This is a project dedicated to reproducing or simplifying the code of classic papers in the field of deep learning. The project encompasses various network implementations for learning purposes. The project relies on Python 3.9+ and PyTorch. Through this endeavor, novice researchers can gain a more profound comprehension of the fundamental code structure underlying deep learning models. Furthermore, they can utilize this code to facilitate their own model construction tasks.
The primary objective of this project is to provide a platform for deep learning enthusiasts to learn and engage in discussions. By reproducing the code of classical papers in the field of deep learning, we aim to deepen the understanding and mastery of deep learning models. Our project mainly comprises the following components:
- Reproduction of code from deep learning model papers
- A simplified version of the model.
- Relevant datasets and pre-trained models
- The training and testing scripts of the model prototype.
- Tools, codes, and illustrations commonly employed in academic paper composition.
- example:
We shall continuously update this project in accordance with the latest research papers and trending models in the field of deep learning, thereby providing valuable code and learning resources to all.
Our code repository supports direct downloading and usage, but we recommend using the git clone command to clone the entire code repository to your local machine for better code management and updates. Here are the specific instructions:
1.Open your terminal or command prompt.
2.Navigate to the directory where you want to clone the code repository.
3.Run the following command:
$ git clone https://github.com/Karenina-na/Deep-Learning-Framework.git4.Wait for the cloning process to complete. Once finished, you will have a local copy of the code repository on your machine.
5.After cloning the code repository, you can use the cd command to navigate to the specific folder of the model and then execute the corresponding training or testing script. We provide accompanying documentation in each model folder to assist you in utilizing the model effectively.
In our project, we have utilized a variety of deep learning frameworks and related libraries. Here is our primary technology stack:
- PyTorch
- Numpy
- Pandas
- Matplotlib
We greatly appreciate your willingness to contribute to our project! If you have any valuable models or paper codes to share, or if you have identified any errors or areas for improvement within the codebase, you can submit your contributions through the following methods:
- Contribute to this endeavor,
Forkthe present undertaking. - Establish your distinctive branch of characteristics. (
git checkout -b feature/AmazingFeature) - Submit your modifications forthwith. (
git commit -m 'Add some AmazingFeature') - Propagate your branch to the remote repository with due diligence. (
git push origin feature/AmazingFeature) - Submit a formal pull request for consideration.
We shall diligently review your contribution and incorporate it into our project in a timely manner. Additionally, we extend our heartfelt gratitude for your support and interest in this endeavor!
This undertaking adheres to the MIT License, and for further details, kindly refer to the MIT document.
Intellectual Property and Copyright
Throughout the entire course of project development, I have placed utmost emphasis on adhering to intellectual property and copyright laws. To ensure the legality and reliability of the project, I consistently demonstrate profound respect for the intellectual property of others and have employed the use and citation of code in accordance with pertinent licensing agreements.
In my pursuit of comprehending and resolving issues within the project, I have conducted extensive internet research and perused diverse sources of relevant information. In this endeavor, I have diligently sought out code samples, solutions, and open-source tools, employing them judiciously within the parameters set forth by the applicable licensing agreements.
Reporting Intellectual Property Concerns
Should I have employed your code or any other intellectual property within the project, and you believe that I have encroached upon your rights, I implore you to promptly contact me. I shall honor your wishes, removing the respective code or work, and extend to you my sincerest apologies. I shall undertake appropriate measures to rectify the error and ensure that similar issues are not replicated in the future.
Please understand that in the course of project development, I have exerted every effort to guarantee the legality and compliance of the code and materials. However, owing to the open nature of the internet and the complexity of information, oversights or errors may occasionally arise. If you discover any instances where I have failed to adhere to relevant licensing agreements or infringed upon your rights, kindly provide me with substantiating evidence and detailed information, and I shall expeditiously address the matter.
Should you have any questions or concerns regarding the project, please feel free to contact me via the following methods:
- Email: weizixiang0@outlook.com