MMYOLO Call for Proposals

在各个领域中 YOLO 系列改进算法众多，哪些改进的算法或者模块你觉得非常有用？欢迎在 Issue 中留言。我们会根据反馈列入开发计划当中。

Issue 回复模板

• 算法名：
• 发布的会议和年份（如有）：
• 论文（如有）：
• 数据集（如有）：
• 简介（如有）：
• 代码（如有）：
• 相关解读（如有）：

示例

• 算法名：TPH-YOLOv5
• 发布的会议和年份：ICCVW2021
• 论文：https://openaccess.thecvf.com/content/ICCV2021W/VisDrone/html/Zhu_TPH-YOLOv5_Improved_YOLOv5_Based_on_Transformer_Prediction_Head_for_Object_ICCVW_2021_paper.html
• 数据集：VisDrone2021
• 简介：YOLOv5 基础上增加 Transformer Prediction Heads (TPH) 和 convolutional block attention model (CBAM)，在 VisDrone2021 数据集上比 baseline YOLOv5 模型，精度提高 7%
• 代码：https://github.com/cv516Buaa/tph-yolov5
• 相关解读：https://zhuanlan.zhihu.com/p/416171744

There are lots of improved algorithms in the YOLO series in various fields. Which improved algorithms or modules do you find useful? Feel free to leave your comments in the Issue. We will include them in the development plan based on the feedback.

Issue Reply Template

• Algorithm Name:
• Conference/Year(Optional):
• Paper(Optional):
• Datasets(Optional):
• Introduction(Optional):
• Repo(Optional):
• Interpretations(Optional):

Example

• Algorithm Name: TPH-YOLOv5
• Conference/Year: ICCVW2021
• Paper: https://openaccess.thecvf.com/content/ICCV2021W/VisDrone/html/Zhu_TPH-YOLOv5_Improved_YOLOv5_Based_on_Transformer_Prediction_Head_for_Object_ICCVW_2021_paper.html
• Datasets: VisDrone2021
• Introduction: Add Transformer Prediction Heads (TPH) and convolutional block attention model (CBAM) in YOLOv5, which improves the accuracy by 7% over the baseline YOLOv5 model on the VisDrone2021 dataset.
• Repo: https://github.com/cv516Buaa/tph-yolov5
• Interpretations: https://zhuanlan.zhihu.com/p/416171744

根据大家反馈，我们会在下面维护一个列表，方便统一查看。
According to your feedback, we will maintain a list below.