AI深度学习TT100k交通标志识别
文章目录
- 研究背景
- 代码下载链接
- 一、效果演示
- 1.1 图像演示
- 1.2 视频演示
- 二、技术原理
- 2.1 整体流程
- 2.2 TT100K中国交通标志数据集介绍
- 2.3 YOLOV5 TT100K中国交通标志检测原理
- 2.3.1 概述
- 2.3.2 输入层
- 2.3.3 Backbone层
- 2.3.4 Backbone层
- 2.3.5 Head层
- 2.4 模型训练
- 2.4.1 Conda环境构建
- 2.4.2 基础环境构建
- 2.4.3 安装YOLOv5环境
- 2.4.4 构建TT100K交通标志检测模型
- 2.4.5 TT100K数据集标记与校验
- 2.4.6 电路板缺陷检测模型训练
- 2.4.7 TT100K中国交通标志验证测试
- 代码下载链接
- 参考文献
研究背景
交通标志识别研究的背景主要有以下几方面：
- 交通安全需求：
- 交通事故频发推动研究：随着汽车保有量的不断增加，交通事故成为严重的社会问题。许多交通事故是由于驾驶员疏忽交通标识、错判交通信号等因素导致的。准确识别交通标志能够为驾驶员提供及时、准确的道路信息，指导驾驶员做出合理的反应，对于减少交通事故、保障人身安全和财产安全具有重要意义。
- 自动驾驶发展的关键技术：在自动驾驶技术中，车辆需要准确理解和识别各种交通标志，才能做出正确的驾驶决策。交通标志识别是自动驾驶系统的关键环节之一，对于实现自动驾驶的安全性和可靠性至关重要。
- 智能交通系统的发展：
- 智能交通的重要组成部分：智能交通系统旨在提高交通效率、改善交通管理和保障交通安全。交通标志识别系统作为智能交通系统的重要组成部分，能够为交通管理部门提供实时的交通标志信息，帮助实现交通流量的优化控制、道路状况的监测和预警等功能。
- 交通数据采集与分析的基础：准确识别交通标志可以为交通数据的采集和分析提供基础信息。通过对交通标志的识别和分析，可以了解不同路段的交通规则、交通流量分布等情况，为交通规划和管理提供科学依据。
- 技术进步的推动：
- 计算机视觉技术的发展：计算机视觉技术的不断进步为交通标志识别提供了技术支持。图像采集设备的性能不断提高，能够获取高质量的交通标志图像；图像处理算法的不断优化，使得对交通标志的特征提取和分析更加准确和高效。
- 深度学习算法的兴起：深度学习算法在图像识别领域取得了显著的成果，为交通标志识别提供了新的解决方案。深度学习模型可以自动学习交通标志的特征，具有较高的识别准确率和鲁棒性，能够适应复杂的道路环境和光照条件。
- 实际道路环境的复杂性：
- 多变的光照条件：自然场景下的光照条件变化很大，如白天、夜晚、阴天、晴天等不同的光照条件会对交通标志的颜色、亮度和对比度产生影响，增加了交通标志识别的难度。
- 复杂的背景干扰：道路上的背景复杂多样，如建筑物、树木、车辆等物体可能会遮挡交通标志，或者与交通标志的颜色、形状相似，干扰交通标志的识别。
- 交通标志的损坏和变形：交通标志在长期使用过程中可能会出现损坏、变形、掉色等情况，导致交通标志的特征发生变化，影响识别的准确性。
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代码下载链接
关注博主的G Z H【小蜜蜂视觉】，回复【TT100K】即可获取下载方式
若您想获得博文中涉及的实现完整全部程序文件（包括系统UI设计文件，电路板缺陷测试数据集、py文件，模型权重文件，调试说明等），代码获取与技术指导，具体见可参考博客与视频，已将所有涉及的文件同时打包到里面，软件安装调试有具体说明，我们有专业的调试技术人员，将远程协助客户调试，具体请看安装调试说明.txt，完整文件截图如下：
一、效果演示
本文构建的电路板缺陷检测系统基于PyQt5构建，支持图像、视频、摄像头以及RTSP等数据源输入。
1.1 图像演示
1.2 视频演示
二、技术原理
2.1 整体流程
深度学习实战TT100K中国交通标志检测是从输入图像中准确地定位交通标志的位置，通常是通过目标检测技术来实现。
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数据准备： 首先，需要准备中国交通标志数据集。
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网络架构： 选择一个适合电路板缺陷定位任务的深度学习网络架构。一种常见的选择是基于卷积神经网络（CNN）的架构，例如Faster R-CNN、YOLO（You Only Look Once）或SSD（Single Shot MultiBox Detector）。这些网络可以同时预测边界框的位置和类别，适用于目标检测任务。
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训练： 使用准备好的训练数据集对所选网络架构进行训练。训练过程涉及将输入图像传递给网络，然后通过反向传播优化网络的权重，使其能够准确地预测电路缺陷位置。训练数据中的每个样本都包括输入图像和相应的缺陷位置标注。
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预测： 在训练完成后，将训练得到的网络应用于新的图像。通过将图像输入网络，网络将输出电路缺陷位置的预测结果，这通常是一个边界框或四个关键点的坐标。
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后处理： 根据网络输出的预测结果，可以使用一些后处理技术来提高定位的准确性。例如，可以使用非极大值抑制（NMS）来抑制重叠的边界框，只保留最有可能的交通标志位置。
-
评估和调优： 对预测结果进行评估，可以使用评价指标如IoU（Intersection over Union）来衡量预测框与真实标注框的重叠程度。根据评估结果，可以对网络架构、训练参数等进行调优，以提高定位的准确性和稳定性。
2.2 TT100K中国交通标志数据集介绍
TT100K指的是Tsinghua-Tencent 100K，是一个大型交通标志基准数据集。以下是关于它的详细介绍：
- 数据来源与规模：
- 该数据集是由清华-腾讯联合实验室提出的，来源于100,000张腾讯街景全景图。数据集中提供了100,000张分辨率为2048像素×2048像素的图像，其中包含30,000个交通标志实例。
- 标注信息：
- 对于数据集中的每个交通标志，都带有详细的标注信息，包括类别标签、边界框以及像素蒙版。这些标注信息为交通标志的识别和检测算法提供了准确的训练和测试数据。
- 数据多样性：
- 图像涵盖了不同光照和天气状况，例如白天、夜晚、晴天、阴天、雨天等各种条件下的交通标志图像，这使得基于该数据集训练的模型能够更好地适应不同的实际场景。
- 应用价值：
- 在交通标志识别研究领域，TT100K 数据集为研究人员提供了丰富的实验数据，有助于推动交通标志识别算法的发展和优化。许多研究人员使用该数据集来训练和测试他们的交通标志识别模型，并与其他先进的方法进行比较，以验证其算法的有效性和优越性。
