monkey_cici/PaddleYOLO (2024)

简体中文 | English

简介

PaddleYOLO是基于PaddleDetection的YOLO系列模型库,只包含YOLO系列模型的相关代码,支持YOLOv3PP-YOLOPP-YOLOv2PP-YOLOEPP-YOLOE+YOLOXYOLOv5YOLOv6YOLOv7YOLOv8YOLOv5uYOLOv7uRTMDet等模型,COCO数据集模型库请参照 ModelZooconfigs

monkey_cici/PaddleYOLO (1) monkey_cici/PaddleYOLO (2)

注意:

  • PaddleYOLO 代码库协议为 GPL 3.0YOLOv5YOLOv6YOLOv7YOLOv8这几类模型代码不合入PaddleDetection,其余YOLO模型推荐在PaddleDetection中使用,会最先发布PP-YOLO系列特色检测模型的最新进展
  • PaddleYOLO代码库推荐使用paddlepaddle-2.3.2以上的版本,请参考官网下载对应适合版本,Windows平台请安装paddle develop版本
  • PaddleYOLO 的Roadmap issue用于收集用户的需求,欢迎提出您的建议和需求;

教程

安装

Clone 代码库和安装 requirements.txt,环境需要在一个Python>=3.7.0 下的环境,且需要安装PaddlePaddle>=2.3.2

git clone https://github.com/PaddlePaddle/PaddleYOLO # clonecd PaddleYOLOpip install -r requirements.txt # install
训练/验证/预测/将以下命令写在一个脚本文件里如```run.sh```,一键运行命令为:```sh run.sh```,也可命令行一句句去运行。
model_name=ppyoloe # 可修改,如 yolov7job_name=ppyoloe_plus_crn_s_80e_coco # 可修改,如 yolov7_tiny_300e_cococonfig=configs/${model_name}/${job_name}.ymllog_dir=log_dir/${job_name}# weights=https://bj.bcebos.com/v1/paddledet/models/${job_name}.pdparamsweights=output/${job_name}/model_final.pdparams# 1.训练(单卡/多卡),加 --eval 表示边训边评估,加 --amp 表示混合精度训练# CUDA_VISIBLE_DEVICES=0 python tools/train.py -c ${config} --eval --amppython -m paddle.distributed.launch --log_dir=${log_dir} --gpus 0,1,2,3,4,5,6,7 tools/train.py -c ${config} --eval --amp# 2.评估,加 --classwise 表示输出每一类mAPCUDA_VISIBLE_DEVICES=0 python tools/eval.py -c ${config} -o weights=${weights} --classwise# 3.预测 (单张图/图片文件夹)CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c ${config} -o weights=${weights} --infer_img=demo/000000014439_640x640.jpg --draw_threshold=0.5# CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c ${config} -o weights=${weights} --infer_dir=demo/ --draw_threshold=0.5
部署/测速

