YOLOv5🚀 reproduction by Guo Quanhao using PaddlePaddle

Related tags

Deep LearningYOLOv5-Paddle
Overview

YOLOv5-Paddle

YOLOv5 🚀 reproduction by Guo Quanhao using PaddlePaddle

Readme Card

  • 支持AutoBatch
  • 支持AutoAnchor
  • 支持GPU Memory

快速开始

使用AIStudio高性能环境快速构建YOLOv5训练(PaddlePaddle2.2.0-gpu version)

需要安装额外模块

pip install gputil==1.4.0
pip install pycocotools
COCO数据集

数据集已挂载至aistudio项目中，如果需要本地训练可以从这里下载数据集，和标签文件

Data
|-- coco
|   |-- annotions
|   |-- images
|      |-- train2017
|      |-- val2017
|      |-- test2017
|   |-- labels
|      |-- train2017
|      |-- val2017
|      |-- train2017.cache(初始解压可删除，训练时会自动生成)
|      |-- val2017.cache(初始解压可删除，训练时会自动生成)
|   |-- test-dev2017.txt
|   |-- val2017.txt
|   |-- train2017.txt
`   `-- validation

修改data/coco.yaml配置自己的coco路径，你可能需要修改path变量

path: /home/aistudio/Data/coco  # dataset root dir

训练

  • 考虑到AIStudio对于github的访问速度，预先提供了Arial.ttf

  • AIStudio后端不支持绘图，部分可视乎在AIStudio仓库被注释

training scratch for coco

mkdir /home/aistudio/.config/QuanhaoGuo/
cp /home/aistudio/Arial.ttf /home/aistudio/.config/QuanhaoGuo/
cd YOLOv5-Paddle
python train.py --img 896 --batch 8 --epochs 300 --data ./data/coco.yaml --cfg yolov5s.yaml --weights ''

验证

python val.py --img 640  --data ./data/coco.yaml --weights ./weights/yolov5s.pdparams --cfg yolov5s.yaml

通过--task [val/test]控制验证集和测试集

所有提供的模型验证精度如下，本仓库的所有资源文件包括预训练模型均可在百度云盘下载code:dng9

Model size
(pixels)		 mAPval
0.5:0.95		 mAPval
0.5		 params
(M)		 FLOPs
@640 (B)		 mAPtest
0.5:0.95		 mAPtest
0.5
YOLOv5n 640 28.4 46.5 1.9 4.5 28.1 46.2
YOLOv5s 640 37.2 56.4 7.2 16.5 37.1 56.1
YOLOv5m 640 45.1 64.2 21.2 49.0 45.4 64.3
YOLOv5l 640 48.6 67.4 46.5 109.1 48.9 67.5
YOLOv5x 640 50.6 69.1 86.7 205.7 0.507 0.690
YOLOv5n6 1280 34.0 51.1 3.2 4.6 34.3 51.7
YOLOv5s6 1280 44.5 63.4 16.8 12.6 44.3 63.0
YOLOv5m6 1280 50.9 69.4 35.7 50.0 51.1 69.5
YOLOv5l6 1280 53.5 71.8 76.8 111.4 53.7 71.8
YOLOv5x6
+ [TTA][TTA]		 1280
1536		 54.6
55.2		 72.6
73.0		 140.7
-		 209.8
-		 55.0
55.8		 73.0
73.5

使用本地环境快速构建YOLOv5训练(PaddlePaddle2.2.0-gpu version)

git clone https://github.com/GuoQuanhao/YOLOv5-Paddle

然后按照使用AIStudio高性能环境快速构建YOLOv5训练执行

训练Custom Data

这里以一个类别的光栅数据集为例，数据集已上传至AIStudio

其组织结构如下：

Data
|-- guangshan
|   |-- images
|      |-- train
|      |-- val
|   |-- labels
|      |-- train
|      |-- val
|   |-- val.txt
|   |-- train.txt

另外你需要构建data/guangshan.yaml，相关文件已放入相关目录，主要用于指定数据集读取路径和模型配置。

# YOLOv5 reproduction 🚀 by GuoQuanhao

train: /home/aistudio/guangshan/images/train  # 118287 images
val: /home/aistudio/guangshan/images/val  # 5000 images
# number of classes
nc: 1
# class names
names: ['spectrum']

训练

python train.py --img 640 --batch 16 --epochs 100 --data ./data/guangshan.yaml --cfg yolov5s.yaml --weights ./weights/yolov5s.pdparams
Starting training for 100 epochs...

