I tried to apply the CAM algorithm to YOLOv4 and it worked.

Overview

YOLOV4:You Only Look Once目标检测模型在pytorch当中的实现


2021年2月7日更新:
加入letterbox_image的选项,关闭letterbox_image后网络的map得到大幅度提升。

目录

  1. 性能情况 Performance
  2. 实现的内容 Achievement
  3. 所需环境 Environment
  4. 注意事项 Attention
  5. 小技巧的设置 TricksSet
  6. 文件下载 Download
  7. 预测步骤 How2predict
  8. 训练步骤 How2train
  9. 参考资料 Reference

性能情况

训练数据集 权值文件名称 测试数据集 输入图片大小 mAP 0.5:0.95 mAP 0.5
VOC07+12+COCO yolo4_voc_weights.pth VOC-Test07 416x416 - 89.0
COCO-Train2017 yolo4_weights.pth COCO-Val2017 416x416 46.1 70.2

实现的内容

  • 主干特征提取网络:DarkNet53 => CSPDarkNet53
  • 特征金字塔:SPP,PAN
  • 训练用到的小技巧:Mosaic数据增强、Label Smoothing平滑、CIOU、学习率余弦退火衰减
  • 激活函数:使用Mish激活函数
  • ……balabla

所需环境

torch==1.2.0

注意事项

代码中的yolo4_weights.pth是基于608x608的图片训练的,但是由于显存原因。我将代码中的图片大小修改成了416x416。有需要的可以修改回来。 代码中的默认anchors是基于608x608的图片的。
注意不要使用中文标签,文件夹中不要有空格!
在训练前需要务必在model_data下新建一个txt文档,文档中输入需要分的类,在train.py中将classes_path指向该文件

小技巧的设置

在train.py文件下:
1、mosaic参数可用于控制是否实现Mosaic数据增强。
2、Cosine_scheduler可用于控制是否使用学习率余弦退火衰减。
3、label_smoothing可用于控制是否Label Smoothing平滑。

文件下载

训练所需的yolo4_weights.pth可在百度网盘中下载。
链接: https://pan.baidu.com/s/1WlDNPtGO1pwQbqwKx1gRZA 提取码: p4sc
yolo4_weights.pth是coco数据集的权重。
yolo4_voc_weights.pth是voc数据集的权重。

预测步骤

a、使用预训练权重

  1. 下载完库后解压,在百度网盘下载yolo4_weights.pth或者yolo4_voc_weights.pth,放入model_data,运行predict.py,输入
img/street.jpg
  1. 利用video.py可进行摄像头检测。

b、使用自己训练的权重

  1. 按照训练步骤训练。
  2. 在yolo.py文件里面,在如下部分修改model_path和classes_path使其对应训练好的文件;model_path对应logs文件夹下面的权值文件,classes_path是model_path对应分的类
_defaults = {
    "model_path": 'model_data/yolo4_weights.pth',
    "anchors_path": 'model_data/yolo_anchors.txt',
    "classes_path": 'model_data/coco_classes.txt',
    "model_image_size" : (416, 416, 3),
    "confidence": 0.5,
    "cuda": True
}
  1. 运行predict.py,输入
img/street.jpg
  1. 利用video.py可进行摄像头检测。

训练步骤

  1. 本文使用VOC格式进行训练。
  2. 训练前将标签文件放在VOCdevkit文件夹下的VOC2007文件夹下的Annotation中。
  3. 训练前将图片文件放在VOCdevkit文件夹下的VOC2007文件夹下的JPEGImages中。
  4. 在训练前利用voc2yolo4.py文件生成对应的txt。
  5. 再运行根目录下的voc_annotation.py,运行前需要将classes改成你自己的classes。注意不要使用中文标签,文件夹中不要有空格!
classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
  1. 此时会生成对应的2007_train.txt,每一行对应其图片位置及其真实框的位置
  2. 在训练前需要务必在model_data下新建一个txt文档,文档中输入需要分的类,在train.py中将classes_path指向该文件,示例如下:
classes_path = 'model_data/new_classes.txt'    

model_data/new_classes.txt文件内容为:

cat
dog
...
  1. 运行train.py即可开始训练。

mAP目标检测精度计算更新

更新了get_gt_txt.py、get_dr_txt.py和get_map.py文件。
get_map文件克隆自https://github.com/Cartucho/mAP
具体mAP计算过程可参考:https://www.bilibili.com/video/BV1zE411u7Vw

Reference

https://github.com/qqwweee/keras-yolo3/
https://github.com/Cartucho/mAP
https://github.com/Ma-Dan/keras-yolo4

The above is original readme.md

My work

I tried to train the YOLOv4 to detect the helmet and it's color. In order to know whether it learned well, I visualized the output of the YOLO-Head.

origin.jpg detection.jpg
origin.jpg detection.jpg
head0 head1 head2
head0layer0score.jpg head1layer0score.jpg head2layer0score.jpg
head0layer1class.jpg head1layer1class.jpg head2layer1class.jpg
head0layer2class_score.jpg head1layer2class_score.jpg head2layer2class_score.jpg

The above are shown the visualization of the "yellow", we can easily see the hot area focus on the yellow helmets. So I think this can help us to train the model to a certain extent.

