorithms.
Load and configure YOLOv8 and YOLOv7-Tiny pre-trained weights to enable real-time, high-accuracy detection of objects and intruders.
Preprocess video streams and images to integrate smoothly with YOLO models for real-time monitoring and effective object detection.
Write Python scripts to detect objects and intruders, extracting bounding boxes, class labels, and confidence scores for interpretation.
Visualize detection results by drawing bounding boxes, adding labels, and showing confidence scores on video frames for better insight.
Optimize YOLOv7-Tiny for real-time performance on devices with limited resources without compromising detection speed or accuracy.
Tackle challenges like low-light detection, occlusions, motion blur, small or overlapping objects in object and intrusion detection.
Apply AI-based intrusion detection in restricted zones, industries, homes, offices, and public places to improve safety and surveillance.

Requirements
Basic understanding of Python programming (helpful but not mandatory).
A laptop or desktop computer with internet access[Windows OS with Minimum 4GB of RAM).
No prior knowledge of AI or Machine Learning is required—this course is beginner-friendly.
Enthusiasm to learn and build practical projects using AI and IoT tools.

Files:

[ WebToolTip.com ] Udemy - Ai Human Intrusion and Object Detection With Yolov7, Python and Cv
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Introduction of the Object Detection using yolov7
    • 1 - Course Overview and Features.mp4 (12.2 MB)
    10 - Launching VS Code from the Command Line
    • 12 - Using CMD to Open VS Code.mp4 (1.8 MB)
      • Intrusion Detection __pycache__
      • sort.cpython-312.pyc (18.8 KB)
      • classes.txt (0.6 KB)
      • intrusion_detection.py (9.6 KB)
        • intrusion_input1
        • intrusion_input1.mp4 (3.5 MB)
        • point.txt (0.1 KB)
        • sample_img_0.png (656.2 KB)
        intrusion_input2
        • input.png (10.4 MB)
        • intrusion_input2.mp4 (98.5 MB)
        • point.txt (0.1 KB)
      • requirements.txt (0.0 KB)
        • sample_image
        • sample_img_0.png (656.2 KB)
        • sample_img_10.png (660.6 KB)
        • sample_img_100.png (702.3 KB)
        • sample_img_105.png (704.6 KB)
        • sample_img_110.png (704.1 KB)
        • sample_img_115.png (705.0 KB)
        • sample_img_120.png (704.7 KB)
        • sample_img_125.png (705.5 KB)
        • sample_img_130.png (706.4 KB)
        • sample_img_135.png (705.9 KB)
        • sample_img_140.png (706.4 KB)
        • sample_img_145.png (705.2 KB)
        • sample_img_15.png (666.7 KB)
        • sample_img_150.png (708.2 KB)
        • sample_img_155.png (710.6 KB)
        • sample_img_160.png (711.1 KB)
        • sample_img_165.png (711.3 KB)
        • sample_img_20.png (668.9 KB)
        • sample_img_25.png (671.4 KB)
        • sample_img_30.png (673.1 KB)
        • sample_img_35.png (673.7 KB)
        • sample_img_40.png (674.8 KB)
        • sample_img_45.png (677.5 KB)
        • sample_img_5.png (657.9 KB)
        • sample_img_50.png (690.8 KB)
        • sample_img_55.png (692.5 KB)
        • sample_img_60.png (694.5 KB)
        • sample_img_65.png (697.5 KB)
        • sample_img_70.png (697.7 KB)
        • sample_img_75.png (698.1 KB)
        • sample_img_80.png (698.5 KB)
        • sample_img_85.png (699.3 KB)
        • sample_img_90.png (699.4 KB)
        • sample_img_95.png (699.8 KB)
      • save_image.py (0.9 KB)
      • sort.py (11.0 KB)
      • yolov8n.pt (6.2 MB)
