cnn computer-vision deep-learning deep-learning-tutorial pytorch-implementation vision-transformer

DeepLearningImplementation

Welcome to the DeepLearningImplementation repository! This repository is dedicated to the implementation of various seminal deep learning architectures for computer vision. Whether you are a researcher, student, or practitioner, you'll find comprehensive implementations, training scripts, and documentation for some of the most influential models in the field.

Philosophy

The DeepLearningImplementation repository is built on a philosophy of simplicity and clarity. The primary goal is to offer implementations that prioritize readability and understandability over optimization and performance. This repository is designed to be a learning resource, helping researchers, students, and practitioners gain a deeper understanding of the inner workings of seminal deep learning architectures.

Key Principles:

Simplicity: Each implementation is crafted to be as straightforward as possible. The aim is to minimize complexity, making it easier for users to follow along and grasp the core concepts without being overwhelmed by intricate optimizations or advanced coding techniques.
Readability: The code is written with a strong emphasis on readability. Clear variable names, concise comments, and structured organization are prioritized to ensure that anyone reading the code can easily understand the flow and purpose of each component.
Learning-Oriented: The repository is meant to be a hands-on educational tool. By focusing on the fundamental mechanisms of each architecture, users can learn how these models work at a basic level, facilitating a deeper comprehension that can serve as a foundation for more advanced studies or applications.
Minimal Dependencies: To keep things simple and focused, the project relies solely on PyTorch, one of the most widely used and accessible deep learning frameworks. This decision eliminates the need for additional external libraries, reducing setup complexity and ensuring that users can dive straight into learning.

Project Phases

The DeepLearningImplementation repository is structured into distinct phases to ensure a comprehensive and systematic approach to developing and refining deep learning models. Each phase builds upon the previous one, progressively enhancing the quality and utility of the repository.

First Phase: Implementation and Initial Documentation

The first phase is dedicated to the implementation of various deep learning architectures. During this phase, the primary focus is on writing clear and understandable code for each model. Alongside the implementation, a raw documentation is provided to explain the basic functioning and structure of the models. This phase sets the foundation for further development and ensures that each model is accessible and easy to comprehend.

Current Status: We are currently in phase 1.

Second Phase: Training and Performance Evaluation

In the second phase, the focus shifts to training each implemented model on relevant datasets. This phase involves computing the performance metrics for each model and making comparisons to understand their strengths and weaknesses.

Third Phase: Code Refinement and Documentation Enhancement

The third and final phase involves refining the code implementations. This phase also includes enhancing the documentation to provide more detailed explanations, usage instructions, and best practices. The aim is to polish the repository, making it a robust and reliable resource for learning and experimentation.

Getting Started

Each directory contains the implementation of a specific architecture along with training scripts and detailed documentation. To get started with any architecture, navigate to the respective directory and follow the instructions in the README file.

Installation

Each architecture has its own set of dependencies listed in the requirements.txt file in its directory. You can install the required packages using:

pip install -r requirements.txt

Project Structure

Contributing

Contributions are welcome! Please feel free to submit issues or pull requests to help improve the implementations and documentation.

Contact

For any questions, please open an issue or contact the repository maintainer.

About

This repository contains implementations of prominent computer vision deep learning architectures. The focus is on simplifying these architectures while relying solely on the PyTorch library. The goal is to provide accessible and streamlined versions of key models in the field.

cnn computer-vision deep-learning deep-learning-tutorial pytorch-implementation vision-transformer

MIT License

Languages

Language:Python 100.0%

GuidoManni / DeepLearningImplementation

DeepLearningImplementation

Philosophy

Key Principles:

Contents

Architectures

Semantic Segmentation

Object Detection

Generative Adversarial Networks

Diffusion Generative Models

Autoregressive Generative Networks

3D Reconstruction from 2D Images