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https://github.com/quantumxiaol/yolov8-small-target-detection
基于yolov8实现小目标检测,在NWPU VHR-10和DOTA上测试
https://github.com/quantumxiaol/yolov8-small-target-detection
Last synced: 2 months ago
JSON representation
基于yolov8实现小目标检测,在NWPU VHR-10和DOTA上测试
- Host: GitHub
- URL: https://github.com/quantumxiaol/yolov8-small-target-detection
- Owner: quantumxiaol
- Created: 2024-05-22T11:34:52.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-05-22T12:35:45.000Z (9 months ago)
- Last Synced: 2024-05-22T12:46:52.416Z (9 months ago)
- Language: Python
- Size: 24.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-yolo-object-detection - quantumxiaol/yolov8-small-target-detection - small-target-detection?style=social"/> : 基于yolov8实现小目标检测,在NWPU VHR-10和DOTA上测试。 (Applications)
- awesome-yolo-object-detection - quantumxiaol/yolov8-small-target-detection - small-target-detection?style=social"/> : 基于yolov8实现小目标检测,在NWPU VHR-10和DOTA上测试。 (Applications)
README
# yolov8-small-target-detection
基于yolov8实现小目标检测,在NWPU VHR-10和DOTA上测试
使用Gradio-YOLOv8-Det进行可视化
yolov8 https://github.com/ultralytics/ultralytics
Gradio-YOLOv8-Det https://gitee.com/CV_Lab/gradio-yolov8-det
可视化需要执行gradio_yolov8_det下的gradio_yolov8_det_v2.py。
根据要求修改/model_config/model_name_all.yaml以添加自己的模型权值
修改/cls_name/cls_name_zh.yaml以修改目标检测的标签值
文件结构
|- yolov8 解压yolov8源代码
|-datasets 储存数据集
|--DOTAs
|--NWPUVHR
|-gradio_yolov8_det 结果可视化
|-yolov8fornwpuvhr.pt 在NWPU VHR-10上训练的一个权值
|-yolov8.yaml 配置网络,修改nc
|-train.py 在NWPU VHR-10训练模型
|-train_DOTAs.py 在DOTA训练模型
|-pred.py 评估模型
github.com/ultralytics/ultralytics
|-docker
|-docs
|-docker
|-examples
|-ultralytics可视化示意

原图

检测结果
在NWPU VHR-10数据集表现

原标签
在DOTA数据集表现

原标签
目前来看在DOTA上表现不好,这可能是由于在DOTA上进行标签转换时没有处理好旋转框,导致模型不能很好的学到较小的目标。
下面是修改了标签计算方法的训练结果。

原标签
Gradio-YOLOv8-Det的原作者 曾逸夫, (2024) Gradio YOLOv8 Det (Version 2.1.0).https://gitee.com/CV_Lab/gradio-yolov8-det.git.