Multi-task yolov5 with detection and segmentation based on yolov5

Related tags

Deep Learningyolov5ds
Overview

YOLOv5DS

Multi-task yolov5 with detection and segmentation based on yolov5(branch v6.0)

  • decoupled head
  • anchor free
  • segmentation head

README中文

Ablation experiment

All experiments is trained on a small dataset with 47 classes ,2.6k+ images for training and 1.5k+ images for validation:

model P R [email protected] [email protected]:95
yolov5s 0.536 0.368 0.374 0.206
yolov5s+train scrach 0.452 0.314 0.306 0.152
yolov5s+decoupled head 0.555 0.375 0.387 0.214
yolov5s + decoupled head+class balance weights 0.541 0.392 0.396 0.217
yolov5s + decoupled head+class balance weights 0.574 0.386 0.403 0.22
yolov5s + decoupled head+seghead 0.533 0.383 0.396 0.212

The baseline model is yolov5s. and decoupled head, add class balance weights all helps to improve MAP.

Adding a segmentation head can still get equivalent MAP as single detection model.

Training Method

python trainds.py

As VOC dataset do not offer the box labels and mask labels, so we forward this model with a detection batch and a segmention batch , and accumulate the gradient , than update the whole model parameters.

MAP

To compare with the SSD512, we use VOC07+12 training set as the detection training set, VOC07 test data as detection test data, for segmentation ,we use VOC12 segmentation datset as training and test set.

the input size is 512(letter box).

model VOC2007 test
SSD512 79.8
yolov5s+seghead(512) 79.2

The above results only trained less than 200 epoch, weights

demo

see detectds.py.

Train custom data

  1. Use labelme to label box and mask on your dataset;

    the box label format is voc, you can use voc2yolo.py to convert to yolo format,

    the mask label is json files , you should convert to mask .png image labels,like VOC2012 segmentation labels.

  2. see how to arrange your detection dataset with yolov5 , then arrange your segmentation dataset same as yolo files , see data/voc.yaml:

    
    # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
    path: .  # dataset root dir
    train: VOC/det/images/train  # train images (relative to 'path') 118287 images
    val: VOC/det/images/test  # train images (relative to 'path') 5000 images
    road_seg_train: VOC/seg/images/train   # road segmentation data
    road_seg_val: VOC/seg/images/val
    
    # Classes
    nc: 20  # number of classes
    segnc: 20
    
    names: ['aeroplane', 'bicycle', 'bird', 'boat',
               'bottle', 'bus', 'car', 'cat', 'chair',
               'cow', 'diningtable', 'dog', 'horse',
               'motorbike', 'person', 'pottedplant',
               'sheep', 'sofa', 'train', 'tvmonitor']  # class names
    
    segnames: ['aeroplane', 'bicycle', 'bird', 'boat',
               'bottle', 'bus', 'car', 'cat', 'chair',
               'cow', 'diningtable', 'dog', 'horse',
               'motorbike', 'person', 'pottedplant',
               'sheep', 'sofa', 'train', 'tvmonitor']
    
    1. change the config in trainds.py and :
    python trainds.py 
    
    1. test image folder with :

      python detectds.py
      

Reference

  1. YOLOP: You Only Look Once for Panoptic Driving Perception
  2. yolov5
You might also like...
a basic code repository for basic task in CV(classification,detection,segmentation)

basic_cv a basic code repository for basic task in CV(classification,detection,segmentation,tracking) classification generate dataset train predict de

A novel Engagement Detection with Multi-Task Training (ED-MTT) system
A novel Engagement Detection with Multi-Task Training (ED-MTT) system

A novel Engagement Detection with Multi-Task Training (ED-MTT) system which minimizes MSE and triplet loss together to determine the engagement level of students in an e-learning environment.

YOLOv5 Series Multi-backbone, Pruning and quantization Compression Tool Box.

YOLOv5-Compression Update News Requirements 环境安装 pip install -r requirements.txt Evaluation metric Visdrone Model mAP [email protected] Parameters(M) GFLOPs FPS@

A Python training and inference implementation of Yolov5 helmet detection in Jetson Xavier nx and Jetson nano
A Python training and inference implementation of Yolov5 helmet detection in Jetson Xavier nx and Jetson nano

yolov5-helmet-detection-python A Python implementation of Yolov5 to detect head or helmet in the wild in Jetson Xavier nx and Jetson nano. In Jetson X

YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset
YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset

YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research int

Implementation of PyTorch-based multi-task pre-trained models

mtdp Library containing implementation related to the research paper "Multi-task pre-training of deep neural networks for digital pathology" (Mormont

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

YOLOv5 detection interface - PyQt5 implementation
YOLOv5 detection interface - PyQt5 implementation

所有代码已上传,直接clone后,运行yolo_win.py即可开启界面。 2021/9/29:加入置信度选择 界面是在ultralytics的yolov5基础上建立的,界面使用pyqt5实现,内容较简单,娱乐而已。 功能: 模型选择 本地文件选择(视频图片均可) 开关摄像头

YOLOv5 + ROS2 object detection package

YOLOv5-ROS YOLOv5 + ROS2 object detection package This program changes the input of detect.py (ultralytics/yolov5) to sensor_msgs/Image of ROS2. Requi

Comments
Releases(v6.0)
A whale detector design for the Kaggle whale-detector challenge!