     Epoch   gpu_mem       box       obj       cls    labels  img_size
      0/99     4.19G    0.1039   0.04733         0        29       640: 100%|████████████████████████████████████████████████████████████████████| 9/9 [01:43<00:00, 11.50s/it]
               Class     Images     Labels          P          R     [email protected] [email protected]:.95: 100%|████████████████████████████████████████████████████| 1/1 [00:06<00:00,  6.64s/it]
                 all         16         29      0.266      0.379      0.226     0.0468

     Epoch   gpu_mem       box       obj       cls    labels  img_size
      1/99     4.41G   0.08177    0.0289         0        37       640: 100%|████████████████████████████████████████████████████████████████████| 9/9 [01:40<00:00, 11.20s/it]
               Class     Images     Labels          P          R     [email protected] [email protected]:.95: 100%|████████████████████████████████████████████████████| 1/1 [00:05<00:00,  5.49s/it]
                 all         16         29      0.462      0.445      0.398      0.109
......

完整的训练日志存在data/training.txt

利用VisualDL可视化训练过程

visualdl --logdir ./runs/train/exp

验证

python val.py --img 640  --data ./data/guangshan.yaml --cfg yolov5s.yaml --weights ./runs/train/exp/weights/best.pdparams

推理

python detect.py --weights ./runs/train/exp/weights/best.pdparams --cfg yolov5s.yaml --data ./data/guangshan.yaml --source ./data/images/guangshan.jpg

TODO

  • Multi-GPU Training ☘️
  • PaddleLite inference 🌟
  • Model to ONNX ⭐

关于作者

姓名 郭权浩
学校 电子科技大学研2020级
研究方向 计算机视觉
主页 Deep Hao的主页
github Deep Hao的github
如有错误，请及时留言纠正，非常蟹蟹！
后续会有更多论文复现系列推出，欢迎大家有问题留言交流学习，共同进步成长！
You might also like...
Torchserve server using a YoloV5 model running on docker with GPU and static batch inference to perform production ready inference.
Torchserve server using a YoloV5 model running on docker with GPU and static batch inference to perform production ready inference.

Yolov5 running on TorchServe (GPU compatible) ! This is a dockerfile to run TorchServe for Yolo v5 object detection model. (TorchServe (PyTorch librar

null 82 Nov 29, 2022
Drone detection using YOLOv5
Drone detection using YOLOv5

This drone detection system uses YOLOv5 which is a family of object detection architectures and we have trained the model on Drone Dataset. Overview I

Tushar Sarkar 27 Dec 20, 2022
Add gui for YoloV5 using PyQt5
Add gui for YoloV5 using PyQt5

HEAD 更新2021.08.16 **添加图片和视频保存功能： 1.图片和视频按照当前系统时间进行命名 2.各自检测结果存放入output文件夹 3.摄像头检测的默认设备序号更改为0，减少调试报错 温馨提示： 1.项目放置在全英文路径下，防止项目报错 2.默认使用cpu进行检测，自

Ruihao Wang 65 Dec 27, 2022
A program to recognize fruits on pictures or videos using yolov5
A program to recognize fruits on pictures or videos using yolov5

Yolov5 Fruits Detector Requirements Either Linux or Windows. We recommend Linux for better performance. Python 3.6+ and PyTorch 1.7+. Installation To

Fateme Zamanian 30 Jan 6, 2023
Various operations like path tracking, counting, etc by using yolov5

Object-tracing-with-YOLOv5 Various operations like path tracking, counting, etc by using yolov5

Pawan Valluri 5 Nov 28, 2022
FaceAnon - Anonymize people in images and videos using yolov5-crowdhuman
FaceAnon - Anonymize people in images and videos using yolov5-crowdhuman

Face Anonymizer Blur faces from image and video files in /input/ folder. Require

null 22 Nov 3, 2022
Yolov5-opencv-cpp-python - Example of using ultralytics YOLO V5 with OpenCV 4.5.4, C++ and Python
Yolov5-opencv-cpp-python - Example of using ultralytics YOLO V5 with OpenCV 4.5.4, C++ and Python

yolov5-opencv-cpp-python Example of performing inference with ultralytics YOLO V

null 183 Jan 9, 2023
Object detection and instance segmentation toolkit based on PaddlePaddle.
Object detection and instance segmentation toolkit based on PaddlePaddle.

