| 1 - Course Overview and Features.mp4 | 12.2 MB | ||
| 10 - Installing Python.mp4 | 9.4 MB | ||
| 11 - VS Code Setup for Python Development.mp4 | 32.7 MB | ||
| 12 - Using CMD to Open VS Code.mp4 | 1.8 MB | ||
| 13 - Understanding Folder and File Structure.mp4 | 4.7 MB | ||
| 14 - Understanding Key Packages for Intrusion Detection System.mp4 | 9.4 MB | ||
| 15 - Polygon Coordinate File Access and Parsing.mp4 | 3.4 MB | ||
| 16 - Understanding and Customizing Key Variables in YOLOv8.mp4 | 7.4 MB | ||
| 17 - Model Inference Code Explanation for Intrusion Detection.mp4 | 88.9 MB | ||
| 18 - Tkinter Implementation for RealTime Intrusion Detection.mp4 | 20.8 MB | ||
| 19 - Getting Polygon Coordinates Using Roboflow.mp4 | 28.4 MB | ||
| 2 - Installing Python.mp4 | 11.4 MB | ||
| 20 - Intrusion Detection Code Execution.mp4 | 39.7 MB | ||
| 21 - Course WrapUp.mp4 | 4.6 MB | ||
| 3 - VS Code Setup for Python Development.mp4 | 32.6 MB | ||
| 4 - Object Detection Project Overview.mp4 | 3.5 MB | ||
| 5 - Understanding Key Packages for Object Detection.mp4 | 7.3 MB | ||
| 6 - Detailed Explanation of the YOLOv7tinypt File.mp4 | 3.8 MB | ||
| 7 - Implementing YOLOv7tiny for Live Video Analysis.mp4 | 24.7 MB | ||
| 8 - Integrating YOLOv7tiny with a Tkinter GUI.mp4 | 16.4 MB | ||
| 9 - Performing RealTime Object Detection with YOLOv7tiny.mp4 | 31.3 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Dockerfile | 819.2 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| README.md | 7.1 KB | ||
| YOLOv7-Dynamic-Batch-ONNXRUNTIME.ipynb | 5.7 MB | ||
| YOLOv7-Dynamic-Batch-TENSORRT.ipynb | 12 MB | ||
| YOLOv7CoreML.ipynb | 873.2 KB | ||
| YOLOv7onnx.ipynb | 1.5 MB | ||
| YOLOv7trt.ipynb | 1.7 MB | ||
| __init__.cpython-312.pyc | 204.8 B | ||
| __init__.cpython-37.pyc | 102.4 B | ||
| __init__.py | 0 B | ||
| activations.py | 2.2 KB | ||
| add_nms.py | 5.5 KB | ||
| additional_requirements.txt | 102.4 B | ||
| app.yaml | 204.8 B | ||
| autoanchor.cpython-312.pyc | 11.1 KB | ||
| autoanchor.cpython-37.pyc | 6.1 KB | ||
| autoanchor.py | 7 KB | ||
| boundingbox.py | 921.6 B | ||
| bus.jpg?042148 | 476 KB | ||
| classes.txt | 614.4 B | ||
| client.py | 14 KB | ||
| coco.yaml | 1.4 KB | ||
| common.cpython-312.pyc | 125.9 KB | ||
| common.cpython-37.pyc | 75.1 KB | ||
| common.py | 82.4 KB | ||
| compare_YOLOv7_vs_YOLOv5m6.ipynb | 3.7 MB | ||
| compare_YOLOv7_vs_YOLOv5m6_half.ipynb | 3.8 MB | ||
| compare_YOLOv7_vs_YOLOv5s6.ipynb | 3.7 MB | ||
| compare_YOLOv7e6_vs_YOLOv5x6.ipynb | 3.7 MB | ||
| compare_YOLOv7e6_vs_YOLOv5x6_half.ipynb | 3.7 MB | ||
| datasets.cpython-312.pyc | 76.2 KB | ||
| datasets.cpython-37.pyc | 40.3 KB | ||
| datasets.py | 54.9 KB | ||
| dog.jpg?042148 | 159.9 KB | ||
| dog_result.jpg?042148 | 179.9 KB | ||
| experimental.cpython-312.pyc | 16 KB | ||
| experimental.cpython-37.pyc | 9.7 KB | ||
| experimental.py | 10.6 KB | ||
| general.cpython-312.pyc | 47.8 KB | ||
| general.cpython-37.pyc | 27.4 KB | ||
| general.py | 36 KB | ||
| get_coco.sh | 819.2 B | ||
| gitignore | 4 KB | ||
| google_utils.cpython-312.pyc | 5.7 KB | ||
| google_utils.cpython-37.pyc | 3.2 KB | ||
