Project Title
awesome-object-detection — A Curated List of Resources for Object Detection Research
Overview
The awesome-object-detection project is a comprehensive repository that serves as a go-to resource for researchers and developers interested in the field of object detection. It provides a curated list of articles, surveys, and papers, along with their corresponding codes, covering various object detection models and techniques. This project stands out for its extensive coverage and up-to-date information on the latest advancements in object detection.
Key Features
- Extensive list of object detection models, including R-CNN, Fast R-CNN, Faster R-CNN, and YOLO series.
- Collection of recent surveys and reviews on object detection, providing insights into the latest research trends.
- Links to research papers and their implementations, facilitating easy access to both theoretical and practical aspects of object detection.
Use Cases
- Researchers can use this repository to stay updated with the latest research papers and surveys in object detection.
- Developers can find implementations of various object detection models to integrate into their projects.
- Educators can utilize the resources for teaching purposes, providing students with a comprehensive view of the object detection landscape.
Advantages
- Provides a single point of access to a wide range of resources on object detection.
- Regularly updated to include the latest research and developments in the field.
- Organized in a way that makes it easy for users to find specific information quickly.
Limitations / Considerations
- The project relies on external links for papers and codes, which may become outdated or broken over time.
- The comprehensive nature of the list may be overwhelming for users looking for specific information.
- The project does not provide a unified platform for experimentation or testing of the models listed.
Similar / Related Projects
- Papers with Code: A platform that provides benchmarks and implementations for various machine learning tasks, including object detection. It differs from awesome-object-detection by offering a more interactive and benchmark-focused approach.
- arXiv Sanity Preserver: A tool for filtering and searching through arXiv papers. It is different from awesome-object-detection as it is a search tool rather than a curated list.
- GitHub Awesome Lists: A collection of high-quality, curated lists of resources on various topics, including machine learning. It differs from awesome-object-detection by covering a broader range of topics beyond object detection.
Basic Information
- GitHub: https://github.com/amusi/awesome-object-detection
- Stars: 7,493
- License: Unknown
- Last Commit: 2025-10-10
📊 Project Information
- Project Name: awesome-object-detection
- GitHub URL: https://github.com/amusi/awesome-object-detection
- Programming Language: Unknown
- ⭐ Stars: 7,493
- 🍴 Forks: 1,938
- 📅 Created: 2018-04-06
- 🔄 Last Updated: 2025-10-10
🏷️ Project Topics
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