        • 11 - Managing Folders and Files of the Project
        • 13 - Understanding Folder and File Structure.mp4 (4.7 MB)
        12 - Understanding and Setting Up Required Packages
        • 14 - Understanding Key Packages for Intrusion Detection System.mp4 (9.4 MB)
        13 - Accessing and Using Polygon Coordinates for Tracking
        • 15 - Polygon Coordinate File Access and Parsing.mp4 (3.4 MB)
        14 - Key Variables and Their Role in YOLOv8
        • 16 - Understanding and Customizing Key Variables in YOLOv8.mp4 (7.4 MB)
        15 - Model Inference for Intrusion Detection
        • 17 - Model Inference Code Explanation for Intrusion Detection.mp4 (88.9 MB)
        16 - Tkinter Implementation for RealTime Intrusion Detection
        • 18 - Tkinter Implementation for RealTime Intrusion Detection.mp4 (20.8 MB)
        17 - Getting Polygon Coordinates Using Roboflow for Intrusion Detection
        • 19 - Getting Polygon Coordinates Using Roboflow.mp4 (28.4 MB)
        18 - Intrusion Detection Code Execution
        • 20 - Intrusion Detection Code Execution.mp4 (39.7 MB)
        19 - Wrapping Up
        • 21 - Course WrapUp.mp4 (4.6 MB)
        2 - Environment Setup for Python Development
        • 2 - Installing Python.mp4 (11.4 MB)
        • 3 - VS Code Setup for Python Development.mp4 (32.6 MB)
        3 - Object Detection Project Overview
        • 4 - Object Detection Project Overview.mp4 (3.5 MB)
          • Object Detection using Yolov7 cfg baseline
          • r50-csp.yaml (1.4 KB)
          • x50-csp.yaml (1.4 KB)
          • yolor-csp-x.yaml (1.6 KB)
          • yolor-csp.yaml (1.6 KB)
          • yolor-d6.yaml (2.0 KB)
          • yolor-e6.yaml (2.0 KB)
          • yolor-p6.yaml (2.0 KB)
          • yolor-w6.yaml (2.0 KB)
          • yolov3-spp.yaml (1.5 KB)
          • yolov3.yaml (1.5 KB)
          • yolov4-csp.yaml (1.6 KB)
          deploy
          • yolov7-d6.yaml (6.0 KB)
          • yolov7-e6.yaml (5.2 KB)
          • yolov7-e6e.yaml (9.3 KB)
          • yolov7-tiny-silu.yaml (3.0 KB)
          • yolov7-tiny.yaml (4.5 KB)
          • yolov7-w6.yaml (4.5 KB)
          • yolov7.yaml (3.9 KB)
          • yolov7x.yaml (4.4 KB)
          training
          • yolov7-d6.yaml (6.1 KB)
          • yolov7-e6.yaml (5.4 KB)
          • yolov7-e6e.yaml (9.4 KB)
          • yolov7-tiny.yaml (4.5 KB)
          • yolov7-w6.yaml (4.7 KB)
          • yolov7.yaml (3.9 KB)
          • yolov7x.yaml (4.4 KB)
          data
          • coco.yaml (1.4 KB)
          • hyp.scratch.custom.yaml (1.5 KB)
          • hyp.scratch.p5.yaml (1.5 KB)
          • hyp.scratch.p6.yaml (1.5 KB)
          • hyp.scratch.tiny.yaml (1.5 KB)
          deploy triton-inference-server
          • README.md (7.1 KB)
          • boundingbox.py (0.9 KB)
          • client.py (14.0 KB)
            • data
            • dog.jpg (159.9 KB)
            • dog_result.jpg (179.9 KB)
              • <

Code:

  • udp://tracker.torrent.eu.org:451/announce
  • udp://tracker.tiny-vps.com:6969/announce
  • http://tracker.foreverpirates.co:80/announce
  • udp://tracker.cyberia.is:6969/announce
  • udp://exodus.desync.com:6969/announce
  • udp://explodie.org:6969/announce
  • udp://tracker.opentrackr.org:1337/announce
  • udp://9.rarbg.to:2780/announce
  • udp://tracker.internetwarriors.net:1337/announce
  • udp://ipv4.tracker.harry.lu:80/announce
  • udp://open.stealth.si:80/announce
  • udp://9.rarbg.to:2900/announce
  • udp://9.rarbg.me:2720/announce
  • udp://opentor.org:2710/announce