Densely Connected Search Space for More Flexible Neural Architecture Search (CVPR2020)

DenseNAS The code of the CVPR2020 paper Densely Connected Search Space for More Flexible Neural Architecture Search. Neural architecture search (NAS)

Jamin Fong 291 Nov 18, 2022
Unofficial Alias-Free GAN implementation. Based on rosinality's version with expanded training and inference options.

Alias-Free GAN An unofficial version of Alias-Free Generative Adversarial Networks (https://arxiv.org/abs/2106.12423). This repository was heavily bas

dusk (they/them) 75 Dec 12, 2022
Algo-burn - Script to configure an Algorand address as a "burn" address for one or more ASA tokens

Algorand Burn Address This is a simple script to illustrate how a "burn address"

GSD 5 May 10, 2022
Gin provides a lightweight configuration framework for Python

Gin Config Authors: Dan Holtmann-Rice, Sergio Guadarrama, Nathan Silberman Contributors: Oscar Ramirez, Marek Fiser Gin provides a lightweight configu

Google 1.7k Jan 03, 2023
Source code and dataset for ACL2021 paper: "ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning".

ERICA Source code and dataset for ACL2021 paper: "ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive L

THUNLP 75 Nov 02, 2022
Text mining project; Using distilBERT to predict authors in the classification task authorship attribution.

DistilBERT-Text-mining-authorship-attribution Dataset used: https://www.kaggle.com/azimulh/tweets-data-for-authorship-attribution-modelling/version/2

1 Jan 13, 2022
Easily pull telemetry data and create beautiful visualizations for analysis.

This repository is a work in progress. Anything and everything is subject to change. Porpo Table of Contents Porpo Table of Contents General Informati

Ryan Dawes 33 Nov 30, 2022
Protect against subdomain takeover

domain-protect scans Amazon Route53 across an AWS Organization for domain records vulnerable to takeover deploy to security audit account scan your en

OVO Technology 0 Nov 17, 2022
code release for USENIX'22 paper `On the Security Risks of AutoML`

This project is a minimized runnable project cut from trojanzoo, which contains more datasets, models, attacks and defenses. This repo will not be mai

Ren Pang 5 Apr 19, 2022
A minimal implementation of face-detection models using flask, gunicorn, nginx, docker, and docker-compose

Face-Detection-flask-gunicorn-nginx-docker This is a simple implementation of dockerized face-detection restful-API implemented with flask, Nginx, and

Pooya-Mohammadi 30 Dec 17, 2022
RLMeta is a light-weight flexible framework for Distributed Reinforcement Learning Research.

RLMeta rlmeta - a flexible lightweight research framework for Distributed Reinforcement Learning based on PyTorch and moolib Installation To build fro

Meta Research 281 Dec 22, 2022
Company clustering with K-means/GMM and visualization with PCA, t-SNE, using SSAN relation extraction

RE results graph visualization and company clustering Installation pip install -r requirements.txt python -m nltk.downloader stopwords python3.7 main.

Jieun Han 1 Oct 06, 2022
To Design and Implement Logistic Regression to Classify Between Benign and Malignant Cancer Types

To Design and Implement Logistic Regression to Classify Between Benign and Malignant Cancer Types, from a Database Taken From Dr. Wolberg reports his Clinic Cases.

Astitva Veer Garg 1 Jul 31, 2022
[CVPR 2022 Oral] Versatile Multi-Modal Pre-Training for Human-Centric Perception

Versatile Multi-Modal Pre-Training for Human-Centric Perception Fangzhou Hong1  Liang Pan1  Zhongang Cai1,2,3  Ziwei Liu1* 1S-Lab, Nanyang Technologic

Fangzhou Hong 96 Jan 03, 2023
SalFBNet: Learning Pseudo-Saliency Distribution via Feedback Convolutional Networks

SalFBNet This repository includes Pytorch implementation for the following paper: SalFBNet: Learning Pseudo-Saliency Distribution via Feedback Convolu

12 Aug 12, 2022
Official repository for the paper F, B, Alpha Matting

FBA Matting Official repository for the paper F, B, Alpha Matting. This paper and project is under heavy revision for peer reviewed publication, and s

Marco Forte 404 Jan 05, 2023
Erpnext app for make employee salary on payroll entry based on one or more project with percentage for all project equal 100 %

Project Payroll this app for make payroll for employee based on projects like project on 30 % and project 2 70 % as account dimension it makes genral

Ibrahim Morghim 8 Jan 02, 2023
Greedy Gaussian Segmentation

GGS Greedy Gaussian Segmentation (GGS) is a Python solver for efficiently segmenting multivariate time series data. For implementation details, please

Stanford University Convex Optimization Group 72 Dec 07, 2022
PyTorch implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation

PyTorch implementation of Interpretable Explanations of Black Boxes by Meaningful Perturbation The paper: https://arxiv.org/abs/1704.03296 What makes

Jacob Gildenblat 322 Dec 17, 2022
A python implementation of Yolov5 to detect fire or smoke in the wild in Jetson Xavier nx and Jetson nano

yolov5-fire-smoke-detect-python A python implementation of Yolov5 to detect fire or smoke in the wild in Jetson Xavier nx and Jetson nano You can see

20 Dec 15, 2022