Tag: 物体認識

[Updated December 2022] How to install YOLOX
What is YOLOX?

Among the many releases in the YOLO series, the newest one, YOLOX, came out in July 2021.

The YOLOX paper is available here (currently a preprint).
The GitHub repository is here.

YOLO is an object detection algorithm.
It is designed to run in real time, so it is extremely lightweight (inference alone can even run on a smartphone), it is easy to build systems around, and it has been adopted in many IoT devices.

“Object detection algorithm” may sound complicated, but an easy example to picture is a camera’s automatic face or eye focus.
A marker appears telling you “here is a face”; the same idea applies to other objects as well, and each one can be classified as to what it is.

There are many versions in the YOLO series: YOLO, YOLOv2, YOLOv3, YOLOv4, YOLOv5, and YOLOX.
It also runs not only on Python but on MATLAB as well, so you can adapt it to whichever platform you use.

Unlike the system used from YOLOv3 onward, YOLOX is based on the system of the original YOLO (the first published method), and it is said to be both faster and considerably more accurate.

Other blogs cover the details as well.

How to install YOLOX

Installation instructions are given on the GitHub page, but they are in English and do not cover using a virtual environment, so here I explain how to do it with an Anaconda virtual environment.

On both Windows and Mac, following the YOLOX manual exactly produced a string of errors.
The errors will likely keep changing with future versions, so look them up as they come up.

See below for how to use the conda command in the macOS Terminal.

Using Conda Commands on macOS

On Windows, I recommend using Anaconda Prompt.

Open Terminal (Anaconda Prompt), and a black window filled with text will appear.
To the left of the last line you should see (base).
At this point you are not inside a virtual environment.

YOLOX will run without a virtual environment, but since environments can conflict with other packages, I recommend creating one.

First, enter the following code to create the virtual environment.
```
conda create -n YoloX python=3.8
```
YoloX is the name of the virtual environment; you can use any other name.
You can also specify the Python version with python=3.8.

Then enter the virtual environment with the following code.
```
conda activate YoloX
```
If the (base) on the left has changed to (YoloX), you have succeeded.

Installing CUDA

First, if this is your first time installing machine learning libraries and you plan to train YOLO on a GPU, install CUDA.
(Training on a CPU takes an absurdly long time, so I do not recommend it. Note, however, that GPUs cannot currently be used on Apple Silicon machines.)
CUDA can be downloaded from the official site.

As for the CUDA version, I suggest checking which CUDA version PyTorch supports and then installing that version.
PyTorch official site

To install an older version, click Download now and then, on the following screen, scroll down to Resources and use Archive of Previous CUDA Releases.

The latest version may well work too, but for now, matching the version to PyTorch should let you run everything without trouble.

To check whether it is installed on Windows, open “Edit the system environment variables,” go to Advanced > Environment Variables, and
check whether there is a path beginning with CUDA_PATH.
The number after the V indicates the version.

Installing PyTorch

Before installing, update pip.
Enter the virtual environment in Anaconda Prompt and run the following command.
```
python -m pip install --upgrade pip
```
Go to the PyTorch official site and select the installation method that matches your environment.
One thing to watch out for is the Package field: here, choose pip.
Installing with conda will cause errors later on.

Copy and paste the command shown under “Run this Command” to install it in your virtual environment.

Once the installation finishes, use the following command to check that PyTorch is installed.
```
pip list
```
As long as torch appears in this list, you’re good to go!!!
Just to make sure everything works, launch python and run the following command.
```
import torch
print(torch.cuda.is_available())
```
If the output is True, the installation went through without problems and torch is ready to run on the GPU.
(On a Mac or on a computer without a GPU, you will get False, but as long as the import works you’re fine.)
If you get an error, go back and check the installation again.

Installing the packages required for YOLOX

Download (clone) the various YOLOX files from GitHub.
If you can use Git commands, you can clone it with the code below.
```
git clone git@github.com:Megvii-BaseDetection/YOLOX.git
```
Next, navigate into the cloned folder and install the required packages.

First, edit the contents of requirements.txt in the cloned YOLOX folder as follows.
```
# TODO: Update with exact module version
numpy
#torch>=1.7
opencv_python
loguru
tqdm
#torchvision
thop
ninja
tabulate

# verified versions
# pycocotools corresponds to https://github.com/ppwwyyxx/cocoapi
#pycocotools>=2.0.2
#onnx==1.8.1
onnxruntime==1.8.0
onnx-simplifier==0.3.5
```
It seems that packages such as pycocotools cannot be installed with this command.

