YoloAll is a collection of yolo all versions. you you use YoloAll to test yolov3/yolov5/yolox/yolo_fastest

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Deep LearningYoloAll
Overview

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YoloAll项目简介

YoloAll是一个将当前主流Yolo版本集成到同一个UI界面下的推理预测工具。可以迅速切换不同的yolo版本，并且可以针对图片，视频，摄像头码流进行实时推理，可以很方便，直观的对比不同版本的yolo的推理效果，耗时等。
自从YoloAll V1版本推出以后，得到了很多朋友的喜欢，大家也跟我一样，被众多的Yolo版本所困扰，有时为了比较两个不同版本的Yolo的效果，往往需要花费很长时间搭建环境，并且很容易出错，因此才有了这个开发这个YoloAll的想法，能够非常方便的测试不同Yolo的效果！注意这个是测试推理效果的哦，不能进行训练，如果想要非常方便的训练，推荐另外一个可视化的YoloX训练项目：YoloX EasyTrain：https://github.com/DL-Practise/YoloX_EasyTrain![image](https://user-images.githubusercontent.com/16218143/143548303-d7927b0d-edf5-4423-850a-6d466753d6eb.png)

YoloAll V2.0新特性

更全、更美、更强、更易用
  • 增加了YoloV4，更新YoloV5到6.0版本，更新YoloX到0.1.1版本
  • 重新设计了界面，美观度提升，使用起来更加舒畅
  • 增加了模型推理时候的参数配置
  • 增加了模型自动下载功能，方便下载与训练模型；
  • 集成了使用手册，方便查阅；
  • 将所有模型的依赖放到根目录，安装依赖更加方便

YoloAll安装

操作系统：Win10、Win7、Ubuntu16.04（其他操作系统没有测试过）
python环境：python3.7
cuda环境：cuda10.1（也可以不用cuda，使用cpu推理）

下载YoloAll源码：

https://github.com/DL-Practise/YoloAll

解压YoloAll，进入到根目录

cd /path/to/yoloall

升级pip

python -m pip install --upgrade pip

安装依赖项

pip install -r requirements.txt

YoloAll使用

启动YoloAll

python main_widget.py

界面简介

主要包括模型管理界面，配置界面，预测界面、消息界面和日志界面组成。 模型管理界面会将所有的模型以及子模型（例如YoloX包含s,m,l等子模型） 配置界面用于配置一些预测参数，例如置信度阈值，nms阈值，图片大小等 预测界面用于展现预测结果 消息界面展现YoloAll的升级信息，使用教程等 日志界面：展示一些关键的日志信息 image

模型加载

刚启动YoloAll的时候，软件会去加载所有的模型，包括所有的子模型，因此需要等待一段时间，此时在日志界面会有相关的提示信息，需要等到日志界面出现“加载模型结束”。此时模型管理界面会出现所有加载的模型，表示模型的加载已经结束，可以进行测试了。 image

下载预训练模型

由于预训练模型通常都很大，因此，下载的YoloAll中是不包含预训练模型的，需要通过鼠标点击模型管理界面中的子模型，例如点击YoloX下面的yolox_l模型，会弹出提示框，提示预训练模型未下载，然后会在提示框中展示预训练模型的下载链接（有可能有多个下载链接），选择一个网速不错的链接，从浏览器下载，下载完毕之后，根据提示框中的提示信息，放到对应的文件夹下面。 image

预测

下载完预训练模型，并放到指定文件夹后，再次点击模型管理界面的模型，就会开始创建模型，并显示创建模型成功的界面
image
此时可以点击预测界面的Photo按钮，选择一张图片进行预测，也可以点击Video按键，选择一段视频文件进行预测，也可以点击Camera按钮，启动摄像头进行拍摄和预测。

修改预测参数

在预测过程中，如果需要进行CPU、GPU的切换，或者修改预测参数，例如修改图像分辨率，置信度阈值，nms阈值等，可以在配置界面进行修改的勾选或者修改，修改完后，点击保存按钮，软件会重新创建模型，等模型创建成功后，可以继续使用新的配置进行预测。
image

预测信息

预测成功后，会在预测界面展示预测的信息，图中的方框即为预测出来的目标，红色的文字是预测的速度和FPS。大家可以切换不同的模型，进行预测结果以及耗时的对比。
image

YoloAll演示视频(V2.0)

后续补充。。。

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Comments
  • python main_widget.py qt.qpa.plugin Qt platform plugin

    python main_widget.py qt.qpa.plugin Qt platform plugin "xcb"

    我的是Ubuntu16.04系统，已安装pip install -r requirements.txt

    运行python main_widget.py时，出现报错如下：`$ python main_widget.py QObject::moveToThread: Current thread (0x564cc7b698e0) is not the object's thread (0x564cc7aba3c0). Cannot move to target thread (0x564cc7b698e0)

    qt.qpa.plugin: Could not load the Qt platform plugin "xcb" in "/media/wl/work/anaconda3/envs/yoloall/lib/python3.7/site-packages/cv2/qt/plugins" even though it was found. This application failed to start because no Qt platform plugin could be initialized. Reinstalling the application may fix this problem.

    Available platform plugins are: xcb, eglfs, linuxfb, minimal, minimalegl, offscreen, vnc, wayland-egl, wayland, wayland-xcomposite-egl, wayland-xcomposite-glx, webgl.

    Aborted (core dumped)`

    问题怎么解决呢？谢谢

    opened by Jerry-muyangren 1
  • yolofastestv2 Pytorch to onnx

    yolofastestv2 Pytorch to onnx

    Hi there, I am having trouble with converting yolofastestv2 model from pytorch to onnx https://github.com/hpc203/yolo-fastestv2-opencv/issues/1#issuecomment-976087012 Any helps will be appreciated!!

    opened by danielzhangau 1
Releases(V2.0.1)

  • V2.0.1(Dec 23, 2021)

    YoloAll V2.0.1 发布 1.增加了YoloV4，更新YoloV5到6.0版本，更新YoloX到0.1.1版本 2.重新设计了界面，美观度提升，使用起来更加舒畅 3.增加了模型推理时候的参数配置 4.增加了模型自动下载功能，方便下载与训练模型 5.集成了使用手册，方便查阅 6.将所有模型的依赖放到根目录，安装依赖更加方便 7.增加了模型保存功能

    Source code(tar.gz)
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DL-Practise
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