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

Related tags

Deep LearningYoloAll
Overview

官方讨论群

QQ群:552703875
微信群:15158106211(先加作者微信,再邀请入群)

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。。。

You might also like...
TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured Scenarios
TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured Scenarios

TPH-YOLOv5 This repo is the implementation of "TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured

Multi-task yolov5 with detection and segmentation based on yolov5
Multi-task yolov5 with detection and segmentation based on yolov5

YOLOv5DS Multi-task yolov5 with detection and segmentation based on yolov5(branch v6.0) decoupled head anchor free segmentation head README中文 Ablation

Yolov5-lite - Minimal PyTorch implementation of YOLOv5
Yolov5-lite - Minimal PyTorch implementation of YOLOv5

Yolov5-Lite: Minimal YOLOv5 + Deep Sort Overview This repo is a shortened versio

yolox_backbone is a deep-learning library and is a collection of YOLOX Backbone models.
yolox_backbone is a deep-learning library and is a collection of YOLOX Backbone models.

YOLOX-Backbone yolox-backbone is a deep-learning library and is a collection of YOLOX backbone models. Install pip install yolox-backbone Load a Pret

YOLTv4 builds upon YOLT and SIMRDWN, and updates these frameworks to use the most performant version of YOLO, YOLOv4
YOLTv4 builds upon YOLT and SIMRDWN, and updates these frameworks to use the most performant version of YOLO, YOLOv4

YOLTv4 builds upon YOLT and SIMRDWN, and updates these frameworks to use the most performant version of YOLO, YOLOv4. YOLTv4 is designed to detect objects in aerial or satellite imagery in arbitrarily large images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks.

Minimal PyTorch implementation of YOLOv3
Minimal PyTorch implementation of YOLOv3

A minimal PyTorch implementation of YOLOv3, with support for training, inference and evaluation.

YOLOv3 in PyTorch > ONNX > CoreML > TFLite
YOLOv3 in PyTorch ONNX CoreML TFLite

This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices

Train a state-of-the-art yolov3 object detector from scratch!
Train a state-of-the-art yolov3 object detector from scratch!

TrainYourOwnYOLO: Building a Custom Object Detector from Scratch This repo let's you train a custom image detector using the state-of-the-art YOLOv3 c

Yolov3 pytorch implementation
Yolov3 pytorch implementation

YOLOV3 Pytorch实现 在bubbliiing大佬代码的基础上进行了修改,添加了部分注释。 预训练模型 预训练模型来源于bubbliiing。 链接:https://pan.baidu.com/s/1ncREw6Na9ycZptdxiVMApw 提取码:appk 训练自己的数据集 按照VO

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)
    Source code(zip)
Owner
DL-Practise
cv engineer; wechat 15158106211
DL-Practise
Project Aquarium is a SUSE-sponsored open source project aiming at becoming an easy to use, rock solid storage appliance based on Ceph.

Project Aquarium Project Aquarium is a SUSE-sponsored open source project aiming at becoming an easy to use, rock solid storage appliance based on Cep

Aquarist Labs 73 Jul 21, 2022
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

DeepSpeed+Megatron trained the world's most powerful language model: MT-530B DeepSpeed is hiring, come join us! DeepSpeed is a deep learning optimizat

Microsoft 8.4k Dec 28, 2022
Official PyTorch repo for JoJoGAN: One Shot Face Stylization

JoJoGAN: One Shot Face Stylization This is the PyTorch implementation of JoJoGAN: One Shot Face Stylization. Abstract: While there have been recent ad

1.3k Dec 29, 2022
Low Complexity Channel estimation with Neural Network Solutions

Interpolation-ResNet Invited paper for WSA 2021, called 'Low Complexity Channel estimation with Neural Network Solutions'. Low complexity residual con

Dianxin 10 Dec 10, 2022
An image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testingAn image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testing

SVM Données Une base d’images contient 490 images pour l’apprentissage (400 voitures et 90 bateaux), et encore 21 images pour fait des tests. Prétrait

Achraf Rahouti 3 Nov 30, 2021
Python library for computer vision labeling tasks. The core functionality is to translate bounding box annotations between different formats-for example, from coco to yolo.

PyLabel pip install pylabel PyLabel is a Python package to help you prepare image datasets for computer vision models including PyTorch and YOLOv5. I

PyLabel Project 176 Jan 01, 2023
Code for Motion Representations for Articulated Animation paper

Motion Representations for Articulated Animation This repository contains the source code for the CVPR'2021 paper Motion Representations for Articulat

Snap Research 851 Jan 09, 2023
A Tensorflow implementation of BicycleGAN.

BicycleGAN implementation in Tensorflow As part of the implementation series of Joseph Lim's group at USC, our motivation is to accelerate (or sometim

Cognitive Learning for Vision and Robotics (CLVR) lab @ USC 97 Dec 02, 2022
[SIGGRAPH'22] StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets

[Project] [PDF] This repository contains code for our SIGGRAPH'22 paper "StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets" by Axel Sauer, Katja

742 Jan 04, 2023
QSYM: A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing

QSYM: A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing Environment Tested on Ubuntu 14.04 64bit and 16.04 64bit Installation # disabl

gts3.org (<a href=[email protected])"> 581 Dec 30, 2022
HyperPose is a library for building high-performance custom pose estimation applications.

HyperPose is a library for building high-performance custom pose estimation applications.

TensorLayer Community 1.2k Jan 04, 2023
Pytorch0.4.1 codes for InsightFace

InsightFace_Pytorch Pytorch0.4.1 codes for InsightFace 1. Intro This repo is a reimplementation of Arcface(paper), or Insightface(github) For models,

1.5k Jan 01, 2023
BOVText: A Large-Scale, Multidimensional Multilingual Dataset for Video Text Spotting

BOVText: A Large-Scale, Bilingual Open World Dataset for Video Text Spotting Updated on December 10, 2021 (Release all dataset(2021 videos)) Updated o

weijiawu 47 Dec 26, 2022
Tensorflow implementation of "Learning Deep Features for Discriminative Localization"

Weakly_detector Tensorflow implementation of "Learning Deep Features for Discriminative Localization" B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and

Taeksoo Kim 363 Jun 29, 2022
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning

Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning This is the official repository for Conservative and Adaptive Penalty fo

7 Nov 22, 2022
Unified learning approach for egocentric hand gesture recognition and fingertip detection

Unified Gesture Recognition and Fingertip Detection A unified convolutional neural network (CNN) algorithm for both hand gesture recognition and finge

Mohammad 227 Dec 25, 2022
Official PyTorch implementation of Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations

Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations Zhenyu Jiang, Yifeng Zhu, Maxwell Svetlik, Kuan Fang, Yu

UT-Austin Robot Perception and Learning Lab 63 Jan 03, 2023
Exploring Versatile Prior for Human Motion via Motion Frequency Guidance (3DV2021)

Exploring Versatile Prior for Human Motion via Motion Frequency Guidance This is the codebase for video-based human motion reconstruction in human-mot

Jiachen Xu 5 Jul 14, 2022
The Python code for the paper A Hybrid Quantum-Classical Algorithm for Robust Fitting

About The Python code for the paper A Hybrid Quantum-Classical Algorithm for Robust Fitting The demo program was only tested under Conda in a standard

Anh-Dzung Doan 5 Nov 28, 2022
Cluttered MNIST Dataset

Cluttered MNIST Dataset A setup script will download MNIST and produce mnist/*.t7 files: luajit download_mnist.lua Example usage: local mnist_clutter

DeepMind 50 Jul 12, 2022