# YOLOv8_Efficient **Repository Path**: liyun00/YOLOv8_Efficient ## Basic Information - **Project Name**: YOLOv8_Efficient - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2023-11-24 - **Last Updated**: 2023-11-24 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Yolov8_Efficient ![](./img/logo.png) 简单高效的使用YOLOv8 [English](README.md) | [简体中文](README.zh-CN.md) --- ![GitHub stars](https://img.shields.io/github/stars/isLinXu/Yolov8_Efficient) ![GitHub forks](https://img.shields.io/github/forks/isLinXu/Yolov8_Efficient) ![GitHub watchers](https://img.shields.io/github/watchers/isLinXu/Yolov8_Efficient) [![Build Status](https://img.shields.io/endpoint.svg?url=https%3A%2F%2Factions-badge.atrox.dev%2Fatrox%2Fsync-dotenv%2Fbadge&style=flat)](https://github.com/isLinXu/Yolov8_Efficient) ![img](https://badgen.net/badge/icon/learning?icon=deepscan&label)![GitHub repo size](https://img.shields.io/github/repo-size/isLinXu/Yolov8_Efficient.svg?style=flat-square) ![GitHub language count](https://img.shields.io/github/languages/count/isLinXu/Yolov8_Efficient) ![GitHub last commit](https://img.shields.io/github/last-commit/isLinXu/Yolov8_Efficient) ![GitHub](https://img.shields.io/github/license/isLinXu/Yolov8_Efficient.svg?style=flat-square)![img](https://hits.dwyl.com/isLinXu/Yolov8_Efficient.svg) ## 😎 介绍 这是一个由独立开发人员维护的非官方存储库，用于基于 ultralytics v8 Weights 和 ultralytics Project 的学习和交流。如果大家有更多的问题和想法，欢迎大家一起讨论。 另外，ultralytics已发布了最新的[ultralytics](https://github.com/ultralytics/ultralytics)仓库，建议优先使用官方的。 本项目基于ultralytics及yolov5等进行综合参考，致力于让yolo系列的更加高效和易用。 目前主要做了以下的工作： - 参考https://docs.ultralytics.com/config/中的Configuration参数，分别针对train.py、detect.py、val.py等做了相应参数的配置对齐。 > ![config_1](img/config_1.png) - 结合yolov5的使用习惯以及代码结构做了兼容和优化。 > ![work](img/work.png) > > ![work](img/work.png) - 通过在coco数据集上在自己的机器上进行验证和计算的权重的指标参数，实验记录存放在https://github.com/isLinXu/YOLOv8_Efficient/tree/main/log.实验数据记录在: > ![log](img/log.png) > > ![model_metrics_data](img/model_metrics_data.png) - 根据计算出来的结果绘制了相应的指标参数对比图，这个绘图程序也开源在https://github.com/isLinXu/model-metrics-plot中。 > ![model_metrics_plot](img/model_metrics_plot.png) - 融合其他更多网络模型结构进行集成整合和配置，正在进行中... ## 🥰展示 ### 指标 ![](./img/plot_metrics.jpg) ### 网络结构图 ![](./img/v8_structure.jpg) > 这里感谢[集智书童](https://github.com/jizhishutong)为本项目提供的网络结构图 ![](./img/demo.png) - wandb训练日志: [log](https://wandb.ai/glenn-jocher/YOLOv8) - 实验日志: [log](https://github.com/isLinXu/YOLOv8_Efficient/tree/main/log) ## 🆕新闻! --- - ... ... - 2023/01/16 - add train_detect, train_cls and train_seg - 2023/01/11 - add metrics plot and model structure - 2023/01/10 - add yolov8 metrics and logs - 2023/01/09 - add val.py and fix some error - 2023/01/07 - fix some error and warning - 2023/01/06 - add train.py, detect.py and README.md - 2023/01/06 - Create and Init a new repository ## 🤔 任务清单： - [x] 模型测试和验证中 - [ ] ## 🧙‍快速开始 - **文档** [**Ultralytics YOLO Docs**](https://docs.ultralytics.com/) - [ultralytics assets releases](https://github.com/ultralytics/assets/releases/) ### 1.命令行执行 简单地使用最新的 Ultralytics YOLO 模型 ```bash yolo task=detect mode=train model=yolov8n.yaml args=... classify predict yolov8n-cls.yaml args=... segment val yolov8n-seg.yaml args=... export yolov8n.pt format=onnx ``` ### 2. Python SDK 使用 Ultralytics YOLO 模型的 pythonic 接口 ```python from ultralytics import YOLO model = YOLO("yolov8n.yaml") # create a new model from scratch model = YOLO( "yolov8n.pt" ) # load a pretrained model (recommended for best training results) results = model.train(data="coco128.yaml", epochs=100, imgsz=640, ...) results = model.val() results = model.predict(source="bus.jpg") success = model.export(format="onnx") ``` 如果您希望为研发修改 YOLO 或在其之上构建，请参阅文档https://docs.ultralytics.com/。 ### 3.训练自己的数据集 这里以coco128为例。 - 1.制作YOLO格式的数据集，可以通过项目目录中的`prepare_data.py`来进行数据集的划分与转换。 - 2.修改`config`中相应模型权重的`.yaml`，配置其数据集路径，读入数据加载器。 - 3.修改model中相应参数，主要是修改类别数量和网络结构参数，如果只是简单应用，那么不建议修改下面的网络结构参数，只修改其类别数量即可。 - 4.