Prompt Details
Model
Completion - GPT-3.5 (text-davinci-003)
Token size
725 ($0.01450 / call)
Example input
Project Name: Image Object Recognition
# Image Object Recognition
## About The Project
Image object recognition is a computer vision technology that enables machines to identify objects in digital images and videos. It is a form of artificial intelligence (AI) that can be used to automate tasks such as face recognition, object tracking, and more. This project seeks to create an open source library of algorithms and tools that can be used to develop applications that use image object recognition.
### Built With
* [Python](https://www.python.org/)
* [OpenCV](https://opencv.org/)
* [TensorFlow](https://www.tensorflow.org/)
## Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
### Prerequisites
What things you need to install the software and how to install them
* Python 3.5 or higher
* OpenCV
* TensorFlow
### Installation
1. Clone the repo
```
git clone https://github.com/username/Image-Object-Recognition.git
```
2. Install packages
```
pip install -r requirements.txt
```
## Usage
1. Train the model on your dataset
2. Run the model on new images
3. Evaluate the model on the test dataset
## Roadmap
* Implement algorithms for object detection and tracking
* Create a web interface for real-time object recognition
* Create an API for integrating with other applications
## Contributing
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## License
Distributed under the MIT License. See `LICENSE` for more information.
## Contact
Your Name - [@your_twitter](https://twitter.com/your_username) - email@example.com
@@@@ OUTPUT @@@@
Example output
# Non-Functional Requirements Specification Document
## Usability
[X] Expected level of experience of the dev/ops working with the code in the repository
A basic knowledge of Python and computer vision technologies.
[X] Documentation and tutorials available to developers
Documentation can be found on the project GitHub page and tutorials are available on the project website.
[X] Expected learning curve for using the code in the repository
The expected learning curve is moderate as the code uses popular technologies and libraries.
[X] Distribution of the code in the repository
The code will be distributed on the project's GitHub page and available for download.
## Reliability
[X] Expected uptime of the runtime service (if applicable)
The expected uptime of the runtime service is 100%.
[ ] Specific requirements for data integrity, accuracy, and security
[ ] How should exceptions be handled
[ ] Security requirements for authentication and authorization
## Performance
[X] Expected response times of the executions
The expected response times of the executions are determined by the hardware used and the complexity of the data being processed.
[X] Expected throughputs
The expected throughputs are determined by the hardware used and the complexity of the data being processed.
[ ] Concurrency needs or constraints
[ ] Amount of data processed
The amount of data processed will be determined by the type of task the code is used for.
[ ] Worst acceptable latency
## Supportability
[X] Planned maintenance and support model
The maintenance and support model will be handled on a case-by-case basis.
[X] Expected lifespan of the code in the repository and any long running services
The expected lifespan of the code in the repository is indefinite as it is open source.
[X] Expected level of portability, if any
The code is written in Python and is platform agnostic.
[X] Handling of bugs, feature requests, and change requests
Bugs, feature requests, and change requests should be submitted to the GitHub page.
[X] Who is expected to maintain the code in the repository and the running systems
The code in the repository and the running systems will be maintained by the project maintainers.
[X] Plans to port the code in the repository to other languages/environments
No plans to port the code to other languages/environments at this time.
## Implementation Constraints
[X] Expected memory and storage requirements
The expected memory and storage requirements are dependent on the hardware used and the complexity of the data being processed.
[X] Expected compatibility requirements
The expected compatibility requirements are dependent on the type of hardware and operating system used.
[X] Expected platform requirements
The expected platform requirements are Python 3.5+ and OpenCV.
[ ] Testing constraints
[ ] Maintenance constraints
## Interface
[ ] API requirements
[ ] Input/output requirements
[X] Specific requirements for logging and reporting
Logging and reporting should be done using standard Python logging libraries.
[ ] Constraints imposed by clients using the code in the repository
[ ] Front-end UX requirements (if applicable)
## Operation
[X] Deployment requirements
The deployment requirements are dependent on the type of hardware and operating system used.
[ ] Specific requirements for scalability, redundancy, or high availability
[ ] Specific requirements for backup and restore
## Legal
[X] Licensing of the code in the repository
The code in the repository is licensed under the MIT License.
[ ] Specific legal requirements that need to be met
## CI/CD
[ ] Specific requirements for continuous integration/delivery
[X] Repository a monorepo or a standalone repository
The repository is a standalone repository.
[ ] Expected requirements for automation and testing
The expected requirements for automation and testing are dependent on the type of task the code is used for.
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TEXT-DAVINCI-003
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