Once you’ve done that, save requirements.txt and run the command below in the terminal (Anaconda prompt).
```
cd YOLOX
pip3 install -r requirements.txt
pip3 install cython pycocotools
```
The YOLOX folder you downloaded contains a file called “requirements.txt” that lists the required packages. The second command opens this file in read-only mode and installs the packages listed in it.
```
pip3 install cython pycocotools
```
This command installs cython and pycocotools.
It appears these need to be installed separately.

When you run this command, some of you may see the following error.
```
      building 'pycocotools._mask' extension
      error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/
```
Apparently pycocotools requires Microsoft C++ Build Tools. (Even though there is no Mac version, the error still shows up on Mac.)
If you’re on Windows, it’s a good idea to install it just in case.

Install it, then try installing again with the code below.
```
pip3 install "git+https://github.com/philferriere/cocoapi.git#egg=pycocotools&subdirectory=PythonAPI"
```
If that still doesn’t work, install it with the following command.
```
conda install -c conda-forge pycocotools
```
In general we’ve been installing these packages with pip, but since this one simply won’t install that way, we fall back on the last resort of installing it with conda.

Next, download yolox_x.pth.
The download starts as soon as you click the link.

Move the downloaded file into the YOLOX folder you cloned from GitHub.
This gives you the model used for detection in the demo, placed wherever you like.

Add the following lines near the top of tools/demo.py so that the script can access the yolox folder.
```
#demo.pyの中身に書き込みましょう。
import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
```
Without this, you will get an error saying that the yolox package cannot be found.

Then run the following command in the terminal.
This lets you check whether the demo runs correctly.
```
python tools/demo.py image -n yolox-x -c yolox_x.pth --path assets/dog.jpg --conf 0.25 --nms 0.45 --tsize 640 --save_result --device gpu 
#gpuで動かさない場合には、gpuをcpuに書き換えてください。
```
Change –device gpu to –device cpu as appropriate for your setup.

You should now find the following image inside YOLOX/YOLOX_outputs/yolox_x/vis_res/20……..

Bonus

For those who aren’t sure which computer to buy, I’ve started offering computer purchase consultations on Coconala!
あなたの要望に合わせてパソコンを選び、提案しますパソコン選びに困っている方々へ！様々な目的に対応できます！
I often pick out computers and give advice on them, and many friends told me I could make money doing computer consultations.
Inspired by that, I decided to give it a try!

I’ll recommend a computer that suits what you plan to use it for and your budget.
In particular, I’ve chosen and used many computers for machine learning.

And if you’d like, I can also advise you on what to look for the next time you buy a computer.

Computers I’ve picked out so far include lab analysis machines, everyday-use machines, game-streaming machines, machines for incoming university students, machines for architecture students that can run CAD, and basic no-frills machines.

I use both Mac and Windows, so I can talk about and recommend either!

Please feel free to make use of it.
2022-11-15
[Updated December 2022] How to Install YOLOv5 [Object Detection Tool]
What is YOLOv5?

YOLO is an object detection algorithm. The name stands for “You Only Look Once.”
It differs somewhat from object tracking: it detects objects and identifies what they are.

YOLOのHPより引用(https://pjreddie.com/darknet/yolo/)

In applications such as autonomous driving, YOLO detects and classifies objects the way a human would judge them—deciding whether something is a person, a pet, or a car.

The classification algorithm itself is complex, so I will not go into it here, but a quick search will turn up a great many sites that explain it.

New versions of YOLO have been appearing at a pace of roughly once every two years. So far we have had YOLO v1 through YOLOv5, and in August 2021 a new version, YOLO X, was announced.
The differences between versions involve substantial changes in the details, but as a rule accuracy improves with each new release.

In this post I will walk through the steps for installing YOLO v5 and verifying that it works—a version that offers sufficient accuracy and, now that plenty of information is available, is easy to get up and running.

Installation environment

OS: Windows 10 (macOS Monterey also worked)
Python: 3.9.7 (3.6 or later)
CUDA: 11.3

How to install

Creating a virtual environment

We will create a virtual environment using Anaconda.
If you do not have Anaconda installed, install it first.

Installing YOLOv5 pulls in quite a few packages, so I recommend installing it inside a virtual environment.
You can name the environment anything you like; here we will call it “Yolov5_env”.
Enter the following command in Anaconda Prompt.
```
conda create -n Yolov5_env python=3.9
```
This should create the virtual environment.
Activate it with the following command.
```
conda activate Yolov5_env
```
Installing CUDA

First, if this is your first time installing machine learning libraries and you plan to train YOLO on a GPU, install CUDA. (Training on a CPU takes an absurdly long time, so I do not recommend it.)
CUDA can be downloaded from the official site.

As for which CUDA version to use, I suggest checking which CUDA versions PyTorch supports and installing that version.
PyTorch official site

To install an older version, click Download now and then, on the screen that appears, use Archive of Previous CUDA Releases under Resources near the bottom of the page.