将以下命令写在一个脚本文件里如run.sh,一键运行命令为:sh run.sh,也可命令行一句句去运行。

model_name=ppyoloe # 可修改,如 yolov7job_name=ppyoloe_plus_crn_s_80e_coco # 可修改,如 yolov7_tiny_300e_cococonfig=configs/${model_name}/${job_name}.ymllog_dir=log_dir/${job_name}# weights=https://bj.bcebos.com/v1/paddledet/models/${job_name}.pdparamsweights=output/${job_name}/model_final.pdparams# 4.导出模型,以下3种模式选一种## 普通导出,加trt表示用于trt加速,对NMS和silu激活函数提速明显CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c ${config} -o weights=${weights} # trt=True## exclude_post_process去除后处理导出,返回和YOLOv5导出ONNX时相同格式的concat后的1个Tensor,是未缩放回原图的坐标+分类置信度# CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c ${config} -o weights=${weights} exclude_post_process=True # trt=True## exclude_nms去除NMS导出,返回2个Tensor,是缩放回原图后的坐标和分类置信度# CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c ${config} -o weights=${weights} exclude_nms=True # trt=True# 5.部署预测,注意不能使用 去除后处理 或 去除NMS 导出后的模型去预测CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/${job_name} --image_file=demo/000000014439_640x640.jpg --device=GPU# 6.部署测速,加 “--run_mode=trt_fp16” 表示在TensorRT FP16模式下测速,注意如需用到 trt_fp16 则必须为加 trt=True 导出的模型CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/${job_name} --image_file=demo/000000014439_640x640.jpg --device=GPU --run_benchmark=True # --run_mode=trt_fp16# 7.onnx导出,一般结合 exclude_post_process去除后处理导出的模型paddle2onnx --model_dir output_inference/${job_name} --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 12 --save_file ${job_name}.onnx# 8.onnx trt测速/usr/local/TensorRT-8.0.3.4/bin/trtexec --onnx=${job_name}.onnx --workspace=4096 --avgRuns=10 --shapes=input:1x3x640x640 --fp16/usr/local/TensorRT-8.0.3.4/bin/trtexec --onnx=${job_name}.onnx --workspace=4096 --avgRuns=10 --shapes=input:1x3x640x640 --fp32
  • 如果想切换模型,只要修改开头两行即可,如:
    model_name=yolov7job_name=yolov7_tiny_300e_coco
  • 导出onnx,首先安装Paddle2ONNXpip install paddle2onnx
  • 统计FLOPs(G)和Params(M),首先安装PaddleSlimpip install paddleslim,然后设置runtime.ymlprint_flops: Trueprint_params: True,并且注意确保是单尺度下如640x640,打印的是MACs,FLOPs=2*MACs
[训练自定义数据集](https://github.com/PaddlePaddle/PaddleYOLO/issues/43)
  • 请参照文档issue
  • PaddleDetection团队提供了基于PP-YOLOE的各种垂类检测模型的配置文件和权重,用户也可以作为参考去使用自定义数据集。请参考 PP-YOLOE applicationpphumanppvehiclevisdronesmalldet
  • PaddleDetection团队也提供了VOC数据集的各种YOLO模型的配置文件和权重,用户也可以作为参考去使用自定义数据集。请参考 voc
  • 训练自定义数据集之前请先确保加载了对应COCO权重作为预训练,将配置文件中的pretrain_weights: 设置为对应COCO模型训好的权重,一般会提示head分类层卷积的通道数没对应上,属于正常现象,是由于自定义数据集一般和COCO数据集种类数不一致;
  • YOLO检测模型建议batch_size至少大于64去训练,如果资源不够请换小模型减小模型的输入尺度,为了保障较高检测精度,尽量不要尝试单卡训和总batch_size小于64

更新日志

  • 【2023/03/13】支持YOLOv5uYOLOv7u预测和部署;
  • 【2023/01/10】支持YOLOv8预测和部署;
  • 【2022/09/29】支持RTMDet预测和部署;
  • 【2022/09/26】发布PaddleYOLO模型套件,请参照ModelZoo
  • 【2022/09/19】支持YOLOv6新版,包括n/t/s/m/l模型;
  • 【2022/08/23】发布YOLOSeries代码库: 支持YOLOv3,PP-YOLOE,PP-YOLOE+,YOLOX,YOLOv5,YOLOv6,YOLOv7等YOLO模型,支持ConvNeXt骨干网络高精度版PP-YOLOE,YOLOXYOLOv5等模型,支持PaddleSlim无损加速量化训练PP-YOLOE,YOLOv5,YOLOv6YOLOv7等模型,详情可阅读此文章

monkey_cici/PaddleYOLO (3) 产品动态

  • 🔥 2023.3.14:PaddleYOLO发布release/2.6版本

    • 💡 模型套件:
      • 支持YOLOv8,YOLOv5u,YOLOv7u等YOLO模型预测和部署;
      • 支持Swin-TransformerViTFocalNet骨干网络高精度版PP-YOLOE+等模型;
      • 支持YOLOv8FastDeploy中多硬件快速部署;
  • 🔥 2022.9.26:PaddleYOLO发布release/2.5版本

    • 💡 模型套件:
      • 发布PaddleYOLO模型套件: 支持YOLOv3,PP-YOLOE,PP-YOLOE+,YOLOX,YOLOv5,YOLOv6,YOLOv7等YOLO模型,支持ConvNeXt骨干网络高精度版PP-YOLOE,YOLOXYOLOv5等模型,支持PaddleSlim无损加速量化训练PP-YOLOE,YOLOv5,YOLOv6YOLOv7等模型;
  • 🔥 2022.8.26:PaddleDetection发布release/2.5版本

