Tag: Yolo

What Exactly Is YORU, a New Behavior Analysis Tool Powered by Object Detection?

In November 2024, a preprint was posted on bioRxiv describing YORU, a behavior analysis tool that takes a different approach from existing behavior analysis tools.
In this post, I’d like to explain what kind of tool it is.

The YORU preprint

The YORU documentation

What is YORU?

YORU is a deep learning-based tool for analyzing animal behavior.

YORUのサイトより引用

A number of deep learning-based tools, such as DeepLabCut and SLEAP, have already been released.
These tools use deep learning to track body parts of animals and estimate their posture (here I’ll refer to them as tracking tools).

By estimating posture, they reveal what posture an animal is in and use that for behavior analysis.

YORU, by contrast, recognizes behavior using an object detection algorithm.
In short, it does not track body parts; instead, it defines behavior directly from the appearance of the animal as it behaves.

When people tell an apple from a mandarin orange, they rely on cues such as shape and color.
Object detection excels at classifying objects, so it makes exactly this kind of appearance-based distinction.

Applying this to animal behavior analysis means classifying animal behavior from its appearance.
Within YORU, we call this a “behavior object.”

Whereas tracking tools represent behavior as points and lines, YORU analyzes behavior by enclosing it in a bounding box.

Advantages of defining behavior with object detection

The advantages of object detection-based behavior analysis include:

・Errors are less likely as the number of individuals increases

・Less sensitive to the orientation of the animal

・Fast analysis speed

・Can capture behaviors that are hard to capture by tracking (such as a mouse crouching)

and so on.

It makes it easy to analyze the behavior of multiple individuals—a weak point of tracking tools—at a considerably lower computational cost.
It can also easily analyze behaviors that are difficult to define from the positional information of body parts.

Consider, for example, the act of opening and closing a hand.
With a tracking tool, you would track the fingertips with a camera and define the hand as open or closed, but if the fingertips become hidden when the hand closes, tracking fails and the positional information can no longer be computed accurately.

With object detection, however, the model looks at the overall shape of the open and closed hand, so it can capture the state accurately even when part of the hand is hidden.

The same logic explains its robustness to larger numbers of individuals: with a tracking tool, as the number of individuals grows, you must assign each body part to the correct individual to capture each individual’s behavior accurately, whereas YORU analyzes only the shape of each individual and therefore requires no individual identification or assignment.

Because analysis is fast, real-time analysis is possible, allowing you to control equipment when a particular behavior occurs and thus automate behavioral experiments.

Disadvantages of defining behavior with object detection

Of course, there are disadvantages as well:

・Cannot capture a sequence of behaviors

・Cannot identify individuals

・Cannot capture unknown behaviors

・Cannot capture behaviors that are hard to distinguish by appearance

・Provides no detailed information about the defined behavior

These are precisely the strengths of tracking tools; rather than one approach being superior, the two complement each other’s advantages and disadvantages.

Experimenters therefore need to choose the method appropriate to their situation.

What makes YORU special?

Having described the advantages and disadvantages of the object detection-based behavior analysis that underlies YORU, what exactly makes YORU special?

Everything runs through a GUI

YORU lets you perform behavior analysis without any programming.

The same is true of tools like DeepLabCut and SLEAP, and it goes a long way toward making these analyses accessible to biologists.

Design

Unlike previous tools, YORU’s design doesn’t really feel like a research tool.

Built-in real-time analysis

Unusually for tools of this kind, YORU comes with a full GUI for real-time analysis.

Building a real-time analysis system isn’t that difficult if you can program to some extent, but otherwise the barrier is quite high.

With YORU, however, real-time analysis and even external triggering can all be done without any programming.

Adaptable to a wide range of experimental setups

The biggest hurdle in real-time analysis is figuring out how to combine the code that drives your own experimental apparatus with the analysis itself.

YORU adopts a plugin system, letting you choose the program that controls your external device.

In other words, you simply select the plugin that matches your own system.

You can also write your own plugins: by wrapping the control program for your own apparatus in a plugin, you can easily link it to YORU’s real-time analysis.

This makes for a remarkably easy-to-use system that no other tool offers.

Closing remarks

YORU has only just been released, and there are still rough edges in usability as well as gaps in the documentation.

That said, as these are addressed over time, I am very much looking forward to seeing how it comes to be used.

2024-11-17
[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

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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!

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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