运行train.py，这一步可以到`parse_opt`中对应的变量下进行修改，需要根据设备和训练的需要进行配置，主要包括`device`、`task`、`data`、`weights`、`epochs`、`batch_size`等，如果不配置则使用默认参数。 ## 🧙‍预训练检查点 | Model | size (pixels) | mAPval 50-95 | mAPval 50 | Speed CPU b1 (ms) | Speed RTX 3080 b1(ms) | layers | params (M) | FLOPs @640 (B) | | :----------------------------------------------------------: | :-----------: | :----------: | :-------: | :---------------: | :-------------------: | :----: | :--------: | :------------: | | [yolov8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640 | 37.2 | 53.2 | 47.2 | 5.6 | 168 | 3.15 | 8.7 | | [yolov8n-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640 | 30.7 | 50.0 | 59.3 | 11.3 | 195 | 3.40 | 12.6 | | [yolov8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640 | 44.7 | 62.2 | 87.9 | 5.7 | 168 | 11.15 | 28.6 | | [yolov8s-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640 | 37.0 | 58.8 | 107.6 | 11.4 | 195 | 11.81 | 42.6 | | [yolov8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640 | 49.9 | 67.4 | 185.6 | 8.3 | 218 | 25.89 | 78.9 | | [yolov8m-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640 | 40.6 | 63.5 | 207.7 | 15.3 | 245 | 27.27 | 110.2 | | [yolov8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.4 | 69.9 | 319.6 | 13.1 | 268 | 43.67 | 165.2 | | [yolov8l-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 42.5 | 66.1 | 296.9 | 16.8 | 295 | 45.97 | 220.5 | | [yolov8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.5 | 70.9 | 334.6 | 20.4 | 268 | 68.20 | 257.8 | | [yolov8x-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 43.2 | 67.1 | 418.8 | 23.8 | 295 | 71.80 | 344.1 | > - **表格注释** 以上数据是在以下配置的环境中运行测试生成的。详情见下文。 > - 显卡：NVIDIA GeForce RTX 3080/PCIe/SSE2 > - CPU：Intel® Core™ i9-10900K CPU @ 3.70GHz × 20 > - 内存：31.3 GiB > - 系统：Ubuntu 18.04 LTS > - (ms): 这里的统计速度是推理速度 ## 安装 ### 直接安装 ```bash pip install ultralytics ``` ### 配置安装 ```shell git clone git@github.com:isLinXu/YOLOv8_Efficient.git cd YOLOv8_Efficient cd ultralytics-master pip install -e . ``` ![](./img/install_img.png) ## 🔨用法 ### 训练 - 单 GPU 训练: ```shell python train.py --data coco128.yaml --weights weights/yolov8ns.pt --img 640 # from pretrained (recommended) ``` ```python python train.py --data coco128.yaml --weights '' --cfg yolov8ns.yaml --img 640 # from scratch ``` > 使用 IDE Pycharm > > ![](./img/pycharm_run_train.png) - 多 GPU DDP 训练： ```shell python -m torch.distributed.run --nproc_per_node 4 --master_port 1 train.py --data coco128.yaml --weights yolov8ns.pt --img 640 --device 0,1,2,3 ``` ​ ### 推理检测 ```shell python detect.py --weights yolov8s.pt --source 0 # webcam img.jpg # image vid.mp4 # video screen # screenshot path/ # directory list.txt # list of images list.streams # list of streams 'path/*.jpg' # glob 'https://youtu.be/Zgi9g1ksQHc' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream ``` > 使用 IDE Pycharm > > ![](./img/pycharm_run_detect.png) ### 验证 - 以coco128为例: >| ![](./img/val_batch1_pred.jpg) | ![](./img/F1_curve.png) | ![](./img/P_curve.png) | > | ------------------------------ | ----------------------- | ------------------------------- | >| ![](./img/PR_curve.png) | ![](./img/R_curve.png) | ![](./img/confusion_matrix.png) | #### 用法: ```shell python val.py --weights yolov8n.pt --data coco128.yaml --img 640 ``` #### 用法 - 格式: ```shell python val.py --weights yolov8s.pt # PyTorch yolov8s.torchscript # TorchScript yolov8s.onnx # ONNX Runtime or OpenCV DNN with --dnn yolov8s_openvino_model # OpenVINO yolov8s.engine # TensorRT yolov8s.mlmodel # CoreML (macOS-only) yolov8s_saved_model # TensorFlow SavedModel yolov8s.pb # TensorFlow GraphDef yolov8s.tflite # TensorFlow Lite yolov8s_edgetpu.tflite # TensorFlow Edge TPU yolov8s_paddle_model # PaddlePaddle ``` > 使用 IDE Pycharm > ![](./img/pycharm_run_val.png) ## 🌹致谢 - [https://github.com/ultralytics/yolov3](https://github.com/ultralytics/yolov3) - [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5) - https://github.com/meituan/YOLOv6 - https://github.com/WongKinYiu/yolov7