    • 🗳 特色模型:
      • 发布PP-YOLOE+,最高精度提升2.4% mAP,达到54.9% mAP,模型训练收敛速度提升3.75倍,端到端预测速度最高提升2.3倍;多个下游任务泛化性提升
      • 发布PicoDet-NPU模型,支持模型全量化部署;新增PicoDet版面分析模型
      • 发布PP-TinyPose升级版增强版,在健身、舞蹈等场景精度提升9.1% AP,支持侧身、卧躺、跳跃、高抬腿等非常规动作
    • 🔮 场景能力:
      • 发布行人分析工具PP-Human v2,新增打架、打电话、抽烟、闯入四大行为识别,底层算法性能升级,覆盖行人检测、跟踪、属性三类核心算法能力,提供保姆级全流程开发及模型优化策略,支持在线视频流输入
      • 首次发布PP-Vehicle,提供车牌识别、车辆属性分析(颜色、车型)、车流量统计以及违章检测四大功能,兼容图片、在线视频流、视频输入,提供完善的二次开发文档教程
    • 💡 前沿算法:
      • 全面覆盖的YOLO家族经典与最新模型: 包括YOLOv3,百度飞桨自研的实时高精度目标检测检测模型PP-YOLOE,以及前沿检测算法YOLOv4、YOLOv5、YOLOX,YOLOv6及YOLOv7
      • 新增基于ViT骨干网络高精度检测模型,COCO数据集精度达到55.7% mAP;新增OC-SORT多目标跟踪模型;新增ConvNeXt骨干网络
    • 📋 产业范例:新增智能健身打架识别来客分析、车辆结构化范例
  • 2022.3.24:PaddleDetection发布release/2.4版本

    • 发布高精度云边一体SOTA目标检测模型PP-YOLOE,提供s/m/l/x版本,l版本COCO test2017数据集精度51.6%,V100预测速度78.1 FPS,支持混合精度训练,训练较PP-YOLOv2加速33%,全系列多尺度模型,满足不同硬件算力需求,可适配服务器、边缘端GPU及其他服务器端AI加速卡。
    • 发布边缘端和CPU端超轻量SOTA目标检测模型PP-PicoDet增强版,精度提升2%左右,CPU预测速度提升63%,新增参数量0.7M的PicoDet-XS模型,提供模型稀疏化和量化功能,便于模型加速,各类硬件无需单独开发后处理模块,降低部署门槛。
    • 发布实时行人分析工具PP-Human,支持行人跟踪、人流量统计、人体属性识别与摔倒检测四大能力,基于真实场景数据特殊优化,精准识别各类摔倒姿势,适应不同环境背景、光线及摄像角度。
    • 新增YOLOX目标检测模型,支持nano/tiny/s/m/l/x版本,x版本COCO val2017数据集精度51.8%。
  • 更多版本发布

monkey_cici/PaddleYOLO (4) 简介

PaddleDetection为基于飞桨PaddlePaddle的端到端目标检测套件,内置30+模型算法250+预训练模型,覆盖目标检测、实例分割、跟踪、关键点检测等方向,其中包括服务器端和移动端高精度、轻量级产业级SOTA模型、冠军方案和学术前沿算法,并提供配置化的网络模块组件、十余种数据增强策略和损失函数等高阶优化支持和多种部署方案,在打通数据处理、模型开发、训练、压缩、部署全流程的基础上,提供丰富的案例及教程,加速算法产业落地应用。

monkey_cici/PaddleYOLO (5)

monkey_cici/PaddleYOLO (6) 特性

  • 模型丰富: 包含目标检测实例分割人脸检测关键点检测多目标跟踪250+个预训练模型,涵盖多种全球竞赛冠军方案。
  • 使用简洁:模块化设计,解耦各个网络组件,开发者轻松搭建、试用各种检测模型及优化策略,快速得到高性能、定制化的算法。
  • 端到端打通: 从数据增强、组网、训练、压缩、部署端到端打通,并完备支持云端/边缘端多架构、多设备部署。
  • 高性能: 基于飞桨的高性能内核,模型训练速度及显存占用优势明显。支持FP16训练, 支持多机训练。

monkey_cici/PaddleYOLO (7)

monkey_cici/PaddleYOLO (8) 技术交流

  • 如果你发现任何PaddleDetection存在的问题或者是建议, 欢迎通过GitHub Issues给我们提issues。

  • 欢迎加入PaddleDetection 微信用户群(扫码填写问卷即可入群)