The latest version may well work too, but for now, matching the version to PyTorch should let you run everything without trouble.

To check whether it installed, on Windows open “Edit the system environment variables,” go to Advanced > Environment Variables, and
look for a path beginning with CUDA_PATH.
The number after the V indicates the version.

Installing PyTorch

Before installing, update pip.
Activate the virtual environment in Anaconda Prompt and run the following command.
```
python -m pip install --upgrade pip
```
Go to the PyTorch official site and select the installation options that match your environment.
One thing to watch out for is the Package field: be sure to choose pip here.
If you install with conda, you will run into errors later on.

Copy and paste the command shown under “Run this Command” to install PyTorch in your virtual environment.

Once the installation finishes, use the following command to check that PyTorch is installed.
```
pip list
```
If torch appears in the list, you are all set!
As a quick sanity check, launch python and run the following commands.
```
import torch
print(torch.cuda.is_available())
```
If the output is True, the installation succeeded and torch is ready to run on the GPU.
(On a Mac, or on a computer without a GPU, you will see False, but as long as the import goes through you are fine.)
If you get an error, go back and review the installation.

Installing YOLOv5

Download (clone) the YOLOv5 files from GitHub.
```
git clone https://github.com/ultralytics/yolov5
```
If you cannot use the git command, either install git or download the files from the YOLOv5 GitHub repository.

Next, run the following command in your virtual environment in Anaconda Prompt.
```
cd yolov5
pip install -r requirements.txt
```
The first command moves you into the yolov5 folder you downloaded from GitHub. If you installed it with the git command, this will work as is; if you downloaded it directly from the website, you will have to navigate to that directory yourself.

The downloaded yolov5 folder contains a file called “requirements.txt” that lists the necessary packages. The second command opens this file in read-only mode and installs the packages listed in it.

Once the installation is finished, let’s check which packages were installed.
```
pip list 
```
You will probably see that quite a few packages were installed.
With that, the YOLOv5 installation is complete for now.

Running it

To check that everything works, let’s try it with the data that comes with the yolov5 folder.
Open Anaconda Prompt, activate the virtual environment, and use the cd command to move into the yolov5 folder.

Then run the following command.
```
python detect.py --source ./data/images/ --weights yolov5s.pt --conf 0.4
```
detect.py is the program that performs object detection using YOLO.
With source you specify the path to the folder containing the material you want to run detection on.
Here I used the images included in yolov5.

With weights you choose which model to use.
Here we use yolov5.pt.

conf sets the likelihood threshold at which an object is recognized.
This is a term you will run into often when studying machine learning.
If you are not sure, leaving it as is should be fine.

After running it, you should find the annotated images in runsdetectexp inside the yolov5 folder.
This is how object detection is done in YOLOv5.

Errors I ran into

・ModuleNotFoundError: No module named ‘torch’

You may see this error when trying to run YOLO.
It means PyTorch is not available, i.e., the module cannot be found.

If you get this error, first check whether torch is installed in your virtual environment.
```
pip list
```
If torch is not listed, install PyTorch.
If it is there but things still don’t work and you’re not sure why, start Python, import PyTorch, and check whether it is usable.
```
import torch
torch.cuda.is_acailable()
```
If it is available, True will be returned; if not, you will likely see the error above.

One possible cause of the error is that PyTorch was installed with the conda command.
If PyTorch is installed via conda while the other packages are installed via pip, the error above can occur.

In that case, uninstall torch with the conda command and reinstall it with pip.

“Module not found” problems often arise when you have mixed up pip and conda when installing.
Here I have used pip throughout, but it’s a good idea to always keep track of which command you used to install something.

Links

Anaconda
CUDA
PyTorch
YOLOv5 GitHub
YOLO homepage

Bonus

For those who aren’t sure which computer to buy, I’ve decided to offer PC purchase consultations on Coconala!
あなたの要望に合わせてパソコンを選び、提案しますパソコン選びに困っている方々へ！様々な目的に対応できます！
I often pick out computers and give advice about them, and many friends have told me I could make money doing PC consultations.
Inspired by that, I figured I’d give it a try!

I’ll suggest a purchase that fits how you plan to use the computer and your budget.
In particular, I’ve chosen and used many computers for machine learning.

And if you’d like, I’m happy to also advise you on what to look for when buying a computer in the future.

Computers I’ve picked out so far include analysis machines for the lab, everyday-use machines, PCs for game streaming, PCs for incoming university students, PCs capable of running CAD for architecture students, and simple stopgap machines.

I use both Mac and Windows, so I can discuss and recommend either!

Please give it a try!
2022-02-23