CenterNet:Objects as Points目标检测模型在Pytorch当中的实现

Overview

CenterNet:Objects as Points目标检测模型在Pytorch当中的实现


目录

  1. 性能情况 Performance
  2. 所需环境 Environment
  3. 注意事项 Attention
  4. 文件下载 Download
  5. 预测步骤 How2predict
  6. 训练步骤 How2train
  7. 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 centernet_resnet50_voc.pth VOC-Test07 512x512 - 77.1
COCO-Train2017 centernet_hourglass_coco.pth COCO-Val2017 512x512 38.4 56.8

所需环境

torch==1.2.0

注意事项

代码中的centernet_resnet50_voc.pth是使用voc数据集训练的。
代码中的centernet_hourglass_coco.pth是使用coco数据集训练的。
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

文件下载

训练所需的centernet_resnet50_voc.pth、centernet_hourglass_coco.pth可在百度网盘中下载。
链接: https://pan.baidu.com/s/1QBBgRb_TH8kJdSCQGgcXmQ 提取码: phnc

centernet_resnet50_voc.pth是voc数据集的权重。
centernet_hourglass_coco.pth是coco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centernet_resnet50_voc.pth或者centernet_hourglass_coco.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/centernet_resnet50_voc.pth',
    "classes_path"      : 'model_data/voc_classes.txt',
    # "model_path"        : 'model_data/centernet_hourglass_coco.h5',
    # "classes_path"      : 'model_data/coco_classes.txt',
    "backbone"          : "resnet50",
    "image_size"        : [512,512,3],
    "confidence"        : 0.3,
    # backbone为resnet50时建议设置为True
    # backbone为hourglass时建议设置为False
    # 也可以根据检测效果自行选择
    "nms"               : True,
    "nms_threhold"      : 0.3,
    "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voc2centernet.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/xuannianz/keras-CenterNet
https://github.com/see--/keras-centernet
https://github.com/xingyizhou/CenterNet

Comments
  • map指标

    map指标

    B导,我在使用get_map.py的时候,您的初始设置confidence为0.02,我正常得到map结果,但是我像其他网络一样把confidence修改成为0.001以后就得不到map结果了,这是为什么呢? 还有就是想问一下,在计算voc的map时,confidence都应该设置为很低,所以是不是0.02和0.001的效果相似?谢谢b导

    opened by ChristmasLee 2
  • 训练没有归一化,预测却有归一化,是不是有问题?

    训练没有归一化,预测却有归一化,是不是有问题?

    训练时候加载数据是dataloader.py 222行,是没有对图片做mean和std归一化的,但预测时predict.py -> centernet.py -> util/util.py -> preprocess_input里却对图片做了mean、std归一化,这应该有问题吧?

    opened by seven-linglx 2
  • 显示no mudule named 'past'

    显示no mudule named 'past'

    Traceback (most recent call last): File "train.py", line 15, in from utils.callbacks import LossHistory File "/root/centernet/centernet-pytorch-main/utils/callbacks.py", line 9, in from torch.utils.tensorboard import SummaryWriter File "/root/.local/lib/python3.7/site-packages/torch/utils/tensorboard/init.py", line 6, in from .writer import FileWriter, SummaryWriter # noqa F401 File "/root/.local/lib/python3.7/site-packages/torch/utils/tensorboard/writer.py", line 18, in from ._convert_np import make_np File "/root/.local/lib/python3.7/site-packages/torch/utils/tensorboard/_convert_np.py", line 12, in from caffe2.python import workspace File "/root/.local/lib/python3.7/site-packages/caffe2/python/workspace.py", line 15, in from past.builtins import basestring

    opened by buloseshi 1
  • 请问我改mobilenetv3的时候运行到第7批次就自动停止了是怎么回事呢

    请问我改mobilenetv3的时候运行到第7批次就自动停止了是怎么回事呢

    Finish Validation 0%| | 0/119 [00:00<?, ?it/s]Get map. 0%| | 0/119 [00:00<?, ?it/s] Traceback (most recent call last): File "/home/linux/data2/sun/centernet-pytorch-main/train.py", line 491, in epoch_step, epoch_step_val, gen, gen_val, UnFreeze_Epoch, Cuda, fp16, scaler, backbone, save_period, save_dir, local_rank) File "/home/linux/data2/sun/centernet-pytorch-main/utils/utils_fit.py", line 161, in fit_one_epoch eval_callback.on_epoch_end(epoch + 1, model_train) File "/home/linux/data2/sun/centernet-pytorch-main/utils/callbacks.py", line 211, in on_epoch_end self.get_map_txt(image_id, image, self.class_names, self.map_out_path) File "/home/linux/data2/sun/centernet-pytorch-main/utils/callbacks.py", line 145, in get_map_txt outputs = decode_bbox(outputs[0], outputs[1], outputs[2], self.confidence, self.cuda) IndexError: list index out of range

    opened by sunsn1997 2
  • 第一次尝试的新手提问

    第一次尝试的新手提问

    image

    按照readme文档中的步骤 1 已解压VOC数据集至项目根目录,pth文件至model_data目录 2 已修改voc_annotation.py 中的annotation_mode为2 3 运行train.py

    环境 pytorch1.2 + cuda10.0 +python3.6 ,Ubuntu 刚开始是使用的高版本torch和python,然后也尝试了python3.6+ torch1.2的环境,出现一样的问题

    opened by Xie-Muxi 1
Releases(v3.0)
  • v3.0(Apr 22, 2022)

