1. Introduction to this article
Hello everyone, what I bring to you this time is not an improvement, but an analysis of the entire YOLOv8 project, The entire series will probably be updated with about 7-10 articles, which will explain everything from the project directory to each functional code in detail. At the same time, the YOLOv8 improvement series has also exceeded 30 article. Finally, it is expected that this column will continue to be updated and hundreds of improvement tutorials will be updated by the end of the year. Therefore, if you have not subscribed to the column, you can subscribe to the following in advance. Let's start the first article of line-by-line analysis of YOLOv8 - Project directory structure analysis
Before I begin, I would like to recommend my column to you.This column updates more than a hundred articles on the YOLOv8 improvement mechanism and teaches you how to add it to In the network structure, targeted article introductions are also provided for readers who have obtained the model but do not know how to modify it or how to publish a paper.The average quality score of this column is 98 points, Content quality is fully guaranteed.
Table of contents
1. Introduction to this article
2. Project directory structure analysis
2.7 Files in the same directory
2. Project directory structure analysis
Before starting, let me analyze the source code address for everyone ->
Official code address:YOLO warehouse download address
The picture below is the entire picture after we downloaded it from the warehouse and opened it. The part on the left is the file, and the right is the display window.
Below is a clear screenshot of the file section ->
Let’s analyze the functions of each file on the left one by one ->
2.1 .github
This directory contains the following contents:
ISSUE_TEMPLATE:Provides different types of issue report templates, including bug-report.yml, config.yml, feature-request.yml and question.yml. These templates help users report bugs, make feature requests, or ask questions in a structured way.
workflows: Contains multiple workflow files, such as ci.yml (continuous integration), cla.yml (contributor license agreement), codeql.yml ( Code quality check), docker.yml (Docker configuration), greetings.yml (automatically greet new contributors), links.yml, publish.yml (automatic publishing), stale.yml (handle stale issues).
dependabot.yml (automatic dependency update)
Together, these files support automated management of the project, including code quality assurance, continuous integration and deployment, community interaction, and dependency maintenance.
2.2 docker
This directory contains the following contents:
The docker directory contains multiple Dockerfiles, each configured for a different environment or platform, for example:
- Dockerfile: The main Docker configuration file, used to build the default Docker image of the project.
- Dockerfile-arm64: Docker configuration customized for ARM64-based devices (such as certain types of servers or advanced embedded devices).
- Dockerfile-conda: Docker configuration file that uses the Conda package manager to configure the environment.
- Dockerfile-cpu: Docker configuration file configured for environments that do not support GPU acceleration.
- Dockerfile-jetson: Docker configuration customized for NVIDIA Jetson platform.
- Dockerfile-python: Possibly a simplified Docker configuration for a pure Python environment.
- Dockerfile-runner: Docker configuration that may be used to configure a continuous integration/continuous deployment (CI/CD) runtime environment.
These configuration files are used for deployment. Users can choose the appropriate environment to deploy and run the project according to their own needs.
2.3 docs
The docs directory is usually used to store documentation, including translations in multiple languages. For example, there are multiple folders in this directory, each folder represents a language (such as en represents English documents). In addition, there are several important Python scripts and configuration files for you to talk about:
build_docs.py:A Python script that automates the process of building and compiling documentation.
mkdocs.yml: MkDocs configuration file, used to specify the structure and settings of the documentation website.
Take mkdocs_es.yml as an example, which is the MkDocs configuration file used to build Spanish documents. Similarly, mkdocs_zh.yml is used to build Chinese documents. So these documents actually have nothing to do with us learning YOLOv8.Everyone just needs to understand the following.
2.4 examples
In the examples folder, you can find YOLOv8 implementation examples in different programming languages and platforms:
YOLOv8-CPP-Inference: Contains YOLOv8 inference examples implemented in C++ language, including CMakeLists.txt (CMake configuration file for project construction), inference.cpp and inference .h (source code and header files related to inference), main.cpp (main program entry) and README.md (instructions for use).
YOLOv8-ONNXRuntime: Provides a YOLOv8 inference example using the Python language and ONNX Runtime. Main.py is the main script file, and README.md provides how to use the example. guide.
YOLOv8-ONNXRuntime-CPP:Similar to the above ONNX Runtime, but written in C++, including the corresponding CMakeLists.txt, inference.cpp, inference.h and main .cpp file, and a README.md that explains how to run the examples.
Each example is equipped with corresponding documentation, which is an example of deploying and using YOLOv8 in different environments when we deploy the model.