- 对于自动驾驶技术的发展也具有重要意义，准确的交通标志识别是自动驾驶系统的关键环节之一，通过使用 TT100K 数据集进行训练，可以提高自动驾驶系统对交通标志的识别能力，从而增强自动驾驶的安全性和可靠性。
总之，TT100K 数据集是交通标志识别领域中一个非常重要的基准数据集，为交通标志识别技术的研究和发展提供了有力的支持。如下图所示
2.3 YOLOV5 TT100K中国交通标志检测原理
2.3.1 概述
YOLOv5算法是一种单阶段目标检测算法，其网络结构主要由输入端（Input）、主干网络Backbone）、特征融合模块（Neck ）和预测层（Head）4个部分组成。如下图所示。
对不同尺寸的目标进行检测时，输入图片经过处理后变成大小为640×640的图片，再输入骨干网络处理得到20×20 、40×40、80×80 三种特征图，再将三种不同尺度的特征图进行融合，使得网络学习同时兼顾目标的顶层和底层特征。
2.3.2 输入层
为了提升模型的泛化能力，在YOLOv5 中增加了Mosaic数据增强方式，即从一个 batch 中随机选取 4 张图片，并将图片进行随机缩放、裁剪，再拼接成一个设定边长的训练样本，作为训练集图片送入神经网络。这样做可以在不改变原来的数据集数量的基础上获得更多数据特征进行训练，既能有效提高系统的鲁棒性，也能在一定程度上减少GPU 的损耗，也可以加快网络训练速度。马赛克数据增强原理如下图所示。
2.3.3 Backbone层
YOLOv5 中的主干网络 Backbone 主要作用是提取输入图像的目标特征，使用了Focus结构作为Backbone中的基准网络，网络结构模型为CSPDarknet53 ，并通过切片操作来获得得到二倍下采样图，可以有效增强主干网络特征提取能力。
1）Focus 结构
输入的图像先经过 Focus 模块，进行切片操作，即在图片中每隔像素值进行取值，得到四张互补的输入图像，再输入骨干网络进行处理，从而达到对系统提速的效果。Focus结构如下图所示。
2）CSP 结构
YOLOv5 中的 CSP 结构主要用于增强主干网络提取深层图的信息，常用的CSP 结构主要有两种，被用于 Backbone 主干网络的是CSP1 模块，被用于特征融合Neck结构的是CSP2 模块。CSP1 模块能有效减少网络计算量和保证网络模型整体的准确性，其结构共有两个分支，一个分支连接残差组件，另一分支在卷积后通过 Concat 方式和上一分支相连接。结构如下图所示。
CBL 模块主要由图像的卷积、批量标准化操作和 Leaky_Relu 激活函数组成，如下图所示。
残差结构 Resunit 主要用于防止当网络深度加深时网络性能退化，如下图所示。
SPP 模块主要用于把输入图像送入池化层中，获得不同的池化特征值，再将这些池化特征值和原图的特征值用Concat进行连接，使得在不影响网络的训练速率的前提下，显著分离图像特征值，如下图所示。
2.3.4 Backbone层
YOLOv5中的Neck 层主要用于将 Backbone 结构中提取到的目标特征进行融合，再输入 Head 层。在YOLOv5的Neck模块中采用FPN+PAN网络结构和CSP2 模块来增加特征融合能力。其中， 特征金字塔网络（FPN），主要用于采集图像中的高层信息，并将其传递给低层，路径聚合网络（PAN），则相反，将目标位置信息由低层传递给高层，从而有效提高目标识别的准确性，如下图所示。
2.3.5 Head层
YOLOv5 的 Head 层主要功能是对经过 Neck 结构特征融合后的目标进行类别的判断和预测。Head 层主要包含损失函数和非极大值抑制两部分，损失函数用于评价训练时预测值与真实值之间的误差程度。其中，YOLOv5 以 GIOU_Loss 做为损失函数，其数值越小，说明模型的预测效果越好。非极大值抑制处理主要用于对最后的目标检测框进行非极大值抑制处理，保留最优目标框，提高了目标识别的准确性。
2.4 模型训练
模型训练主要分为如下几步：
2.4.1 Conda环境构建
新人安装Anaconda环境可以参考博主写的文章Anaconda3与PyCharm安装配置保姆教程
2.4.2 基础环境构建
新人安装PyTorch GPU版本可以参考博主写的文章基于conda的PyTorch深度学习框架GPU安装教程
2.4.3 安装YOLOv5环境
conda create -n yolov5 python=3.8
conda activate yolov5
git clone https://github.com/ultralytics/yolov5.git
cd yolov5
pip install -r requirement.txt
2.4.4 构建TT100K交通标志检测模型
TT100K数据集进行清洗，最终选择了50种中国交通标志，分别为
names: ['pl80', 'p6', 'ph', 'w', 'pa', 'p27', 'i5', 'p1', 'il70', 'p5', 'pm', 'p19', 'ip', 'p11', 'p13', 'p26', 'i2', 'pn', 'p10', 'p23', 'pbp', 'p3', 'p12',
'pne', 'i4', 'pb', 'pg', 'pr','pl5','pl10', 'pl15','pl20','pl25','pl30','pl35','pl40','pl50','pl60','pl65','pl70','pl90','pl100','pl110',
'pl120','il50','il60','il80','il90','il100','il110']
模型选用YOLOv5s来训练，参数如下：
# Parameters
nc: 50 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
# YOLOv5 v6.0 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 3, C3, [1024]],
[-1, 1, SPPF, [1024, 5]], # 9
]
# YOLOv5 v6.0 head
head:
[[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, C3, [512, False]], # 13
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]
2.4.5 TT100K数据集标记与校验
TT100K数据集训练集一共20000张左右，验证集4000张左右，标注格式采用yolo格式组织
TT100K
images
train
image1.jpg
image2.jpg
...
val
image11.jpg
image22.jpg
...
labels
train
image1.txt
image2.txt
...
val
image11.txt
image22.txt
...