| google_utils.py | 4.8 KB | ||
| horses.jpg?042148 | 130.4 KB | ||
| horses_prediction.jpg?042148 | 151.4 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 | ||
| image1.jpg?042148 | 78.8 KB | ||
| image2.jpg?042148 | 140.3 KB | ||
| image3.jpg?042148 | 114.9 KB | ||
| inference.py | 5.4 KB | ||
| inference_tkinter.py | 8.7 KB | ||
| inpu_video1.mp4 | 28.3 MB | ||
| input.png | 10.4 MB | ||
| instance.ipynb | 476.9 KB | ||
| intrusion_detection.py | 9.6 KB | ||
| intrusion_input1.mp4 | 3.5 MB | ||
| intrusion_input2.mp4 | 98.5 MB | ||
| keypoint.ipynb | 465.2 KB | ||
| labels.py | 1.3 KB | ||
| log_dataset.py | 819.2 B | ||
| loss.cpython-312.pyc | 81.7 KB | ||
| loss.cpython-37.pyc | 40.8 KB | ||
| loss.py | 73.3 KB | ||
| mask.png | 101.6 KB | ||
| metrics.cpython-312.pyc | 13.5 KB | ||
| metrics.cpython-37.pyc | 7.6 KB | ||
| metrics.py | 9.1 KB | ||
| mime.sh | 819.2 B | ||
| performance.png | 164.5 KB | ||
| plots.cpython-312.pyc | 32.9 KB | ||
| plots.cpython-37.pyc | 17.6 KB | ||
| plots.py | 20.4 KB | ||
| point.txt | 102.4 B | ||
| pose.png | 347.1 KB | ||
| processing.py | 2.1 KB | ||
| r50-csp.yaml | 1.4 KB | ||
| render.py | 3.1 KB | ||
| reparameterization.ipynb | 30.8 KB | ||
| requirements.txt | 102.4 B | ||
| resume.py | 1.1 KB | ||
| 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 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 | 921.6 B | ||
| sort.cpython-312.pyc | 18.8 KB | ||
| sort.py | 11 KB | ||
| tennis.jpg?042148 | 6.8 KB | ||
| tennis_caption.png | 18.6 KB | ||
| tennis_panoptic.png | 8.1 KB | ||
| tennis_semantic.jpg?042148 | 4.4 KB | ||
| torch_utils.cpython-312.pyc | 23.9 KB | ||
| torch_utils.cpython-37.pyc | 13 KB | ||
| torch_utils.py | 15.1 KB | ||
| traced_model.pt | 24 MB | ||
| userdata.sh | 1.2 KB | ||
| visualization.ipynb | 482.3 KB | ||
| wandb_utils.py | 15.9 KB | ||
| x50-csp.yaml | 1.4 KB | ||
| yolo.cpython-312.pyc | 62.1 KB | ||
| yolo.cpython-37.pyc | 29.1 KB | ||
| yolo.py | 39.1 KB | ||
| yolor-csp-x.yaml | 1.6 KB | ||
| yolor-csp.yaml | 1.6 KB | ||
| yolor-d6.yaml | 2 KB | ||
| yolor-e6.yaml | 2 KB | ||
| yolor-p6.yaml | 2 KB | ||
| yolor-w6.yaml | 2 KB | ||
| yolov3-spp.yaml | 1.5 KB | ||
| yolov3.yaml | 1.5 KB | ||
| yolov4-csp.yaml | 1.6 KB | ||
| yolov7-d6.yaml | 6.1 KB | ||
| yolov7-e6.yaml | 5.4 KB | ||
| yolov7-e6e.yaml | 9.4 KB | ||
| yolov7-tiny-silu.yaml | 3 KB | ||
| yolov7-tiny.pt | 12.1 MB | ||
| yolov7-tiny.yaml | 4.5 KB | ||
| yolov7-w6.yaml | 4.7 KB | ||
| yolov7.pdf | 5.8 MB | ||
| yolov7.yaml | 3.9 KB | ||
| yolov7x.yaml | 4.4 KB | ||
| yolov8n.pt | 6.2 MB | ||
| zidane.jpg?042148 | 165 KB | ||
| ▲ 197 total files | |||
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.
| torrent name | size | uploader | age | seed | leech |
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| 3.5 GB | freecoursewb | 3 days | 71 | 18 | |
| 1.1 GB | freecoursewb | 3 days | 40 | 6 | |
| 483.5 MB | freecoursewb | 1 week | 15 | 3 | |
| 2.8 GB | freecoursewb | 1 week | 46 | 16 | |
| 2.3 GB | freecoursewb | 1 week | 1 | 2 |
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