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The Implementation of CGI24 paper: An Improved YOLOv8-Based Rice Pest and Disease Detection

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# Documentation

> This document mainly introduces the code implementation part of "Detecting and Analyzing
Pests and Diseases in Agricultural Fields Based on YOLOv8".The purpose of the whole thesis is
mainly to improve the network structure of YOLOv8, so that it can improve the accuracy and
real-time performance in detecting pests and diseases. Next, I will describe how to build up
this project from scratch.

# Installing Python

Go to the official python website: [https://www.python.org/](https://www.python.org/) to download and install it.

![](https://yangyang666.oss-cn-chengdu.aliyuncs.com/typoraImages/Snipaste_2024-05-23_15-10-42.png)

Once the installation is complete, run: python in a command line window to see the results of the 
installation, as shown below:

![](https://yangyang666.oss-cn-chengdu.aliyuncs.com/typoraImages/Snipaste_2024-05-23_15-14-22.png)

At this point, the Python installation is complete, and the next step is to install anaconda, a python
virtual environment that is particularly good for managing python

# Installing anaconda

Go to anaconda's official website: [https://www.anaconda.com/download/success](https://www.anaconda.com/download/success) to download and install it:

![](https://yangyang666.oss-cn-chengdu.aliyuncs.com/typoraImages/Snipaste_2024-05-23_15-17-10.png)

After successful installation, it will appear in the start menu as shown below:

![](https://yangyang666.oss-cn-chengdu.aliyuncs.com/typoraImages/Snipaste_2024-05-23_15-19-17.png)

anaconda installation is complete, next install pycharm, which is mainly used for writing code

# Installing Pycharm

![](https://yangyang666.oss-cn-chengdu.aliyuncs.com/typoraImages/Snipaste_2024-05-23_15-23-47.png)

Students can apply for the educational version

![](https://yangyang666.oss-cn-chengdu.aliyuncs.com/typoraImages/Snipaste_2024-05-23_15-24-59.png)

And, all software installations are complete.

# Introduction to YOLOv8 Catalog Structure

First introduce the entire project's table of contents:

![](https://yangyang666.oss-cn-chengdu.aliyuncs.com/typoraImages/Snipaste_2024-05-23_15-27-47.png)

![](https://yangyang666.oss-cn-chengdu.aliyuncs.com/typoraImages/Snipaste_2024-05-23_15-28-07.png)

Compared with the original YOLOv8, the root directory adds some new training scripts and testing
scripts, such as train.py and Detect.py, of course, you can also directly through the command line,
both have the same effect.
> **The focus is on the ultralytics/nn directory, where all the improvement modules are
made, and where I have created a new Addmodules directory with the various modules
for the improvements, including improvements to the backbone network, the neck
network, and the detection header.**

![](https://yangyang666.oss-cn-chengdu.aliyuncs.com/typoraImages/Snipaste_2024-05-23_15-36-15.png)

I've already made all the changes I need to make, so I don't need to make any more changes.

> **There is another important directory: ultralytics/cfg/models/Add, which holds all the yaml
files, of which the improved yaml files are already written and do not need to be
changed.**

![](https://yangyang666.oss-cn-chengdu.aliyuncs.com/typoraImages/Snipaste_2024-05-23_15-38-32.png)

Below is an example of a yaml file, the others are similarly structured, just with different parameters:

# Installation of the project's environment 
(very important)

> Environment configuration is very important, I was with the environment for about a week, in
the middle of the experience of a variety of errors, package incompatibility problems and
graphics card driver matching problems, in short, it is not good to get. In order to reproduce
the work, I have exported the anaconda environment to environment.yml, located in the root
directory of the project, and use it directly when creating the virtual environment.

## Anaconda virtual environment

Then type conda env create -f environment.yml in the anaconda prompt terminal to create a virtual
environment based on the environment.yml file. After creating the environment, check whether
the environment exists through the conda env list, as shown in the following figure, which
indicates that the creation is successful:

![](https://yangyang666.oss-cn-chengdu.aliyuncs.com/typoraImages/Snipaste_2024-05-23_16-35-14.png)

If there are torch-related errors when installing, the probability is that your graphics card driver
does not match the version of the torch package in here, this problem needs to be modified on its
own, and there is a lot of information about this on the Internet.

## Using virtual environments

Once the virtual environment is created, you can use it in pycharm by clicking on the bottom right
corner, switching conda environments, and selecting the virtual environment you just created. If at
this point there is no error, congratulations, has completed 80% of the work.

Run the Detect.py script to test the detection, if no errors are reported, the next step is to train the model.

# Training script train.py

Find the train.py file in the root directory, the comments have been written clearly as below:

```py
import warnings
warnings.filterwarnings('ignore')
from ultralytics import YOLO

if __name__ == '__main__':
    model = YOLO('yolov8-HSFPN.yaml')

# model.load('yolov8n.pt') # 是否加载预训练权重,科研不建议大家加载否则很难提升精度

model.train(data=r'D:/Downloads/YOLOv8/datasets/data.yaml',
                # 如果大家任务是其它的'ultralytics/cfg/default.yaml'找到这里修改task可以改成detect, segment, classify, pose
                cache=False,
                imgsz=640,
                epochs=150,
                single_cls=False,  # 是否是单类别检测
                batch=4,
                close_mosaic=10,
                workers=0,
                device='0',
                optimizer='SGD', # using SGD
                # resume='runs/train/exp21/weights/last.pt', # 如过想续训就设置last.pt的地址
                amp=True,  # 如果出现训练损失为Nan可以关闭amp
                project='runs/train',
                name='exp',
                )
```

model = YOLO('yolov8-HSFPN.yaml'), replace the yaml file in it with your own yaml file, I am using
yolov8- HSFPN.yaml here, data=r'D:/Downloads/YOLOv8/datasets/data.yaml, similarly, replace it with
the yaml file of your own dataset, my dataset here is in YOLO format. Other parameters can be
adjusted according to your task.