    重要更新

    • 支持step、cos学习率下降法。
    • 支持adam、sgd优化器选择。
    • 支持学习率根据batch_size自适应调整。
    • 支持不同预测模式的选择,单张图片预测、文件夹预测、视频预测、图片裁剪、heatmap、各个种类目标数量计算。
    • 更新summary.py文件,用于观看网络结构。
    • 增加了多GPU训练。
    Source code(tar.gz)
    Source code(zip)
  • v2.0(Mar 4, 2022)

    重要更新

    • 更新train.py文件,增加了大量的注释,增加多个可调整参数。
    • 更新predict.py文件,增加了大量的注释,增加fps、视频预测、批量预测等功能。
    • 更新centernet.py文件,增加了大量的注释,增加先验框选择、置信度、非极大抑制等参数。
    • 合并get_dr_txt.py、get_gt_txt.py和get_map.py文件,通过一个文件来实现数据集的评估。
    • 更新voc_annotation.py文件,增加多个可调整参数。
    • 更新summary.py文件,用于观看网络结构。
    Source code(tar.gz)
    Source code(zip)
Owner
Bubbliiiing
Bubbliiiing
Use stochastic processes to generate samples and use them to train a fully-connected neural network based on Keras

Use stochastic processes to generate samples and use them to train a fully-connected neural network based on Keras which will then be used to generate residuals

Federico Lopez 2 Jan 14, 2022
PyTorch implementation of paper "Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes", CVPR 2021

Neural Scene Flow Fields PyTorch implementation of paper "Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes", CVPR 20

Zhengqi Li 585 Jan 04, 2023
BABEL: Bodies, Action and Behavior with English Labels [CVPR 2021]

BABEL is a large dataset with language labels describing the actions being performed in mocap sequences. BABEL labels about 43 hours of mocap sequences from AMASS [1] with action labels.

113 Dec 28, 2022
Codebase to experiment with a hybrid Transformer that combines conditional sequence generation with regression

Regression Transformer Codebase to experiment with a hybrid Transformer that combines conditional sequence generation with regression . Development se

International Business Machines 27 Jan 05, 2023
Code for KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs

KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs Check out the paper on arXiv: https://arxiv.org/abs/2103.13744 This repo cont

Christian Reiser 373 Dec 20, 2022
AI pipelines for Nvidia Jetson Platform

Jetson Multicamera Pipelines Easy-to-use realtime CV/AI pipelines for Nvidia Jetson Platform. This project: Builds a typical multi-camera pipeline, i.

NVIDIA AI IOT 96 Dec 23, 2022
Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation, NeurIPS 2021 Spotlight

PCAN for Multiple Object Tracking and Segmentation This is the offical implementation of paper PCAN for MOTS. We also present a trailer that consists

ETH VIS Group 328 Dec 29, 2022
GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition

GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition

Xinyan Zhao 29 Dec 26, 2022
Pytorch implementation of our paper under review -- 1xN Pattern for Pruning Convolutional Neural Networks

1xN Pattern for Pruning Convolutional Neural Networks (paper) . This is Pytorch re-implementation of "1xN Pattern for Pruning Convolutional Neural Net

Mingbao Lin (林明宝) 29 Nov 29, 2022
This is the code of paper ``Contrastive Coding for Active Learning under Class Distribution Mismatch'' with python.

Contrastive Coding for Active Learning under Class Distribution Mismatch Official PyTorch implementation of ["Contrastive Coding for Active Learning u

21 Dec 22, 2022
Small utility to demangle Nim symbols in callgrind files

nim_callgrind A small utility to demangle Nim symbols from callgrind files. Usage Run your (Nim) program with something like this: valgrind --tool=cal

kraptor 3 Feb 15, 2022
Multivariate Boosted TRee

Multivariate Boosted TRee What is MBTR MBTR is a python package for multivariate boosted tree regressors trained in parameter space. The package can h

SUPSI-DACD-ISAAC 61 Dec 19, 2022
Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation

SimplePose Code and pre-trained models for our paper, “Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation”, a

Jia Li 256 Dec 24, 2022
Face2webtoon - Despite its importance, there are few previous works applying I2I translation to webtoon.

Despite its importance, there are few previous works applying I2I translation to webtoon. I collected dataset from naver webtoon 연애혁명 and tried to transfer human faces to webtoon domain.

이상윤 64 Oct 19, 2022
DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition, TPAMI 2021

DVG-Face: Dual Variational Generation for HFR This repo is a PyTorch implementation of DVG-Face: Dual Variational Generation for Heterogeneous Face Re

52 Dec 30, 2022
PyTorch implementation for MINE: Continuous-Depth MPI with Neural Radiance Fields

MINE: Continuous-Depth MPI with Neural Radiance Fields Project Page | Video PyTorch implementation for our ICCV 2021 paper. MINE: Towards Continuous D

Zijian Feng 325 Dec 29, 2022
Code for the paper A Theoretical Analysis of the Repetition Problem in Text Generation

A Theoretical Analysis of the Repetition Problem in Text Generation This repository share the code for the paper "A Theoretical Analysis of the Repeti

Zihao Fu 37 Nov 21, 2022
Embracing Single Stride 3D Object Detector with Sparse Transformer

SST: Single-stride Sparse Transformer This is the official implementation of paper: Embracing Single Stride 3D Object Detector with Sparse Transformer

TuSimple 385 Dec 28, 2022
Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration

CoGAIL Table of Content Overview Installation Dataset Training Evaluation Trained Checkpoints Acknowledgement Citations License Overview This reposito

Jeremy Wang 29 Dec 24, 2022