2.4.6 电路板缺陷检测模型训练
python train.py --data data/tt100k.yaml --weights weights/yolo5s.pt --epochs 300 --img 640 --batch 32
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr2
0, 0.063361, 0.013733, 0.088093, 0.019856, 0.44401, 0.023848, 0.013745, 0.040425, 0.0081039, 0.088355, 0.0033307, 0.0033307, 0.070023
1, 0.045026, 0.0082092, 0.084019, 0.99071, 0.034742, 0.061011, 0.035571, 0.034428, 0.0064194, 0.082601, 0.0066421, 0.0066421, 0.040001
2, 0.042414, 0.0074137, 0.07688, 0.34249, 0.25845, 0.11027, 0.068245, 0.03558, 0.0059564, 0.074893, 0.0099314, 0.0099314, 0.0099573
3, 0.03855, 0.0068509, 0.069874, 0.48283, 0.34299, 0.16249, 0.1086, 0.032213, 0.0054188, 0.069299, 0.009901, 0.009901, 0.009901
4, 0.035629, 0.0064825, 0.065864, 0.62325, 0.293, 0.20435, 0.14103, 0.028827, 0.0051086, 0.06401, 0.009901, 0.009901, 0.009901
5, 0.034627, 0.0061857, 0.060686, 0.21299, 0.49078, 0.22633, 0.15646, 0.028586, 0.005032, 0.060611, 0.009868, 0.009868, 0.009868
6, 0.034033, 0.0061096, 0.058237, 0.18917, 0.55519, 0.2565, 0.18519, 0.027525, 0.0046499, 0.057097, 0.009835, 0.009835, 0.009835
7, 0.033843, 0.0060086, 0.055699, 0.19722, 0.55381, 0.28461, 0.20411, 0.028055, 0.0047453, 0.055075, 0.009802, 0.009802, 0.009802
8, 0.033772, 0.0060093, 0.054237, 0.19437, 0.6019, 0.2962, 0.21088, 0.02756, 0.0046903, 0.053216, 0.009769, 0.009769, 0.009769
9, 0.033476, 0.0059833, 0.052363, 0.20441, 0.59436, 0.31073, 0.22382, 0.027098, 0.004673, 0.051697, 0.009736, 0.009736, 0.009736
10, 0.032851, 0.0057573, 0.051422, 0.22232, 0.61606, 0.34443, 0.25356, 0.027246, 0.0044971, 0.049342, 0.009703, 0.009703, 0.009703
11, 0.03272, 0.0056973, 0.049853, 0.24867, 0.61201, 0.3626, 0.26863, 0.026034, 0.0043373, 0.047599, 0.00967, 0.00967, 0.00967
12, 0.03266, 0.0056695, 0.047681, 0.28717, 0.61165, 0.39347, 0.29665, 0.025374, 0.004349, 0.045888, 0.009637, 0.009637, 0.009637
13, 0.032153, 0.0055037, 0.046518, 0.3058, 0.57287, 0.40662, 0.30909, 0.025011, 0.0042358, 0.044333, 0.009604, 0.009604, 0.009604
14, 0.032576, 0.0054886, 0.04542, 0.33658, 0.58213, 0.42061, 0.32379, 0.024829, 0.0041742, 0.043134, 0.009571, 0.009571, 0.009571
15, 0.032387, 0.0055425, 0.044446, 0.37355, 0.52607, 0.42231, 0.32391, 0.024602, 0.0042754, 0.042749, 0.009538, 0.009538, 0.009538
16, 0.032671, 0.0056246, 0.043551, 0.36358, 0.5695, 0.45077, 0.34856, 0.024069, 0.0041521, 0.040751, 0.009505, 0.009505, 0.009505
17, 0.03226, 0.0053648, 0.042455, 0.3658, 0.57732, 0.45753, 0.35646, 0.023806, 0.0040891, 0.039581, 0.009472, 0.009472, 0.009472
18, 0.03243, 0.0055152, 0.042473, 0.3621, 0.57262, 0.4535, 0.3522, 0.02402, 0.0042186, 0.039545, 0.009439, 0.009439, 0.009439
19, 0.03275, 0.005531, 0.040938, 0.37596, 0.59058, 0.47926, 0.37439, 0.023726, 0.0041094, 0.038301, 0.009406, 0.009406, 0.009406
20, 0.033239, 0.0056834, 0.040887, 0.41268, 0.56448, 0.48873, 0.38445, 0.023789, 0.0040913, 0.037346, 0.009373, 0.009373, 0.009373
21, 0.032865, 0.0054198, 0.039451, 0.42437, 0.57752, 0.5105, 0.40284, 0.022967, 0.0039995, 0.036103, 0.00934, 0.00934, 0.00934
22, 0.032828, 0.0053395, 0.038421, 0.44325, 0.58882, 0.52703, 0.41799, 0.022897, 0.0039739, 0.035061, 0.009307, 0.009307, 0.009307
23, 0.032811, 0.0054493, 0.036931, 0.44394, 0.60169, 0.54538, 0.43321, 0.022514, 0.003919, 0.034121, 0.009274, 0.009274, 0.009274
24, 0.03268, 0.005388, 0.037201, 0.44381, 0.61491, 0.5533, 0.44201, 0.022378, 0.0039265, 0.033726, 0.009241, 0.009241, 0.009241
25, 0.032402, 0.005423, 0.036682, 0.47562, 0.58721, 0.55986, 0.44753, 0.022448, 0.0039325, 0.033295, 0.009208, 0.009208, 0.009208
26, 0.033003, 0.0054167, 0.036404, 0.49296, 0.58061, 0.56703, 0.45353, 0.022303, 0.0039037, 0.032514, 0.009175, 0.009175, 0.009175
27, 0.033008, 0.0053913, 0.035039, 0.49935, 0.59701, 0.58161, 0.46631, 0.021898, 0.0038903, 0.031634, 0.009142, 0.009142, 0.009142
28, 0.032476, 0.0053106, 0.03442, 0.54556, 0.55914, 0.58927, 0.47249, 0.021813, 0.0038598, 0.030857, 0.009109, 0.009109, 0.009109
29, 0.032375, 0.0053179, 0.033558, 0.57121, 0.57172, 0.60157, 0.48411, 0.021647, 0.0038647, 0.030234, 0.009076, 0.009076, 0.009076
30, 0.032728, 0.0052627, 0.032921, 0.59628, 0.56386, 0.60798, 0.49015, 0.021591, 0.0038217, 0.029875, 0.009043, 0.009043, 0.009043
31, 0.032719, 0.0052322, 0.03244, 0.59523, 0.57938, 0.61583, 0.49755, 0.021523, 0.0038025, 0.029317, 0.00901, 0.00901, 0.00901
32, 0.03286, 0.0052865, 0.031345, 0.56057, 0.61302, 0.6258, 0.50429, 0.021365, 0.0037989, 0.028644, 0.008977, 0.008977, 0.008977
33, 0.032526, 0.0053013, 0.031186, 0.60673, 0.58878, 0.63047, 0.50989, 0.021331, 0.003777, 0.028041, 0.008944, 0.008944, 0.008944
34, 0.0326, 0.0053323, 0.031624, 0.59372, 0.60155, 0.63147, 0.51128, 0.021231, 0.0037723, 0.027678, 0.008911, 0.008911, 0.008911