CNN (InceptionV1) + STFT based Whale Detection Algorithm So, this repository is my PyTorch solution for the Kaggle whale-detection challenge. The obje

Tarin Ziyaee 92 Sep 28, 2021
novel deep learning research works with PaddlePaddle

Research 发布基于飞桨的前沿研究工作,包括CV、NLP、KG、STDM等领域的顶会论文和比赛冠军模型。 目录 计算机视觉(Computer Vision) 自然语言处理(Natrual Language Processing) 知识图谱(Knowledge Graph) 时空数据挖掘(Spa

1.5k Dec 29, 2022
Code for ACM MM 2020 paper "NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination"

NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination The offical implementation for the "NOH-NMS: Improving Pedestrian Detection by

Tencent YouTu Research 64 Nov 11, 2022
Pytorch implementation of MaskFlownet

MaskFlownet-Pytorch Unofficial PyTorch implementation of MaskFlownet (https://github.com/microsoft/MaskFlownet). Tested with: PyTorch 1.5.0 CUDA 10.1

Daniele Cattaneo 84 Nov 02, 2022
This repository attempts to replicate the SqueezeNet architecture and implement the same on an image classification task.

SqueezeNet-Implementation This repository attempts to replicate the SqueezeNet architecture using TensorFlow discussed in the research paper: "Squeeze

Rohan Mathur 3 Dec 13, 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
O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis

O-CNN This repository contains the implementation of our papers related with O-CNN. The code is released under the MIT license. O-CNN: Octree-based Co

Microsoft 607 Dec 28, 2022
LSTM-VAE Implementation and Relevant Evaluations

LSTM-VAE Implementation and Relevant Evaluations Before using any file in this repository, please create two directories under the root directory name

Lan Zhang 5 Oct 08, 2022
we propose a novel deep network, named feature aggregation and refinement network (FARNet), for the automatic detection of anatomical landmarks.

Feature Aggregation and Refinement Network for 2D Anatomical Landmark Detection Overview Localization of anatomical landmarks is essential for clinica

aoyueyuan 0 Aug 28, 2022
Lane follower: Lane-detector (OpenCV) + Object-detector (YOLO5) + CAN-bus

Lane Follower This code is for the lane follower, including perception and control, as shown below. Environment Hardware Industrial Camera Intel-NUC(1

Siqi Fan 3 Jul 07, 2022
A deep learning tabular classification architecture inspired by TabTransformer with integrated gated multilayer perceptron.

The GatedTabTransformer. A deep learning tabular classification architecture inspired by TabTransformer with integrated gated multilayer perceptron. C

Radi Cho 60 Dec 15, 2022
A parametric soroban written with CADQuery.

A parametric soroban written in CADQuery The purpose of this project is to demonstrate how "code CAD" can be intuitive to learn. See soroban.py for a

Lee 4 Aug 13, 2022
Real-time LIDAR-based Urban Road and Sidewalk detection for Autonomous Vehicles 🚗

urban_road_filter: a real-time LIDAR-based urban road and sidewalk detection algorithm for autonomous vehicles Dependency ROS (tested with Kinetic and

JKK - Vehicle Industry Research Center 180 Dec 12, 2022
ScriptProfilerPy - Module to visualize where your python script is slow

ScriptProfiler helps you track where your code is slow It provides: Code lines t

Lucas BLP 3 Jun 02, 2022
The official repository for paper ''Domain Generalization for Vision-based Driving Trajectory Generation'' submitted to ICRA 2022

DG-TrajGen The official repository for paper ''Domain Generalization for Vision-based Driving Trajectory Generation'' submitted to ICRA 2022. Our Meth

Wang 25 Sep 26, 2022
Easy to use and customizable SOTA Semantic Segmentation models with abundant datasets in PyTorch

Semantic Segmentation Easy to use and customizable SOTA Semantic Segmentation models with abundant datasets in PyTorch Features Applicable to followin

sithu3 530 Jan 05, 2023
ViViT: Curvature access through the generalized Gauss-Newton's low-rank structure

ViViT is a collection of numerical tricks to efficiently access curvature from the generalized Gauss-Newton (GGN) matrix based on its low-rank structure. Provided functionality includes computing

Felix Dangel 12 Dec 08, 2022
Code for IntraQ, PyTorch implementation of our paper under review

IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization paper Requirements Python = 3.7.10 Pytorch == 1.7

1 Nov 19, 2021
PaddleBoBo是基于PaddlePaddle和PaddleSpeech、PaddleGAN等开发套件的虚拟主播快速生成项目

PaddleBoBo - 元宇宙时代,你也可以动手做一个虚拟主播。 PaddleBoBo是基于飞桨PaddlePaddle深度学习框架和PaddleSpeech、PaddleGAN等开发套件的虚拟主播快速生成项目。PaddleBoBo致力于简单高效、可复用性强,只需要一张带人像的图片和一段文字,就能

502 Jan 08, 2023
Research using Cirq!

ReCirq Research using Cirq! This project contains modules for running quantum computing applications and experiments through Cirq and Quantum Engine.

quantumlib 230 Dec 29, 2022