Category: Research Tools

What is YOLOX?

Among the many entries in the YOLO series, the latest one, YOLOX, was released 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 and is extremely lightweight (inference alone can even run on a smartphone). It is also highly adaptable, and is widely used in IoT devices.

“Object detection algorithm” may sound complicated, but an easy analogy is a camera’s automatic face or eye focus.
A marker appears indicating “this is a face”; the same idea applies to other objects, and the algorithm can also classify what each one 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 architecture used from YOLOv3 onward, YOLOX returns to the design of the original YOLO. It is reportedly 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 one error after another.
Errors will likely keep changing with future versions, so look them up as they come.

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 the Terminal (Anaconda Prompt), and you will get a black window filled with text.
You should see (base) at the left of the last line.
This means you are not currently 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 a virtual environment.

conda create -n YoloX python=3.8

YoloX is the name of the virtual environment; you can use any name you like.
You can also specify the Python version with python=3.8.

Then activate the virtual environment with the following code.

conda activate YoloX

If (base) on the left has changed to (YoloX), you are all set.

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 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 versions PyTorch supports and installing one of those.
PyTorch official site

To install an older version, click Download now and then, further down the page under Resources, use Archive of Previous CUDA Releases.

The latest version may 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 see whether there is 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 choose the installation method that matches your environment.
One catch here is the Package field: be sure to select pip.
Installing with conda will cause errors later on.

Copy and paste the command 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 the list, you’re good to go!!!
As a quick check, start python and run the following command.

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 it will return 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 YOLOX files from GitHub.
If you can use Git commands, you can clone the repository with the code below.

git clone git@github.com:Megvii-BaseDetection/YOLOX.git

Next, move 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 that’s done, 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 downloaded YOLOX folder 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 seems these have 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/

Microsoft C++ Build Tools is apparently required by pycocotools. (Oddly enough, the error also appears on Mac, even though there is no Mac version.)
Windows users should install it just to be safe.

After installing it, try the installation 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 resort to 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 to the top of tools/demo.py so that the yolox folder can be accessed.

#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’ll get an error saying 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。

Replace –device gpu with –device cpu as appropriate for your setup.

If the following image appears in YOLOX/YOLOX_outputs/yolox_x/vis_res/20…….., everything is working.

Bonus

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