    • 入群福利 💎:获取PaddleDetection团队整理的重磅学习大礼包🎁
      • 📊 福利一:获取飞桨联合业界企业整理的开源数据集
      • 👨‍🏫 福利二:获取PaddleDetection历次发版直播视频与最新直播咨询
      • 🗳 福利三:获取垂类场景预训练模型集合,包括工业、安防、交通等5+行业场景
      • 🗂 福利四:获取10+全流程产业实操范例,覆盖火灾烟雾检测、人流量计数等产业高频场景

    monkey_cici/PaddleYOLO (9)

monkey_cici/PaddleYOLO (10) 套件结构概览

Architectures Backbones Components Data Augmentation
    Object Detection
    • YOLOv3
    • YOLOv5
    • YOLOv6
    • YOLOv7
    • YOLOv8
    • PP-YOLOv1/v2
    • PP-YOLO-Tiny
    • PP-YOLOE
    • PP-YOLOE+
    • YOLOX
    • RTMDet
Details
  • ResNet(&vd)
  • CSPResNet
  • DarkNet
  • CSPDarkNet
  • ConvNeXt
  • EfficientRep
  • CSPBepBackbone
  • ELANNet
  • CSPNeXt
Common
  • Sync-BN
  • Group Norm
  • DCNv2
  • EMA
FPN
  • YOLOv3FPN
  • PPYOLOFPN
  • PPYOLOTinyFPN
  • PPYOLOPAN
  • YOLOCSPPAN
  • Custom-PAN
  • RepPAN
  • CSPRepPAN
  • ELANFPN
  • ELANFPNP6
  • CSPNeXtPAFPN
Loss
  • Smooth-L1
  • GIoU/DIoU/CIoU
  • IoUAware
  • Focal Loss
  • VariFocal Loss
Post-processing
  • SoftNMS
  • MatrixNMS
Speed
  • FP16 training
  • Multi-machine training
Details
  • Resize
  • Lighting
  • Flipping
  • Expand
  • Crop
  • Color Distort
  • Random Erasing
  • Mixup
  • AugmentHSV
  • Mosaic
  • Cutmix
  • Grid Mask
  • Auto Augment
  • Random Perspective

monkey_cici/PaddleYOLO (11) 模型性能概览

云端模型性能对比

各模型结构和骨干网络的代表模型在COCO数据集上精度mAP和单卡Tesla V100上预测速度(FPS)对比图。

monkey_cici/PaddleYOLO (12)

说明:

  • PP-YOLOE是对PP-YOLO v2模型的进一步优化,在COCO数据集精度51.6%,Tesla V100预测速度78.1FPS
  • PP-YOLOE+是对PPOLOE模型的进一步优化,在COCO数据集精度53.3%,Tesla V100预测速度78.1FPS
  • 图中模型均可在模型库中获取
移动端模型性能对比

各移动端模型在COCO数据集上精度mAP和高通骁龙865处理器上预测速度(FPS)对比图。

monkey_cici/PaddleYOLO (13)

说明:

  • 测试数据均使用高通骁龙865(4*A77 + 4*A55)处理器batch size为1, 开启4线程测试,测试使用NCNN预测库,测试脚本见MobileDetBenchmark
  • PP-PicoDetPP-YOLO-Tiny为PaddleDetection自研模型,其余模型PaddleDetection暂未提供

monkey_cici/PaddleYOLO (14) 模型库

1. 通用检测

PP-YOLOE+系列 推荐场景:Nvidia V100, T4等云端GPU和Jetson系列等边缘端设备

模型名称COCO精度(mAP)V100 TensorRT FP16速度(FPS)配置文件模型下载
PP-YOLOE+_s43.9333.3链接下载地址
PP-YOLOE+_m50.0208.3链接下载地址
PP-YOLOE+_l53.3149.2链接下载地址
PP-YOLOE+_x54.995.2链接下载地址

前沿检测算法

模型名称COCO精度(mAP)V100 TensorRT FP16速度(FPS)配置文件模型下载
YOLOX-l50.1107.5链接下载地址
YOLOv5-l48.6136.0链接下载地址
YOLOv7-l51.0135.0链接下载地址

monkey_cici/PaddleYOLO (15) 文档教程

入门教程

  • 安装说明
  • 快速体验
  • 数据准备
  • PaddleDetection全流程使用
  • FAQ/常见问题汇总

进阶教程

  • 参数配置

    • PP-YOLO参数说明
  • 模型压缩(基于PaddleSlim)

    • 剪裁/量化/蒸馏教程
  • 推理部署

    • 模型导出教程
    • Paddle Inference部署
      • Python端推理部署
      • C++端推理部署
    • Paddle-Lite部署
    • Paddle Serving部署
    • ONNX模型导出
    • 推理benchmark
  • 进阶开发