35, 0.032783, 0.0053221, 0.031254, 0.59656, 0.60743, 0.63621, 0.51489, 0.021004, 0.0037619, 0.027334, 0.008878, 0.008878, 0.008878
36, 0.033119, 0.0053159, 0.030469, 0.64668, 0.58125, 0.64055, 0.51886, 0.02108, 0.0037737, 0.026995, 0.008845, 0.008845, 0.008845
37, 0.032538, 0.0052159, 0.029027, 0.65477, 0.58883, 0.64825, 0.52594, 0.020977, 0.0037651, 0.026315, 0.008812, 0.008812, 0.008812
38, 0.032932, 0.0052535, 0.029924, 0.63616, 0.60366, 0.65139, 0.52838, 0.020967, 0.003748, 0.025895, 0.008779, 0.008779, 0.008779
39, 0.033062, 0.0052943, 0.029677, 0.66848, 0.59473, 0.65422, 0.53131, 0.020937, 0.0037527, 0.025598, 0.008746, 0.008746, 0.008746
40, 0.032384, 0.0052629, 0.028554, 0.66109, 0.60544, 0.65856, 0.53575, 0.020842, 0.0037502, 0.025287, 0.008713, 0.008713, 0.008713
41, 0.033194, 0.0051974, 0.027931, 0.62554, 0.63166, 0.66413, 0.54023, 0.020755, 0.0037449, 0.024943, 0.00868, 0.00868, 0.00868
42, 0.032743, 0.0051519, 0.027257, 0.65158, 0.6261, 0.66896, 0.54414, 0.020681, 0.0037297, 0.024577, 0.008647, 0.008647, 0.008647
43, 0.03308, 0.005295, 0.028377, 0.66213, 0.62352, 0.67199, 0.5467, 0.020678, 0.0037395, 0.024318, 0.008614, 0.008614, 0.008614
44, 0.032709, 0.0052557, 0.0275, 0.64745, 0.63553, 0.67496, 0.54934, 0.020637, 0.0037424, 0.024106, 0.008581, 0.008581, 0.008581
45, 0.032875, 0.0052871, 0.027811, 0.64394, 0.64258, 0.67693, 0.55146, 0.020595, 0.0037384, 0.023931, 0.008548, 0.008548, 0.008548
46, 0.033106, 0.0052173, 0.026308, 0.64515, 0.64619, 0.67994, 0.55399, 0.020563, 0.0037373, 0.023685, 0.008515, 0.008515, 0.008515
47, 0.032743, 0.0053661, 0.027287, 0.65528, 0.64148, 0.68218, 0.55603, 0.02056, 0.0037373, 0.02343, 0.008482, 0.008482, 0.008482
48, 0.032783, 0.0051316, 0.02628, 0.65353, 0.64715, 0.6846, 0.55786, 0.020541, 0.0037341, 0.02324, 0.008449, 0.008449, 0.008449
49, 0.033169, 0.00518, 0.025224, 0.65421, 0.6491, 0.68819, 0.56068, 0.020537, 0.0037266, 0.023026, 0.008416, 0.008416, 0.008416
50, 0.032762, 0.0051755, 0.024361, 0.66691, 0.64322, 0.69143, 0.5632, 0.020525, 0.0037191, 0.022816, 0.008383, 0.008383, 0.008383
51, 0.032688, 0.005195, 0.024139, 0.67172, 0.64494, 0.69447, 0.56648, 0.020498, 0.0037175, 0.022625, 0.00835, 0.00835, 0.00835
52, 0.033155, 0.0051913, 0.024461, 0.66517, 0.65231, 0.69703, 0.56887, 0.020462, 0.0037136, 0.022443, 0.008317, 0.008317, 0.008317
53, 0.032538, 0.0052959, 0.025882, 0.67526, 0.64679, 0.69903, 0.57142, 0.020447, 0.0037166, 0.022296, 0.008284, 0.008284, 0.008284
54, 0.033405, 0.0053291, 0.025363, 0.70203, 0.63234, 0.7006, 0.57304, 0.020417, 0.0037208, 0.022181, 0.008251, 0.008251, 0.008251
55, 0.033117, 0.0053235, 0.02563, 0.69674, 0.63709, 0.70143, 0.57447, 0.020415, 0.0037269, 0.022101, 0.008218, 0.008218, 0.008218
56, 0.032616, 0.0052915, 0.024074, 0.69781, 0.63901, 0.70254, 0.57595, 0.020397, 0.0037259, 0.022001, 0.008185, 0.008185, 0.008185
57, 0.032899, 0.0051291, 0.022947, 0.7243, 0.62732, 0.70451, 0.57765, 0.02038, 0.0037233, 0.021901, 0.008152, 0.008152, 0.008152
58, 0.033528, 0.0052153, 0.023924, 0.73188, 0.62646, 0.70652, 0.57978, 0.020359, 0.0037229, 0.02177, 0.008119, 0.008119, 0.008119
59, 0.032791, 0.0052104, 0.02353, 0.74785, 0.62166, 0.70859, 0.58161, 0.020345, 0.0037196, 0.021649, 0.008086, 0.008086, 0.008086
60, 0.032782, 0.0052591, 0.02366, 0.73689, 0.62871, 0.71018, 0.583, 0.020347, 0.0037166, 0.021515, 0.008053, 0.008053, 0.008053
61, 0.033105, 0.005191, 0.023096, 0.75221, 0.62455, 0.71193, 0.5842, 0.020378, 0.0037143, 0.021392, 0.00802, 0.00802, 0.00802
62, 0.032828, 0.0051065, 0.022109, 0.75581, 0.62469, 0.71433, 0.58598, 0.020375, 0.0037095, 0.021269, 0.007987, 0.007987, 0.007987
63, 0.032723, 0.0051402, 0.021845, 0.73776, 0.63563, 0.71624, 0.58827, 0.020353, 0.0037058, 0.021149, 0.007954, 0.007954, 0.007954
64, 0.032645, 0.0051876, 0.022193, 0.76202, 0.6253, 0.71793, 0.58966, 0.020328, 0.0037009, 0.021028, 0.007921, 0.007921, 0.007921
65, 0.032686, 0.0051379, 0.021638, 0.76173, 0.62734, 0.71986, 0.59118, 0.020313, 0.0036968, 0.020894, 0.007888, 0.007888, 0.007888
66, 0.033133, 0.0052641, 0.022097, 0.74754, 0.63598, 0.7215, 0.59273, 0.020314, 0.0036944, 0.02077, 0.007855, 0.007855, 0.007855
67, 0.03282, 0.0051806, 0.021866, 0.75078, 0.63631, 0.72343, 0.59433, 0.020316, 0.0036919, 0.020657, 0.007822, 0.007822, 0.007822
68, 0.032406, 0.0050863, 0.020631, 0.75866, 0.63413, 0.72519, 0.59593, 0.020305, 0.0036886, 0.020537, 0.007789, 0.007789, 0.007789
69, 0.032291, 0.0051567, 0.020383, 0.76347, 0.63402, 0.7269, 0.5977, 0.020299, 0.0036852, 0.020417, 0.007756, 0.007756, 0.007756
70, 0.032656, 0.00514, 0.021058, 0.76951, 0.6329, 0.72843, 0.59917, 0.020292, 0.0036837, 0.020295, 0.007723, 0.007723, 0.007723