2.5 tests
The tests directory contains the project's automated test scripts, each of which tests different parts of the project:
conftest.py:Contains test configuration options or shared test helper functions.
test_cli.py: Used to test the functionality and behavior of the command line interface (CLI).
test_cuda.py: Specifically tests whether the project can correctly use NVIDIA's CUDA technology to ensure that the GPU acceleration function is normal.
test_engine.py: Test the underlying inference engine, such as model loading and data processing.
test_integrations.py: Test whether the project's integration with other services or libraries works properly.
test_python.py: Used to test whether the Python API interface of the project works as expected.
These test scripts ensure that the file will still run after you have improved it, updated it, or added new features.
2. 6 runs
We did not see this file in the directory structure above because it is a file generated after we successfully trained the model once, and it stores various information after each training.
The following is a complete save file after successful training. If you are interested, you can read my other explanation, which introduces each file in detail and explains each parameter contained in it in detail!
YOLOv8 performance evaluation indicators->mAP, Precision, Recall, FPS, IoU
2.6 utlralytics (key points)
Most of the files mentioned above are actually not useful to most readers. The utralytics file here is the focus, including YOLOv8 All functions are integrated under this file directory. Here I will only introduce the functions of each directory. I will talk about the internal code of each file in detail in the next few blogs.
2.6.1 assets
Below this file are saved two of the most classic pictures in the history of YOLO. These are the pictures that everyone uses for basic reasoning and are for testing.
2.6.2 cfg (emphasis)
Our model configuration file is saved under this file. The cfg directory is the centralized place for project configuration, including:
datasets folder: Contains the configuration file of the data set, such as data path, category information, etc. (That is, when we train the YOLO model, we need a data set, and this is where we save the part. The yaml file of the data set. If we do not specify a data set during training, the data set file will be automatically downloaded. But it is easy to fail!).
models folder: stores the model configuration file, which defines the model structure and training parameters. This is what we improved or configured from a yaml file of the basic version. Place,The screenshot is as follows->
Each .yaml file in the models folder represents a different YOLOv8 model configuration, including:
yolov8.yaml: This is the standard configuration file of the YOLOv8 model, which defines the basic architecture and parameters of the model.
yolov8-cls.yaml: The configuration file adjusts the YOLOv8 model specifically for image classification tasks.
yolov8-ghost.yaml: Applies the YOLOv8 variant of the Ghost module, aiming to improve computing efficiency.
yolov8-ghost-p2.yaml and yolov8-ghost-p6.yaml: These files are Ghost model variant configurations for specific size inputs.
yolov8-p2.yaml and yolov8-p6.yaml: YOLOv8 model configurations for different processing levels (such as different input resolutions or model depths).
yolov8-pose.yaml: YOLOv8 model configuration customized for pose estimation tasks.
yolov8-pose-p6.yaml: Aiming at larger input resolutions or more complex model architectures for pose estimation tasks.
yolov8-rtdetr.yaml: May represent a variant of the YOLOv8 model for real-time detection and tracking.
yolov8-seg.yaml and yolov8-seg-p6.yaml: These are YOLOv8 model configurations customized for semantic segmentation tasks.
These configuration files are the core of model training and deployment. At the same time, if you make improvements, you will also modify the corresponding files to optimize the network structure.
trackers folder: is used for tracking algorithm configuration.
__init__.py file: Indicates that `cfg` is a Python package.
default.yaml: The default configuration file of the project, which contains common configuration items shared by multiple modules.
This file is used when configuring training and some task selection parts. For specific usage, you can read my other blog, which details the detailed meaning of more than a hundred parameters.
2.6.3 data
In the data/scripts folder, a series of scripts and Python files are included:
- download_weights.sh: Script used to download pre-trained weights.
- get_coco.sh, get_coco128.sh, get_imagenet.sh: Scripts for downloading the full version of the COCO dataset, the 128-image version, and the ImageNet dataset.
In the data folder, including:
annotator.py: Tool for data annotation.
augment.py: Functions or tools related to data enhancement.
base.py, build.py, converter.py: Contains basic classes or functions for data processing, scripts for building data sets, and data format conversion tools.
dataset.py: Related functions for data set loading and processing.
loaders.py: Defines the method for loading data.
utils.py: General tool functions related to various data processing.
2.6.4 engine
The engine folder contains core code related to model training, evaluation and inference:
exporter.py: is used to export the trained model to other formats, such as ONNX or TensorRT.
model.py: Contains the model definition, and also includes methods for model initialization and loading.
predictor.py: Contains the logic for inference and prediction, such as loading the model and predicting the input data.
results.py: Used to store and process the results output by the model.
trainer.py: Contains the logic of the model training process.
tuner.py: is used for model hyperparameter tuning.
validator.py: Contains the logic of model validation, such as evaluating model performance on the validation set.
2.6.5 hub
The hub folder is typically used to handle operations related to platform or service integration, including:
auth.py: Handles authentication processes, such as API key verification or OAuth processes.
session.py: Manage sessions, including creating and maintaining persistent sessions.
utils.py: Contains some common utility functions that may be used to support authentication and session management functions.