    • 数据处理模块
    • 新增检测模型
    • 二次开发教程
      • 目标检测

monkey_cici/PaddleYOLO (16) 版本更新

版本更新内容请参考版本更新文档

monkey_cici/PaddleYOLO (17) 许可证书

本项目的发布受GPL-3.0 license许可认证。

monkey_cici/PaddleYOLO (18) 引用

@misc{ppdet2019,title={PaddleDetection, Object detection and instance segmentation toolkit based on PaddlePaddle.},author={PaddlePaddle Authors},howpublished = {\url{https://github.com/PaddlePaddle/PaddleDetection}},year={2019}}

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Moreover, your license from a particular copyright holder isreinstated permanently if the copyright holder notifies you of theviolation by some reasonable means, this is the first time you havereceived notice of violation of this License (for any work) from thatcopyright holder, and you cure the violation prior to 30 days afteryour receipt of the notice. Termination of your rights under this section does not terminate thelicenses of parties who have received copies or rights from you underthis License. If your rights have been terminated and not permanentlyreinstated, you do not qualify to receive new licenses for the samematerial under section 10. 9. Acceptance Not Required for Having Copies. You are not required to accept this License in order to receive orrun a copy of the Program. Ancillary propagation of a covered workoccurring solely as a consequence of using peer-to-peer transmissionto receive a copy likewise does not require acceptance. However,nothing other than this License grants you permission to propagate ormodify any covered work. These actions infringe copyright if you donot accept this License. Therefore, by modifying or propagating acovered work, you indicate your acceptance of this License to do so. 10. Automatic Licensing of Downstream Recipients. Each time you convey a covered work, the recipient automaticallyreceives a license from the original licensors, to run, modify andpropagate that work, subject to this License. You are not responsiblefor enforcing compliance by third parties with this License. An "entity transaction" is a transaction transferring control of anorganization, or substantially all assets of one, or subdividing anorganization, or merging organizations. If propagation of a coveredwork results from an entity transaction, each party to thattransaction who receives a copy of the work also receives whateverlicenses to the work the party's predecessor in interest had or couldgive under the previous paragraph, plus a right to possession of theCorresponding Source of the work from the predecessor in interest, ifthe predecessor has it or can get it with reasonable efforts. You may not impose any further restrictions on the exercise of therights granted or affirmed under this License. For example, you maynot impose a license fee, royalty, or other charge for exercise ofrights granted under this License, and you may not initiate litigation(including a cross-claim or counterclaim in a lawsuit) alleging thatany patent claim is infringed by making, using, selling, offering forsale, or importing the Program or any portion of it. 11. Patents. A "contributor" is a copyright holder who authorizes use under thisLicense of the Program or a work on which the Program is based. Thework thus licensed is called the contributor's "contributor version". A contributor's "essential patent claims" are all patent claimsowned or controlled by the contributor, whether already acquired orhereafter acquired, that would be infringed by some manner, permittedby this License, of making, using, or selling its contributor version,but do not include claims that would be infringed only as aconsequence of further modification of the contributor version. Forpurposes of this definition, "control" includes the right to grantpatent sublicenses in a manner consistent with the requirements ofthis License. Each contributor grants you a non-exclusive, worldwide, royalty-freepatent license under the contributor's essential patent claims, tomake, use, sell, offer for sale, import and otherwise run, modify andpropagate the contents of its contributor version. In the following three paragraphs, a "patent license" is any expressagreement or commitment, however denominated, not to enforce a patent(such as an express permission to practice a patent or covenant not tosue for patent infringement). To "grant" such a patent license to aparty means to make such an agreement or commitment not to enforce apatent against the party. If you convey a covered work, knowingly relying on a patent license,and the Corresponding Source of the work is not available for anyoneto copy, free of charge and under the terms of this License, through apublicly available network server or other readily accessible means,then you must either (1) cause the Corresponding Source to be soavailable, or (2) arrange to deprive yourself of the benefit of thepatent license for this particular work, or (3) arrange, in a mannerconsistent with the requirements of this License, to extend the patentlicense to downstream recipients. "Knowingly relying" means you haveactual knowledge that, but for the patent license, your conveying thecovered work in a country, or your recipient's use of the covered workin a country, would infringe one or more identifiable patents in thatcountry that you have reason to believe are valid. If, pursuant to or in connection with a single transaction orarrangement, you convey, or propagate by procuring conveyance of, acovered work, and grant a patent license to some of the partiesreceiving the covered work authorizing them to use, propagate, modifyor convey a specific copy of the covered work, then the patent licenseyou grant is automatically extended to all recipients of the coveredwork and works based on it. A patent license is "discriminatory" if it does not include withinthe scope of its coverage, prohibits the exercise of, or isconditioned on the non-exercise of one or more of the rights that arespecifically granted under this License. You may not convey a coveredwork if you are a party to an arrangement with a third party that isin the business of distributing software, under which you make paymentto the third party based on the extent