71, 0.032562, 0.0051437, 0.020336, 0.76394, 0.63723, 0.72984, 0.6, 0.020284, 0.0036829, 0.020184, 0.00769, 0.00769, 0.00769
72, 0.033339, 0.0051422, 0.020397, 0.78689, 0.62788, 0.73128, 0.60119, 0.02028, 0.0036796, 0.02007, 0.007657, 0.007657, 0.007657
73, 0.032924, 0.0051349, 0.019935, 0.79185, 0.62849, 0.73262, 0.60218, 0.020269, 0.0036791, 0.019949, 0.007624, 0.007624, 0.007624
74, 0.032712, 0.0050793, 0.01975, 0.80314, 0.62483, 0.73388, 0.60357, 0.020261, 0.0036779, 0.019835, 0.007591, 0.007591, 0.007591
75, 0.032649, 0.0051876, 0.021183, 0.79815, 0.62941, 0.73496, 0.60496, 0.020255, 0.0036766, 0.019728, 0.007558, 0.007558, 0.007558
76, 0.032327, 0.0051959, 0.020844, 0.78488, 0.63673, 0.73601, 0.60588, 0.020256, 0.0036762, 0.019629, 0.007525, 0.007525, 0.007525
77, 0.033221, 0.0055748, 0.023089, 0.78882, 0.63588, 0.7363, 0.60617, 0.020271, 0.0036816, 0.019567, 0.007492, 0.007492, 0.007492
78, 0.032745, 0.0053106, 0.020553, 0.81674, 0.62355, 0.73688, 0.60638, 0.020282, 0.0036867, 0.019512, 0.007459, 0.007459, 0.007459
79, 0.032628, 0.0052305, 0.020333, 0.82063, 0.62387, 0.73751, 0.60694, 0.020278, 0.0036888, 0.019428, 0.007426, 0.007426, 0.007426
80, 0.032683, 0.0050951, 0.019174, 0.81818, 0.62727, 0.7384, 0.60774, 0.020279, 0.0036862, 0.019333, 0.007393, 0.007393, 0.007393
81, 0.032434, 0.0051544, 0.020096, 0.81329, 0.63119, 0.73916, 0.60829, 0.020288, 0.0036853, 0.019238, 0.00736, 0.00736, 0.00736
82, 0.032803, 0.0051839, 0.019458, 0.81857, 0.62966, 0.73985, 0.60883, 0.020285, 0.0036846, 0.019136, 0.007327, 0.007327, 0.007327
83, 0.03249, 0.0052054, 0.019774, 0.8188, 0.62989, 0.74089, 0.60965, 0.020287, 0.0036766, 0.019014, 0.007294, 0.007294, 0.007294
84, 0.032005, 0.0050745, 0.019692, 0.82507, 0.62846, 0.74191, 0.61064, 0.020289, 0.0036704, 0.018906, 0.007261, 0.007261, 0.007261
85, 0.032442, 0.0050993, 0.019001, 0.83204, 0.62638, 0.74313, 0.61128, 0.02029, 0.0036669, 0.018793, 0.007228, 0.007228, 0.007228
86, 0.032386, 0.0050815, 0.018914, 0.83875, 0.6245, 0.7442, 0.61192, 0.020288, 0.0036636, 0.018678, 0.007195, 0.007195, 0.007195
87, 0.032351, 0.0050276, 0.018433, 0.8234, 0.63342, 0.74526, 0.61274, 0.020285, 0.0036584, 0.018566, 0.007162, 0.007162, 0.007162
88, 0.032578, 0.0051392, 0.01944, 0.81976, 0.63689, 0.74625, 0.61391, 0.020283, 0.0036545, 0.018464, 0.007129, 0.007129, 0.007129
89, 0.032239, 0.004979, 0.018214, 0.83379, 0.63199, 0.74732, 0.61476, 0.020272, 0.0036513, 0.018355, 0.007096, 0.007096, 0.007096
90, 0.032618, 0.0050673, 0.019266, 0.84107, 0.6304, 0.74844, 0.61581, 0.02027, 0.0036465, 0.018238, 0.007063, 0.007063, 0.007063
91, 0.032067, 0.0050042, 0.018535, 0.81888, 0.64185, 0.74939, 0.61693, 0.020255, 0.003644, 0.018129, 0.00703, 0.00703, 0.00703
92, 0.032051, 0.0050181, 0.018502, 0.82316, 0.64247, 0.75046, 0.61782, 0.020242, 0.0036414, 0.018008, 0.006997, 0.006997, 0.006997
93, 0.031761, 0.0049327, 0.017309, 0.82136, 0.64464, 0.75178, 0.61893, 0.02023, 0.0036384, 0.01788, 0.006964, 0.006964, 0.006964
94, 0.031771, 0.0049112, 0.017089, 0.82038, 0.64759, 0.75305, 0.62, 0.020216, 0.0036331, 0.017752, 0.006931, 0.006931, 0.006931
95, 0.031649, 0.0048784, 0.017121, 0.82372, 0.64756, 0.75443, 0.62102, 0.020214, 0.0036291, 0.017622, 0.006898, 0.006898, 0.006898
96, 0.031286, 0.0050115, 0.017306, 0.83245, 0.64511, 0.75575, 0.62196, 0.020206, 0.0036264, 0.017498, 0.006865, 0.006865, 0.006865
97, 0.031636, 0.0049934, 0.017788, 0.83834, 0.64372, 0.75687, 0.62287, 0.020194, 0.0036229, 0.017378, 0.006832, 0.006832, 0.006832
98, 0.031732, 0.004993, 0.017667, 0.84219, 0.64446, 0.75806, 0.62414, 0.020187, 0.0036207, 0.017265, 0.006799, 0.006799, 0.006799
99, 0.031885, 0.0049469, 0.017463, 0.83978, 0.64723, 0.75912, 0.62501, 0.02018, 0.0036165, 0.017149, 0.006766, 0.006766, 0.006766
100, 0.032113, 0.0049781, 0.018377, 0.84517, 0.64556, 0.76021, 0.62611, 0.02017, 0.0036131, 0.017043, 0.006733, 0.006733, 0.006733
101, 0.031877, 0.0049538, 0.017199, 0.84022, 0.65048, 0.76138, 0.62663, 0.020156, 0.00361, 0.016926, 0.0067, 0.0067, 0.0067
102, 0.031947, 0.0049057, 0.016569, 0.84697, 0.64847, 0.76247, 0.62757, 0.020148, 0.003606, 0.016806, 0.006667, 0.006667, 0.006667
103, 0.031553, 0.0048878, 0.016301, 0.84633, 0.65073, 0.76361, 0.62846, 0.02013, 0.0036015, 0.016691, 0.006634, 0.006634, 0.006634
104, 0.031434, 0.0048729, 0.016646, 0.85391, 0.64748, 0.76434, 0.62885, 0.020115, 0.0035975, 0.016594, 0.006601, 0.006601, 0.006601
105, 0.031806, 0.0050806, 0.018338, 0.84972, 0.65018, 0.76497, 0.62943, 0.0201, 0.0035943, 0.016496, 0.006568, 0.006568, 0.006568
