detection-objets-yolov5
The script performs object detection using the specified YOLOv5 model, removes redundant detections, and displays the results with the option to save images or videos.
Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (8.8%) to scientific vocabulary
Repository
The script performs object detection using the specified YOLOv5 model, removes redundant detections, and displays the results with the option to save images or videos.
Basic Info
- Host: GitHub
- Owner: Yassine-Jegham
- License: agpl-3.0
- Language: Python
- Default Branch: main
- Size: 16.8 MB
Statistics
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Detection-Objets-YOLOv5
This Python script is based on YOLOv5, a real-time object detection model. It supports multiple input sources including images, videos, webcams and screens. The script performs object detection using the specified YOLOv5 model, removes redundant detections, and displays the results with the option to save images or videos. Additionally, it generates a voice message to inform about object detection using gTTS (Google Text-to-Speech) and playsound. Runtime parameters, such as model, confidence thresholds, and input source, can be configured via command line arguments. This code is ideal for real-time object detection applications and can be customized according to specific project needs.
Requirements
- Python 3.x
- PyTorch
- OpenCV
- YOLOv5
- roboflow
- ultralytics
- torch
- Pillow
- tensorboard
References
[ [YOLOv5]]https://github.com/ultralytics/yolov5
[[[ roboflow ]] ]https://roboflow.com/
Resultats
Next Step
*The implementation phase is where the YOLOv5 object detection model becomes a pivotal part of your real-time application, seamlessly processing and analyzing live data streams to swiftly and accurately recognize and locate objects, contributing to the application's core functionality.
Contact Me
If you have any questions or feedback, please contact me at [yjegham@gmail.com]
Owner
- Login: Yassine-Jegham
- Kind: user
- Repositories: 1
- Profile: https://github.com/Yassine-Jegham
Citation (CITATION.cff)
cff-version: 1.2.0
preferred-citation:
type: software
message: If you use YOLOv5, please cite it as below.
authors:
- family-names: Jocher
given-names: Glenn
orcid: "https://orcid.org/0000-0001-5950-6979"
title: "YOLOv5 by Ultralytics"
version: 7.0
doi: 10.5281/zenodo.3908559
date-released: 2020-5-29
license: AGPL-3.0
url: "https://github.com/ultralytics/yolov5"