There's also a script for detecting, Detect.py:

```python
import warnings
warnings.filterwarnings('ignore')
from ultralytics import YOLO

if __name__ == '__main__':
    model = YOLO('D:/Downloads/YOLOv8/result/result_8_HSFPN/train/exp/weights/best.pt') # select your model.pt path
    model.predict(source='D:/Downloads/YOLOv8/ultralytics/assets',
                  imgsz=640,
                  project='runs/detect',
                  name='exp',
                  save=True,
                )
```

Similarly, replace best.pt with your own trained model, enter the path of the detected image
inside source, run the script to start the detection, and save the results in the runs/detect directory.

# Start training

Prepare the dataset, preferably in yolo format, my dataset comes with the project, no need to redownload it:

Inside the datasets directory is my dataset: there are three directories, TRAIN, TEST, and VALID,
which hold the images and labels for the training, test, and validation sets, respectively

![](https://yangyang666.oss-cn-chengdu.aliyuncs.com/typoraImages/Snipaste_2024-05-23_15-58-01.png)

![](https://yangyang666.oss-cn-chengdu.aliyuncs.com/typoraImages/Snipaste_2024-05-23_15-58-32.png)

After preparing these, run the train.py file and start training. If you get an error, look it up on the
internet, it's just that you can't find the dataset, the version of a package is wrong, or you can't
use the GPU, you can only use the CPU.

# Training results

> The training results will be saved in the runs/train directory in the order of exp1,exp2,exp3, indicating 
the results of each training

![](https://yangyang666.oss-cn-chengdu.aliyuncs.com/typoraImages/Snipaste_2024-05-23_16-04-37.png)

The picture above is the structure of the directory after the training is done, inside the weights
directory are the models we need: best.pts is the one with the best results, and that's the one we
need at the end, and last.pt is the result of the last training.

![](https://yangyang666.oss-cn-chengdu.aliyuncs.com/typoraImages/Snipaste_2024-05-23_16-05-47.png)

# Summarization

**I have already done the improvement work of the whole project, if you reproduce it, you only
need to install the corresponding environment, modify the parameters of train.py, run train.py
tostart training; modify the parameters of Detect.py, you can detect. Currently, the project is
only for the detection task, and no improvements have been made for segmentation and
classification.**

# rule of thumb

**（1）The following are the versions of two important libraries, which must be downloaded 
accordingly, otherwise errors will be reported**

> python == 3.9.7
> pytorch == 1.12.1 
> timm == 0.9.12  # 此安装包必须要
> mmcv-full == 1.6.2  # 不安装此包部分关于dyhead的代码运行不了以及Gold-YOLO

**（2）The reason why mmcv-full fails to install is because of a problem with the compilation tool on 
your own system, or because of a conflict between the environments in which it is installed.**

We recommend that you install offline in the form of the following address you can find their own version, 
download to the local installation.
    https://download.openmmlab.com/mmcv/dist/cu111/torch1.8.0/index.html
    https://download.openmmlab.com/mmcv/dist/index.html

**（3）basicsr installation failure reasons, through pip install basicsr download if it fails, you can
go to Baidu search how to change the download mirror source can be repaired!**

## The solution to some of the reported errors is here

**(1)Nan values appear for loss during training.**
   Try turning off AMP mixed precision training.

**(2) Multi-card training problems, after modifying the model can not support multi-card
training you can try the following two lines of command line operations, the two are different
operations, is on behalf of different versions now try the first one does not work with the
second one**

python -m torch.distributed.run --nproc_per_node 2 train.py
    python -m torch.distributed.launch --nproc_per_node 2 train.py

**(3) For the runtime of some of the error resolution**
    1. If the validation of the training process reported an error (mainly some shape mismatch error this 
is due to the validation of the set of some of the special pictures lead to)
Find rect=mode in the build_dataset function in the DetectionTrainer class of 
ultralytics/models/yolo/detect/train.py
```py
2. Running the detect.py file while reasoning reported a shape mismatch error
Find ultralytics/engine/predictor.py and find the function def pre_transform(self, im), 
change auto to False in LetterBox.

3. The problem of mismatched types of errors reported during the training process
Find the file 'ultralytics/engine/validator.py' and find 'class
BaseValidator:' and then in its 'call ' self.args.half = self.device.type ! = 'cpu' # force FP16 val during training with
self.args.half = False below the line of code
```

**(4) For the nc changes in the yaml file**
No need to modify it, the model will automatically get it based on the profile of your dataset.
This is also the difference between printing the model twice, the first printout is the structure of
the yaml file you chose for the model, and the second printout is the yaml file that replaces your
dataset, the model uses the second one.

**(5) Environment-specific issues**
The environment is different for everyone who meets it, so you can find out for yourself online.

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