106, 0.031046, 0.0050535, 0.01826, 0.83371, 0.65966, 0.76563, 0.63023, 0.020094, 0.0035909, 0.016395, 0.006535, 0.006535, 0.006535
107, 0.031261, 0.0049281, 0.016505, 0.83666, 0.65892, 0.76661, 0.63071, 0.020083, 0.0035885, 0.016286, 0.006502, 0.006502, 0.006502
108, 0.032034, 0.0051916, 0.018763, 0.8493, 0.65227, 0.76741, 0.63124, 0.02008, 0.003586, 0.016173, 0.006469, 0.006469, 0.006469
109, 0.031512, 0.0050455, 0.018, 0.84341, 0.65515, 0.767, 0.63117, 0.020096, 0.0035875, 0.016086, 0.006436, 0.006436, 0.006436
110, 0.031427, 0.0051308, 0.018631, 0.84003, 0.65713, 0.76769, 0.63152, 0.020084, 0.0035843, 0.016001, 0.006403, 0.006403, 0.006403
111, 0.031827, 0.0050066, 0.017365, 0.85014, 0.65301, 0.76858, 0.63242, 0.020081, 0.003582, 0.015906, 0.00637, 0.00637, 0.00637
112, 0.030934, 0.0048533, 0.015904, 0.87947, 0.63874, 0.76966, 0.63348, 0.02007, 0.0035777, 0.015805, 0.006337, 0.006337, 0.006337
113, 0.03153, 0.0048292, 0.015772, 0.84913, 0.65636, 0.77084, 0.6344, 0.020066, 0.0035747, 0.015707, 0.006304, 0.006304, 0.006304
114, 0.030914, 0.0048851, 0.016255, 0.88032, 0.64124, 0.77196, 0.63526, 0.020051, 0.0035698, 0.01561, 0.006271, 0.006271, 0.006271
115, 0.031569, 0.0049324, 0.016916, 0.8923, 0.63666, 0.77286, 0.63615, 0.020036, 0.0035675, 0.015523, 0.006238, 0.006238, 0.006238
116, 0.03189, 0.0050273, 0.017454, 0.84748, 0.66028, 0.77354, 0.63721, 0.020019, 0.0035648, 0.015436, 0.006205, 0.006205, 0.006205
117, 0.031168, 0.0048909, 0.015903, 0.85865, 0.65541, 0.77439, 0.63761, 0.02001, 0.003563, 0.015349, 0.006172, 0.006172, 0.006172
118, 0.031597, 0.0048817, 0.016465, 0.86109, 0.65502, 0.77524, 0.63831, 0.02001, 0.0035602, 0.015266, 0.006139, 0.006139, 0.006139
119, 0.031691, 0.0048574, 0.016009, 0.8507, 0.66263, 0.7763, 0.63903, 0.020005, 0.0035575, 0.015178, 0.006106, 0.006106, 0.006106
120, 0.031368, 0.0048128, 0.015618, 0.85214, 0.66439, 0.77758, 0.63996, 0.019994, 0.0035546, 0.01509, 0.006073, 0.006073, 0.006073
121, 0.031325, 0.0049538, 0.017231, 0.84948, 0.66651, 0.77813, 0.64065, 0.019994, 0.0035506, 0.015019, 0.00604, 0.00604, 0.00604
122, 0.030841, 0.0049518, 0.017417, 0.85127, 0.66597, 0.77855, 0.64099, 0.019977, 0.003547, 0.014954, 0.006007, 0.006007, 0.006007
123, 0.031086, 0.0048497, 0.016481, 0.85359, 0.66504, 0.77911, 0.64177, 0.019964, 0.0035423, 0.01488, 0.005974, 0.005974, 0.005974
124, 0.030517, 0.004914, 0.016658, 0.85335, 0.66582, 0.77967, 0.64231, 0.019958, 0.0035384, 0.014808, 0.005941, 0.005941, 0.005941
125, 0.031265, 0.0048175, 0.016053, 0.85698, 0.66433, 0.78048, 0.6428, 0.019952, 0.0035351, 0.014739, 0.005908, 0.005908, 0.005908
126, 0.030632, 0.0047603, 0.015218, 0.88499, 0.65083, 0.78129, 0.64361, 0.019934, 0.003531, 0.01467, 0.005875, 0.005875, 0.005875
127, 0.030986, 0.0047461, 0.014968, 0.88118, 0.65412, 0.78197, 0.64427, 0.019922, 0.0035266, 0.014597, 0.005842, 0.005842, 0.005842
128, 0.030575, 0.0047971, 0.015223, 0.8868, 0.65261, 0.78148, 0.64303, 0.019935, 0.003522, 0.014554, 0.005809, 0.005809, 0.005809
129, 0.030405, 0.0048258, 0.015621, 0.88782, 0.65238, 0.78154, 0.64335, 0.019939, 0.0035198, 0.014497, 0.005776, 0.005776, 0.005776
130, 0.030507, 0.0048118, 0.015942, 0.88719, 0.65295, 0.78199, 0.64399, 0.019928, 0.0035156, 0.014437, 0.005743, 0.005743, 0.005743
131, 0.030374, 0.0048454, 0.016069, 0.88629, 0.65416, 0.78262, 0.64451, 0.019909, 0.0035137, 0.014375, 0.00571, 0.00571, 0.00571
132, 0.030838, 0.0049723, 0.017097, 0.8914, 0.6531, 0.78329, 0.64482, 0.019899, 0.0035112, 0.014306, 0.005677, 0.005677, 0.005677
133, 0.030861, 0.004896, 0.016528, 0.88947, 0.65395, 0.78391, 0.64578, 0.019882, 0.0035089, 0.014243, 0.005644, 0.005644, 0.005644
134, 0.030806, 0.0048672, 0.016085, 0.88524, 0.65611, 0.78437, 0.64605, 0.019867, 0.0035069, 0.014185, 0.005611, 0.005611, 0.005611
135, 0.03115, 0.0048788, 0.016257, 0.88477, 0.65736, 0.78488, 0.64634, 0.019851, 0.0035045, 0.014127, 0.005578, 0.005578, 0.005578
136, 0.030839, 0.0048022, 0.015253, 0.88644, 0.65705, 0.7854, 0.64714, 0.019839, 0.0035033, 0.01407, 0.005545, 0.005545, 0.005545
137, 0.030376, 0.0047212, 0.014489, 0.87715, 0.66325, 0.78625, 0.6478, 0.019826, 0.0035001, 0.014006, 0.005512, 0.005512, 0.005512
138, 0.029985, 0.0047087, 0.015312, 0.86364, 0.67173, 0.78701, 0.64853, 0.019814, 0.0034963, 0.013946, 0.005479, 0.005479, 0.005479
139, 0.030604, 0.0048447, 0.015221, 0.86334, 0.6732, 0.78773, 0.64921, 0.019797, 0.0034934, 0.013887, 0.005446, 0.005446, 0.005446
140, 0.030748, 0.004771, 0.015497, 0.86672, 0.67253, 0.7886, 0.65019, 0.01978, 0.0034895, 0.013828, 0.005413, 0.005413, 0.005413
141, 0.03022, 0.0048063, 0.015194, 0.86254, 0.67601, 0.78932, 0.6508, 0.01976, 0.0034869, 0.013765, 0.00538, 0.00538, 0.00538
