Project Title
deep_learning_object_detection — A comprehensive paper list for deep learning-based object detection research
Overview
The deep_learning_object_detection project is a curated list of academic papers focusing on object detection using deep learning techniques. It provides a structured and chronological overview of the field, making it easier for researchers and developers to navigate the rapidly evolving landscape of object detection algorithms. This project stands out for its comprehensiveness and the inclusion of performance metrics, which help in comparing different models.
Key Features
- Chronological list of papers from 2014 to 2019
- Performance table comparing various object detection models
- Diagrams illustrating the history of object detection using deep learning
- Links to official and unofficial implementation codes of the papers
Use Cases
- Researchers in computer vision can use this list to stay updated with the latest advancements in object detection.
- Developers working on object detection models can refer to this list for state-of-the-art algorithms and their performance metrics.
- Educators can utilize this resource as a teaching aid to provide students with a structured understanding of the evolution of object detection techniques.
Advantages
- Provides a single, organized source for a wide range of papers on object detection.
- Includes performance metrics, allowing for easy comparison of different models.
- Offers a historical perspective on the development of object detection techniques.
Limitations / Considerations
- The project is a paper list and does not include code implementations, requiring users to seek out implementations separately.
- The performance metrics are hardware-dependent, making direct comparisons between models challenging without standardized testing conditions.
- The list may not be exhaustive, as it is curated based on the creator's personal opinion and research.
Similar / Related Projects
- mmdetection: An open-source object detection toolbox based on PyTorch, which provides a wide range of models and implementation details, differing from deep_learning_object_detection by offering actual code.
- TensorFlow Object Detection API: A collection of object detection models implemented using TensorFlow, offering a practical application of the理论研究 presented in deep_learning_object_detection.
- Detectron: A research platform by Facebook AI Research, which includes a variety of object detection models, and differs from deep_learning_object_detection by providing a framework for developing and testing new ideas.
Basic Information
- GitHub: https://github.com/hoya012/deep_learning_object_detection
- Stars: 11,414
- License: Unknown
- Last Commit: 2025-09-19
📊 Project Information
- Project Name: deep_learning_object_detection
- GitHub URL: https://github.com/hoya012/deep_learning_object_detection
- Programming Language: Python
- ⭐ Stars: 11,414
- 🍴 Forks: 2,775
- 📅 Created: 2018-09-18
- 🔄 Last Updated: 2025-09-19
🏷️ Project Topics
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