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 (13.4%) to scientific vocabulary
Repository
detection of vahines in live
Basic Info
- Host: GitHub
- Owner: ffaahhimm
- License: agpl-3.0
- Language: Python
- Default Branch: main
- Size: 95.7 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Object Detection Project
Welcome to the Object Detection Project repository! This project is focused on implementing and experimenting with various object detection algorithms and models.
📚 Table of Contents
🌟 Introduction
Object detection is a computer vision technique used to identify and locate objects within an image or video. This project aims to provide a comprehensive implementation of popular object detection algorithms, including YOLO, SSD, and Faster R-CNN.
✨ Features
- Implementation of popular object detection models
- Pre-trained model weights for quick setup
- Customizable training scripts
- Evaluation metrics for model performance
- Visualization tools for detected objects
🛠 Installation
To get started with this project, you'll need to clone the repository and install the required dependencies. Follow the steps below:
Clone the repository:
sh git clone https://github.com/ffaahhimm/object-detection.git cd object-detectionInstall the required dependencies:
sh pip install -r requirements.txt
🚀 Usage
To use the object detection models provided in this repository, follow the instructions below:
Run inference on an image:
sh python inference.py --model yolov5 --image path/to/image.jpgTrain a model on a custom dataset:
sh python train.py --model yolov5 --dataset path/to/datasetEvaluate the model performance:
sh python evaluate.py --model yolov5 --dataset path/to/validation_dataset
🤝 Contributing
We welcome contributions to enhance the functionality and performance of this project. To contribute, please follow these steps:
- Fork the repository.
- Create a new branch:
git checkout -b feature-branch. - Make your changes and commit them:
git commit -m 'Add new feature'. - Push to the branch:
git push origin feature-branch. - Submit a pull request.
📄 License
This project is licensed under the MIT License. See the LICENSE file for more details.
Feel free to reach out if you have any questions or suggestions!
Happy coding! 🚀
Owner
- Name: SAEED FAHIM
- Login: ffaahhimm
- Kind: user
- Repositories: 1
- Profile: https://github.com/ffaahhimm
Student@chandigarh university
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"
GitHub Events
Total
- Push event: 5
- Create event: 2
Last Year
- Push event: 5
- Create event: 2
Dependencies
- matplotlib >=3.3.0
- numpy >=1.22.2
- opencv-python >=4.6.0
- pandas >=1.1.4
- pillow >=7.1.2
- psutil *
- py-cpuinfo *
- pyyaml >=5.3.1
- requests >=2.23.0
- scipy >=1.4.1
- seaborn >=0.11.0
- thop >=0.1.1
- torch >=1.8.0
- torchvision >=0.9.0
- tqdm >=4.64.0
- ultralytics >=8.1.47
- PyYAML >=5.3.1
- gitpython >=3.1.30
- matplotlib >=3.3
- numpy >=1.23.5
- opencv-python >=4.1.1
- pandas >=1.1.4
- pillow >=10.3.0
- psutil *
- requests >=2.32.2
- scipy >=1.4.1
- seaborn >=0.11.0
- setuptools >=70.0.0
- thop >=0.1.1
- torchvision >=0.9.0
- tqdm >=4.66.3