142, 0.029818, 0.0047004, 0.01471, 0.86634, 0.67553, 0.79001, 0.65152, 0.019743, 0.0034834, 0.013707, 0.005347, 0.005347, 0.005347
143, 0.030387, 0.0047326, 0.014323, 0.87472, 0.67176, 0.79075, 0.65188, 0.01973, 0.0034797, 0.013645, 0.005314, 0.005314, 0.005314
144, 0.030215, 0.0047528, 0.015347, 0.87765, 0.67133, 0.79121, 0.65235, 0.019708, 0.0034762, 0.013595, 0.005281, 0.005281, 0.005281
145, 0.0298, 0.0047936, 0.015609, 0.87538, 0.67342, 0.79161, 0.65263, 0.019694, 0.0034744, 0.013553, 0.005248, 0.005248, 0.005248
146, 0.029669, 0.0048091, 0.015629, 0.875, 0.67419, 0.79202, 0.65293, 0.01968, 0.0034723, 0.01351, 0.005215, 0.005215, 0.005215
147, 0.029889, 0.0048393, 0.015743, 0.87275, 0.67638, 0.79238, 0.65355, 0.019671, 0.0034701, 0.013469, 0.005182, 0.005182, 0.005182
148, 0.030303, 0.004692, 0.014702, 0.87847, 0.67445, 0.79274, 0.65362, 0.019665, 0.0034678, 0.013428, 0.005149, 0.005149, 0.005149
149, 0.030168, 0.004706, 0.015006, 0.88225, 0.67328, 0.79311, 0.65403, 0.019655, 0.003466, 0.013395, 0.005116, 0.005116, 0.005116
150, 0.029806, 0.0046252, 0.014383, 0.88195, 0.67364, 0.79356, 0.65466, 0.019643, 0.0034622, 0.013358, 0.005083, 0.005083, 0.005083
151, 0.029649, 0.0046202, 0.01415, 0.88847, 0.67084, 0.79402, 0.65506, 0.019639, 0.0034579, 0.01332, 0.00505, 0.00505, 0.00505
152, 0.02953, 0.0045877, 0.014023, 0.89276, 0.66953, 0.7947, 0.65564, 0.019624, 0.0034543, 0.013279, 0.005017, 0.005017, 0.005017
153, 0.029266, 0.004573, 0.013272, 0.89334, 0.67037, 0.79559, 0.65636, 0.019612, 0.0034493, 0.013235, 0.004984, 0.004984, 0.004984
154, 0.029439, 0.0046827, 0.014827, 0.89534, 0.66967, 0.79612, 0.657, 0.0196, 0.0034459, 0.013198, 0.004951, 0.004951, 0.004951
155, 0.029172, 0.004629, 0.014836, 0.885, 0.67589, 0.79654, 0.65756, 0.019587, 0.0034425, 0.013164, 0.004918, 0.004918, 0.004918
156, 0.029751, 0.0047398, 0.015841, 0.88817, 0.67561, 0.79701, 0.65807, 0.019583, 0.0034404, 0.01313, 0.004885, 0.004885, 0.004885
157, 0.02981, 0.0048484, 0.016958, 0.89535, 0.67148, 0.79726, 0.65808, 0.019582, 0.0034387, 0.013106, 0.004852, 0.004852, 0.004852
158, 0.029677, 0.0046674, 0.013917, 0.89434, 0.67234, 0.79741, 0.65835, 0.019586, 0.0034382, 0.013071, 0.004819, 0.004819, 0.004819
159, 0.029464, 0.0045948, 0.013542, 0.89265, 0.67363, 0.79786, 0.65883, 0.019586, 0.0034363, 0.013031, 0.004786, 0.004786, 0.004786
160, 0.029218, 0.004708, 0.014342, 0.8856, 0.67784, 0.79818, 0.65933, 0.019582, 0.0034349, 0.012994, 0.004753, 0.004753, 0.004753
161, 0.029343, 0.0046623, 0.014719, 0.88885, 0.67613, 0.79853, 0.6598, 0.019574, 0.0034322, 0.012953, 0.00472, 0.00472, 0.00472
162, 0.029323, 0.0046967, 0.014958, 0.89266, 0.67382, 0.79884, 0.65984, 0.019562, 0.0034307, 0.012922, 0.004687, 0.004687, 0.004687
163, 0.029396, 0.0046844, 0.014559, 0.88638, 0.67755, 0.79911, 0.66011, 0.019551, 0.003429, 0.012892, 0.004654, 0.004654, 0.004654
164, 0.029645, 0.0048339, 0.015533, 0.87965, 0.68065, 0.79908, 0.65998, 0.01954, 0.0034278, 0.012879, 0.004621, 0.004621, 0.004621
165, 0.029143, 0.0048531, 0.016001, 0.88006, 0.68057, 0.79925, 0.6603, 0.019527, 0.0034253, 0.012857, 0.004588, 0.004588, 0.004588
166, 0.029355, 0.0046257, 0.014049, 0.8832, 0.67887, 0.79965, 0.6608, 0.019518, 0.0034221, 0.012822, 0.004555, 0.004555, 0.004555
167, 0.029609, 0.0049048, 0.017226, 0.87486, 0.68331, 0.7995, 0.66086, 0.019501, 0.0034224, 0.012811, 0.004522, 0.004522, 0.004522
168, 0.029227, 0.0046878, 0.014494, 0.88423, 0.67854, 0.79962, 0.66086, 0.019495, 0.0034235, 0.012791, 0.004489, 0.004489, 0.004489
169, 0.028963, 0.0046821, 0.014431, 0.88801, 0.67668, 0.79974, 0.66112, 0.019491, 0.003425, 0.012767, 0.004456, 0.004456, 0.004456
170, 0.028792, 0.0046551, 0.014323, 0.88618, 0.67864, 0.80019, 0.66131, 0.019481, 0.0034249, 0.01274, 0.004423, 0.004423, 0.004423
171, 0.028833, 0.0047184, 0.014769, 0.88584, 0.6792, 0.80029, 0.66137, 0.019477, 0.0034259, 0.012709, 0.00439, 0.00439, 0.00439
172, 0.029024, 0.0045442, 0.013321, 0.89036, 0.67712, 0.8006, 0.66162, 0.019467, 0.0034259, 0.01268, 0.004357, 0.004357, 0.004357
173, 0.02841, 0.0045506, 0.013934, 0.87756, 0.68475, 0.80094, 0.66228, 0.019454, 0.0034232, 0.012654, 0.004324, 0.004324, 0.004324
174, 0.02903, 0.0047525, 0.014831, 0.8811, 0.68269, 0.8014, 0.66284, 0.019439, 0.0034217, 0.012631, 0.004291, 0.004291, 0.004291
175, 0.028494, 0.0045341, 0.013479, 0.86662, 0.69268, 0.80191, 0.66313, 0.019424, 0.0034192, 0.0126, 0.004258, 0.004258, 0.004258
176, 0.028275, 0.0044714, 0.012635, 0.87422, 0.68897, 0.80257, 0.66377, 0.019411, 0.003415, 0.012558, 0.004225, 0.004225, 0.004225
177, 0.028392, 0.0044671, 0.012628, 0.8761, 0.68958, 0.80325, 0.66449, 0.019393, 0.0034106, 0.012516, 0.004192, 0.004192, 0.004192