of your activity of conveyingthe work, and under which the third party grants, to any of theparties who would receive the covered work from you, a discriminatorypatent license (a) in connection with copies of the covered workconveyed by you (or copies made from those copies), or (b) primarilyfor and in connection with specific products or compilations thatcontain the covered work, unless you entered into that arrangement,or that patent license was granted, prior to 28 March 2007. Nothing in this License shall be construed as excluding or limitingany implied license or other defenses to infringement that mayotherwise be available to you under applicable patent law. 12. No Surrender of Others' Freedom. If conditions are imposed on you (whether by court order, agreement orotherwise) that contradict the conditions of this License, they do notexcuse you from the conditions of this License. If you cannot convey acovered work so as to satisfy simultaneously your obligations under thisLicense and any other pertinent obligations, then as a consequence you maynot convey it at all. For example, if you agree to terms that obligate youto collect a royalty for further conveying from those to whom you conveythe Program, the only way you could satisfy both those terms and thisLicense would be to refrain entirely from conveying the Program. 13. Use with the GNU Affero General Public License. Notwithstanding any other provision of this License, you havepermission to link or combine any covered work with a work licensedunder version 3 of the GNU Affero General Public License into a singlecombined work, and to convey the resulting work. The terms of thisLicense will continue to apply to the part which is the covered work,but the special requirements of the GNU Affero General Public License,section 13, concerning interaction through a network will apply to thecombination as such. 14. Revised Versions of this License. The Free Software Foundation may publish revised and/or new versions ofthe GNU General Public License from time to time. Such new versions willbe similar in spirit to the present version, but may differ in detail toaddress new problems or concerns. Each version is given a distinguishing version number. If theProgram specifies that a certain numbered version of the GNU GeneralPublic License "or any later version" applies to it, you have theoption of following the terms and conditions either of that numberedversion or of any later version published by the Free SoftwareFoundation. If the Program does not specify a version number of theGNU General Public License, you may choose any version ever publishedby the Free Software Foundation. If the Program specifies that a proxy can decide which futureversions of the GNU General Public License can be used, that proxy'spublic statement of acceptance of a version permanently authorizes youto choose that version for the Program. Later license versions may give you additional or differentpermissions. However, no additional obligations are imposed on anyauthor or copyright holder as a result of your choosing to follow alater version. 15. Disclaimer of Warranty. THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BYAPPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHTHOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTYOF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULARPURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAMIS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OFALL NECESSARY SERVICING, REPAIR OR CORRECTION. 16. Limitation of Liability. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITINGWILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYSTHE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANYGENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THEUSE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OFDATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRDPARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OFSUCH DAMAGES. 17. Interpretation of Sections 15 and 16. If the disclaimer of warranty and limitation of liability providedabove cannot be given local legal effect according to their terms,reviewing courts shall apply local law that most closely approximatesan absolute waiver of all civil liability in connection with theProgram, unless a warranty or assumption of liability accompanies acopy of the Program in return for a fee. END OF TERMS AND CONDITIONS How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatestpossible use to the public, the best way to achieve this is to make itfree software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safestto attach them to the start of each source file to most effectivelystate the exclusion of warranty; and each file should have at leastthe "copyright" line and a pointer to where the full notice is found. <one line to give the program's name and a brief idea of what it does.> Copyright (C) <year> <name of author> This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.Also add information on how to contact you by electronic and paper mail. If the program does terminal interaction, make it output a shortnotice like this when it starts in an interactive mode: <program> Copyright (C) <year> <name of author> This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details.The hypothetical commands `show w' and `show c' should show the appropriateparts of the General Public License. Of course, your program's commandsmight be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or school,if any, to sign a "copyright disclaimer" for the program, if necessary.For more information on this, and how to apply and follow the GNU GPL, see<https://www.gnu.org/licenses/>. The GNU General Public License does not permit incorporating your programinto proprietary programs. If your program is a subroutine library, youmay consider it more useful to permit linking proprietary applications withthe library. If this is what you want to do, use the GNU Lesser GeneralPublic License instead of this License. But first, please read<https://www.gnu.org/licenses/why-not-lgpl.html>.

monkey_cici/PaddleYOLO (2024)
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