2.6.6 models (key points)
Below this directory are the method implementations of some models included in the YOLO warehouse. We are talking about YOLO here, and here is just a brief introduction. The following blog will explain each of them separately.
The models/yolo directory contains different task-specific implementations of the YOLO model:
classify: This directory may contain YOLO models for image classification.
detect: Contains the YOLO model for object detection.
pose: Contains the YOLO model for pose estimation tasks.
segment: Contains the YOLO model for image segmentation,
2.6.7 nn (key points)
All files in this file directory are to define some components of our model. After that, we will improve and optimize. When adding other structures, we must make changes under the corresponding files.
modules folder:
__init__.py: indicates that this directory is a Python package.
block.py: Contains definitions of basic blocks in neural networks, such as residual blocks or bottleneck blocks.
conv.py: Contains implementation related to convolutional layers.
head.py: Define the head of the network for prediction.
transformer.py: Contains implementation related to the Transformer model.
utils.py: Provides auxiliary functions that may be used when building neural networks.
__init__.py: Also mark this directory as a Python package.
autobackend.py: is used to automatically select the optimal computing backend.
tasks.py: defines the process of different tasks completed using neural networks, such as classification, detection or segmentation. All processes are basically defined here. Before defining the model All communication is here.
2.6.8 solutions
__init__.py: identifies this as a Python package.
ai_gym.py: Related to reinforcement learning, such as code for training models in the OpenAI Gym environment.
heatmap.py: Used to generate and process heatmap data, which is common in object detection and event localization.
object_counter.py: Script for object counting, containing logic for detecting and counting instances from images.
2.6.9 trackers
The trackers folder contains scripts and modules that implement the goal tracking function:
__init__.py: indicates that the folder is a Python package.
basetrack.py: Contains the base classes or methods of the tracker.
bot_sort.py: Implements the SORT algorithm (Simple Online and Realtime Tracking) version.
byte_tracker.py: is a tracker based on deep learning that tracks targets in bytes.
track.py: Contains specific logic for tracking single or multiple targets.
README.md: Provides description of the contents and usage of this directory.
2.6.10 utils
This utils directory contains multiple Python scripts, each with specific functions:
callbacks.py: Contains callback functions that are called during the training process.
autobatch.py: Used to implement batch processing optimization to improve the efficiency of training or inference.
benchmarks.py: Contains functions related to performance benchmark testing.
checks.py: Used for various checks in the project, such as parameter verification or environment checking.
dist.py: Involves tools related to distributed computing.
downloads.py: Contains scripts for downloading resources such as data or models.
errors.py: Define classes and functions related to error handling.
files.py: Contains tool functions related to file operations.
instance.py: Contains tools for instantiating objects or models.
loss.py: Define the loss function.
metrics.py: Contains metric calculation functions for evaluating model performance.
ops.py: Contains custom operations, such as special mathematical operations or data transformations.
patches.py: A tool for implementing modifications or patch applications.
plotting.py: Contains drawing tools related to data visualization.
tal.py: Some functional applications of loss functions
torch_utils.py: Provides PyTorch-related tools and auxiliary functions, including calculation of GFLOPs.
triton.py: May be related to NVIDIA Triton Inference Server integration.
tuner.py: Contains tools related to model or algorithm tuning.
All the functions in the ultralytics file directory that are the focus here have been introduced. Here is just a brief introduction. Later blogs will introduce some important functions in detail.
2.7 Files in the same directory
Here are the basic configuration and documentation files of the project:
.gitignore: Git configuration file specifies files to be ignored by Git version control.
.pre-commit-config.yaml: Configuration file for pre-commit hook, used to automatically perform code quality checks before submission.
CITATION.cff: provides format instructions on how to cite this project.
CONTRIBUTING.md: Guidelines explaining how to contribute code to the project.
LICENSE: Contains the license information for the project.
MANIFEST.in: Lists files that need to be included when building and distributing Python packages.
README.md and README.zh-CN.md: The description files of the project are English and Chinese versions respectively.
requirements.txt: Lists the Python dependencies required for the project to run.
setup.cfg and setup.py: Contains scripts for setting up project installation and distribution.
7. Summary of this article
This completes the entire content of this article. It roughly analyzes the functions of each file. Later, the code will be explained in detail based on the importance of the functions of this article. Here I would like to recommend my YOLOv8 Improvement Effective Points Column. This column is currently newly opened with an average quality score of 98. In the future, I will reproduce the paper based on various latest cutting-edge conferences, and will also make some old improvements. Mechanism to supplement, Currently this column is free to read (for the time being, everyone should pay attention as soon as possible to avoid getting lost~), if you think this article has helped you, subscribe to this column , pay attention to more updates in the future~