Object detection and instance segmentation toolkit based on PaddlePaddle.

null 9.3k Jan 2, 2023
A PaddlePaddle version image model zoo.

Paddle-Image-Models English | 简体中文 A PaddlePaddle version image model zoo. Install Package Install by pip： $ pip install ppim Install by wheel package

AgentMaker 131 Dec 7, 2022
Comments
  • train相关问题

    train相关问题

    作者您好，我在使用您的数据集训练YOLOv5n模型时，出现一个警告： Epoch gpu_mem box obj cls labels img_size 1/99 1.94G 0.09787 0.5162 0 679 640: 100%|███████████████████████████████████████████████████████████████████████| 57/57 [04:48<00:00, 5.05s/it] Class Images Labels P R [email protected] [email protected]:.95: 25%|██████████████▎ | 1/4 [00:17<00:53, 17.96s/it]/mnt/YOLOv5-Paddle-main/utils/loss.py:191: RuntimeWarning: divide by zero encountered in true_divide j = np.maximum(r, 1 / r).max(2) < self.hyp['anchor_t'] # compare Class Images Labels P R [email protected] [email protected]:.95: 100%|█████████████████████████████████████████████████████████| 4/4 [00:50<00:00, 12.56s/it] all 50 7742 0.571 0.664 0.579 0.188

    训练集为450，验证集为50，训练的目标只有一类“钢筋” （label: rebar） 想问一下会影响后续的部署使用吗？

    opened by qiujianchen 0
  • resume掉精度问题该怎么解决呢？

    resume掉精度问题该怎么解决呢？

    您好，我在使用您的代码时发现--resume时精度会下降，并且可能需要训练一些epoch才能恢复到之前的精度，请问该如何做才能做到resume时不掉精度呢（因为AIstudio的GPU每天只有8点算力卡，我所使用的数据集训练一个epoch需要一个小时，只能通过resume来完成整个训练过程） image

    图中的第一个39-45是直接resume的结果

    图中的第二个39-48是我认为之前训练时x['learning_rate']和x['momentum']存在，所以尝试在训练开始前给它们赋上warmup结束时的值，但发现效果并未达到预期 for j, x in enumerate(optimizer._param_groups): x['learning_rate'] = np.interp(nw, [0, nw], [hyp['warmup_bias_lr'] if j == 2 else 0.0, scheduler.base_lr * lf(epoch)]) if 'momentum' in x: x['momentum'] = np.interp(nw, [0, nw], [hyp['warmup_momentum'], hyp['momentum']])

    希望能得到您的帮助，谢谢！

    opened by diaoa1900 0
  • ai studio绘图问题

    ai studio绘图问题

    对比了yolov5的源码，绘图部分应该不是ai studio不支持的原因，而是在metrics.py的plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=())函数中，for i, y in enumerate(py.t())编写错误，修改为for i, y in enumerate(py.T)后可以正确绘制PR图

    opened by misaka-network10032 0
Owner
QuanHao Guo
Master at UESTC
QuanHao Guo
GitHub Repository
A Simple Long-Tailed Rocognition Baseline via Vision-Language Model

BALLAD This is the official code repository for A Simple Long-Tailed Rocognition Baseline via Vision-Language Model. Requirements Python3 Pytorch(1.7.

Teli Ma 4 Jan 20, 2022
Deep Crop Rotation

Deep Crop Rotation Paper (to come very soon!) We propose a deep learning approach to modelling both inter- and intra-annual patterns for parcel classi

Félix Quinton 5 Sep 23, 2022
A torch.Tensor-like DataFrame library supporting multiple execution runtimes and Arrow as a common memory format

TorchArrow (Warning: Unstable Prototype) This is a prototype library currently under heavy development. It does not currently have stable releases, an

Facebook Research 536 Jan 06, 2023
Position detection system of mobile robot in the warehouse enviroment

Autonomous-Forklift-System About | GUI | Tests | Starting | License | Author | 🎯 About An application that run the autonomous forklift paletization a

Kamil Goś 1 Nov 24, 2021
FastCover: A Self-Supervised Learning Framework for Multi-Hop Influence Maximization in Social Networks by Anonymous.

