人群计数套餐
crowdcount的Python项目详细描述
人群计数套餐
crowdcount是使用Pytorch进行群组计数的库，由Fudan-VTS Research支持
来源
安装
pip install crowdcount --user --upgrade
简介
人群计数任务：
- 估计人群数量
用户指南：
- 在
模型
在from crowdcount.models import * # crowd counting models includes csr_net, mcnn, resnet50, resnet101, unet, vgg - 在
变换
^{pr2}$ 在 - 在
数据加载程序
在from crowdcount.data.data_loader import * # includes ShanghaiTech, UCF_QNRF, UCF_CC_50, Fudan-ShanghaiTech temporarily - 在
数据预处理
在from crowdcount.data.data_preprocess import * # gaussian preprocess for datasets - 在
实用工具
在from crowdcount.utils import * # includes loss functions, optimizers, tensorboard and save function - 在
发动机
在from crowdcount.engine import train # start to train train(*args, **kwargs) - 在
更多详细信息请参见document
在
演示
from crowdcount.engine import train
from crowdcount.models import Res101
from crowdcount.data.data_loader import *
from crowdcount.utils import *
import crowdcount.transforms as cc_transforms
import torchvision.transforms as transforms
# init model
model = Res101()
# init transforms
img_transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize(mean=[0.452016860247, 0.447249650955, 0.431981861591],
std=[0.23242045939, 0.224925786257, 0.221840232611])
])
gt_transform = cc_transforms.LabelEnlarge()
both_transform = cc_transforms.ComplexCompose([cc_transforms.TransposeFlip()])
# init dataset
train_set = ShanghaiTechDataset(mode="train",
part="b",
img_transform=img_transform,
gt_transform=gt_transform,
both_transform=both_transform,
root="/home/vts/chensongjian/CrowdCount/crowdcount/data/datasets/shtu_dataset_sigma_15")
test_set = ShanghaiTechDataset(mode="test",
part='b',
img_transform=img_transform,
root="/home/vts/chensongjian/CrowdCount/crowdcount/data/datasets/shtu_dataset_sigma_15")
# init loss
train_loss = AVGLoss()
test_loss = EnlargeLoss(100)
# init save function
saver = Saver(path="../exp/2019-12-22-main_sigma15_6")
# init tensorboard
tb = TensorBoard(path="../runs/2019-12-22-main_sigma15_6")
# start to train
train(model, train_set, test_set, train_loss, test_loss, optim="Adam", saver=saver, cuda_num=[3], train_batch=2,
test_batch=2, learning_rate=1e-5, epoch_num=500, enlarge_num=100, tensorboard=tb)
- 您可以在demo中找到更多演示
实验
我们将很快添加结果
感谢来自
- 项目
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