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object-detection

https://github.com/godfathxx/object-detection

Science Score: 44.0%

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Repository

Basic Info
  • Host: GitHub
  • Owner: Godfathxx
  • Language: Python
  • Default Branch: main
  • Size: 29.8 MB
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  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
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Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme Contributing Citation

README.md

Object-Detection

This repository contains the object detection project for the AI course in your engineering semester. The project utilizes the YOLOv5 model for detecting objects in images, videos, and live streams.

Overview YOLOv5 (You Only Look Once) is a state-of-the-art, real-time object detection model that can detect various objects in images and videos with high accuracy. This project demonstrates the usage of YOLOv5 for object detection on different media sources, including local videos, live camera feeds, and YouTube links.

Setup Prerequisites Make sure you have the following installed:

Python 3.7+ PyTorch OpenCV Other dependencies listed in requirements.txt Installation Clone the repository:

bash Copy code git clone https://github.com/Godfathxx/Object-Detection.git cd object-detection-yolov5 Install dependencies:

bash Copy code pip install -r requirements.txt Download YOLOv5 weights:

Download the pre-trained weights from the official YOLOv5 repository or use custom-trained weights.

Running the Object Detection 1. Detect objects in an image: go Copy code bash python detect.py This command will use the default settings to detect objects in the provided image.

  1. Detect objects in a live camera feed: go Copy code bash python detect.py --source 0 This command will use your computer's default webcam to perform real-time object detection.

  2. Detect objects in a video file: go Copy code bash python detect.py --source vid.mp4 Replace vid.mp4 with the path to your video file. The model will process each frame in the video and display the detected objects.

  3. Detect objects in a YouTube video: go Copy code bash python detect.py --source 'link of yt video' Replace 'link of yt video' with the actual URL of the YouTube video. This command will stream the video and perform object detection in real time.

Results The detected objects will be shown in the output window with bounding boxes and labels. The results are saved in the runs/detect/exp directory by default.

Contributing Feel free to contribute by submitting issues or pull requests. Ensure your code adheres to the project's style guidelines.

License This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments YOLOv5 by Ultralytics PyTorch

Owner

  • Name: Shreyash Joshi
  • Login: Godfathxx
  • Kind: user
  • Location: Ghaziabad,India

👋 Hi, I’m @Godfathxx 👀 I’m interested in Software Development & Cyber Securities 🌱 I’m currently learning Python,HTML,CSS,Java

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"

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Dependencies

utils/docker/Dockerfile docker
  • pytorch/pytorch 2.0.0-cuda11.7-cudnn8-runtime build
utils/google_app_engine/Dockerfile docker
  • gcr.io/google-appengine/python latest build
requirements.txt pypi
  • Pillow >=9.4.0
  • PyYAML >=5.3.1
  • gitpython >=3.1.30
  • matplotlib >=3.3
  • numpy >=1.23.5
  • opencv-python >=4.1.1
  • pandas >=1.1.4
  • psutil *
  • requests >=2.23.0
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • setuptools >=65.5.1
  • thop >=0.1.1
  • torchvision >=0.9.0
  • tqdm >=4.64.0
  • ultralytics >=8.0.232
  • wheel >=0.38.0
utils/google_app_engine/additional_requirements.txt pypi
  • Flask ==2.3.2
  • gunicorn ==19.10.0
  • pip ==23.3
  • werkzeug >=3.0.1