FastCover: A Self-Supervised Learning Framework for Multi-Hop Influence Maximization in Social Networks by Anonymous.

0 Apr 02, 2021
The source code for 'Noisy-Labeled NER with Confidence Estimation' accepted by NAACL 2021

Kun Liu*, Yao Fu*, Chuanqi Tan, Mosha Chen, Ningyu Zhang, Songfang Huang, Sheng Gao. Noisy-Labeled NER with Confidence Estimation. NAACL 2021. [arxiv]

30 Nov 12, 2022
A Broader Picture of Random-walk Based Graph Embedding

Random-walk Embedding Framework This repository is a reference implementation of the random-walk embedding framework as described in the paper: A Broa

Zexi Huang 23 Dec 13, 2022
A minimalist environment for decision-making in autonomous driving

highway-env A collection of environments for autonomous driving and tactical decision-making tasks An episode of one of the environments available in

Edouard Leurent 1.6k Jan 07, 2023
Deep learning model, heat map, data prepo

deep learning model, heat map, data prepo

Pamela Dekas 1 Jan 14, 2022
PyTorch implementation of TSception V2 using DEAP dataset

TSception This is the PyTorch implementation of TSception V2 using DEAP dataset in our paper: Yi Ding, Neethu Robinson, Su Zhang, Qiuhao Zeng, Cuntai

Yi Ding 27 Dec 15, 2022
A list of awesome PyTorch scholarship articles, guides, blogs, courses and other resources.

Awesome PyTorch Scholarship Resources A collection of awesome PyTorch and Python learning resources. Contributions are always welcome! Course Informat

Arnas Gečas 302 Dec 03, 2022
RGB-D Local Implicit Function for Depth Completion of Transparent Objects

RGB-D Local Implicit Function for Depth Completion of Transparent Objects [Project Page] [Paper] Overview This repository maintains the official imple

NVIDIA Research Projects 43 Dec 12, 2022
Graph Neural Networks with Keras and Tensorflow 2.

Welcome to Spektral Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2. The main goal of this project is to

Daniele Grattarola 2.2k Jan 08, 2023
PyG (PyTorch Geometric) - A library built upon PyTorch to easily write and train Graph Neural Networks (GNNs)

PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.

PyG 16.5k Jan 08, 2023
Data and codes for ACL 2021 paper: Towards Emotional Support Dialog Systems

Emotional-Support-Conversation Copyright © 2021 CoAI Group, Tsinghua University. All rights reserved. Data and codes are for academic research use onl

126 Dec 21, 2022
The Few-Shot Bot: Prompt-Based Learning for Dialogue Systems

Few-Shot Bot: Prompt-Based Learning for Dialogue Systems This repository includes the dataset, experiments results, and code for the paper: Few-Shot B

Andrea Madotto 103 Dec 28, 2022
External Attention Network

Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks paper : https://arxiv.org/abs/2105.02358 Jittor code will come soon

MenghaoGuo 357 Dec 11, 2022
A PyTorch implementation of "Pathfinder Discovery Networks for Neural Message Passing"

A PyTorch implementation of "Pathfinder Discovery Networks for Neural Message Passing" (WebConf 2021). Abstract In this work we propose Pathfind

Benedek Rozemberczki 49 Dec 01, 2022
(CVPR 2021) Lifting 2D StyleGAN for 3D-Aware Face Generation

Lifting 2D StyleGAN for 3D-Aware Face Generation Official implementation of paper "Lifting 2D StyleGAN for 3D-Aware Face Generation". Requirements You

Yichun Shi 66 Nov 29, 2022
Implementing a simplified copy of Shazam application from scratch using MinHashing and LSH.

Building Shazam from scratch In this repository we tried to implement a simplified copy of the Shazam application able to tell you the name of a song

Arturo Ghinassi 0 Nov 17, 2022