178, 0.028098, 0.0045219, 0.013251, 0.87967, 0.68822, 0.80392, 0.66485, 0.019379, 0.003406, 0.012475, 0.004159, 0.004159, 0.004159
179, 0.028316, 0.0045562, 0.013679, 0.87757, 0.6905, 0.80439, 0.66541, 0.01936, 0.0034017, 0.01243, 0.004126, 0.004126, 0.004126
180, 0.028266, 0.0045204, 0.013536, 0.87832, 0.69125, 0.80493, 0.66603, 0.019342, 0.0033985, 0.012389, 0.004093, 0.004093, 0.004093
181, 0.028115, 0.0045113, 0.013767, 0.87478, 0.69366, 0.8056, 0.66674, 0.019323, 0.0033951, 0.012351, 0.00406, 0.00406, 0.00406
182, 0.028973, 0.0045893, 0.014179, 0.87357, 0.69558, 0.80605, 0.66693, 0.019307, 0.0033911, 0.012299, 0.004027, 0.004027, 0.004027
183, 0.028398, 0.0045255, 0.01315, 0.86279, 0.70187, 0.80665, 0.66747, 0.019291, 0.0033867, 0.012239, 0.003994, 0.003994, 0.003994
184, 0.028347, 0.0046116, 0.014029, 0.86156, 0.70312, 0.80708, 0.66819, 0.01928, 0.0033823, 0.012213, 0.003961, 0.003961, 0.003961
185, 0.028126, 0.0044354, 0.012803, 0.86827, 0.70004, 0.80771, 0.66844, 0.019264, 0.0033783, 0.012179, 0.003928, 0.003928, 0.003928
186, 0.028092, 0.0044288, 0.012635, 0.85921, 0.70728, 0.80816, 0.6688, 0.019249, 0.0033743, 0.012144, 0.003895, 0.003895, 0.003895
187, 0.028219, 0.0044341, 0.012455, 0.8597, 0.70751, 0.80868, 0.66937, 0.019237, 0.0033696, 0.012106, 0.003862, 0.003862, 0.003862
188, 0.028297, 0.0044604, 0.013685, 0.86853, 0.70219, 0.80926, 0.6699, 0.019225, 0.0033652, 0.012083, 0.003829, 0.003829, 0.003829
189, 0.028089, 0.0044908, 0.013035, 0.86344, 0.70547, 0.80969, 0.67069, 0.019209, 0.0033617, 0.012048, 0.003796, 0.003796, 0.003796
190, 0.028265, 0.0044647, 0.012995, 0.87179, 0.70153, 0.81014, 0.6713, 0.019195, 0.003359, 0.012001, 0.003763, 0.003763, 0.003763
191, 0.027968, 0.0044832, 0.013211, 0.87127, 0.70281, 0.81054, 0.67156, 0.019184, 0.0033564, 0.011958, 0.00373, 0.00373, 0.00373
192, 0.02819, 0.0044138, 0.012269, 0.87067, 0.70463, 0.81111, 0.67218, 0.01917, 0.0033532, 0.011915, 0.003697, 0.003697, 0.003697
193, 0.027483, 0.0044478, 0.012964, 0.87322, 0.7037, 0.81165, 0.67277, 0.019166, 0.0033503, 0.011878, 0.003664, 0.003664, 0.003664
194, 0.027975, 0.0043979, 0.01293, 0.87524, 0.70343, 0.81218, 0.67302, 0.01916, 0.0033473, 0.011833, 0.003631, 0.003631, 0.003631
195, 0.028067, 0.0044256, 0.012334, 0.88085, 0.70041, 0.81271, 0.67337, 0.019153, 0.003345, 0.011791, 0.003598, 0.003598, 0.003598
196, 0.0279, 0.0045048, 0.013608, 0.89154, 0.69518, 0.81295, 0.67396, 0.019145, 0.0033433, 0.011759, 0.003565, 0.003565, 0.003565
197, 0.02758, 0.0044779, 0.013187, 0.88395, 0.70034, 0.81364, 0.6745, 0.019134, 0.0033405, 0.011734, 0.003532, 0.003532, 0.003532
198, 0.027943, 0.0044789, 0.013491, 0.88299, 0.70136, 0.81395, 0.67496, 0.019127, 0.0033391, 0.011698, 0.003499, 0.003499, 0.003499
199, 0.027604, 0.0044764, 0.012396, 0.86991, 0.71011, 0.81444, 0.67534, 0.019111, 0.0033367, 0.011651, 0.003466, 0.003466, 0.003466
200, 0.027905, 0.0044609, 0.013532, 0.87429, 0.70821, 0.81511, 0.67561, 0.019101, 0.0033339, 0.011603, 0.003433, 0.003433, 0.003433
201, 0.027205, 0.0044224, 0.012354, 0.8785, 0.70566, 0.81534, 0.6761, 0.019092, 0.0033307, 0.011553, 0.0034, 0.0034, 0.0034
202, 0.027364, 0.004326, 0.011794, 0.87383, 0.70961, 0.81579, 0.67664, 0.019081, 0.0033271, 0.011496, 0.003367, 0.003367, 0.003367
203, 0.027034, 0.0043808, 0.012486, 0.88445, 0.70409, 0.81623, 0.67716, 0.019069, 0.0033234, 0.01144, 0.003334, 0.003334, 0.003334
204, 0.027096, 0.0043339, 0.012473, 0.88645, 0.70395, 0.81675, 0.67764, 0.019056, 0.0033205, 0.01145, 0.003301, 0.003301, 0.003301
205, 0.02718, 0.0044947, 0.013286, 0.88663, 0.70472, 0.817, 0.67766, 0.019045, 0.0033171, 0.011448, 0.003268, 0.003268, 0.003268
206, 0.02737, 0.0043754, 0.012324, 0.88532, 0.70629, 0.81732, 0.67782, 0.019033, 0.0033138, 0.011418, 0.003235, 0.003235, 0.003235
207, 0.026862, 0.0043414, 0.011856, 0.88522, 0.70735, 0.81771, 0.67826, 0.019021, 0.0033098, 0.011374, 0.003202, 0.003202, 0.003202
208, 0.027041, 0.0043822, 0.012505, 0.88641, 0.70764, 0.8183, 0.679, 0.019005, 0.0033065, 0.011301, 0.003169, 0.003169, 0.003169
209, 0.027375, 0.0044019, 0.012307, 0.88915, 0.70627, 0.81876, 0.67965, 0.018992, 0.0033027, 0.01124, 0.003136, 0.003136, 0.003136
210, 0.026408, 0.0042864, 0.011387, 0.88826, 0.7068, 0.81916, 0.68025, 0.018979, 0.003299, 0.011185, 0.003103, 0.003103, 0.003103
211, 0.026816, 0.004366, 0.01286, 0.88913, 0.70698, 0.81964, 0.68057, 0.018966, 0.0032956, 0.011134, 0.00307, 0.00307, 0.00307
212, 0.026834, 0.0044753, 0.013064, 0.89418, 0.70444, 0.81979, 0.68096, 0.018962, 0.0032933, 0.01108, 0.003037, 0.003037, 0.003037
213, 0.026738, 0.0043588, 0.01216, 0.89288, 0.70576, 0.82012, 0.68117, 0.018951, 0.0032915, 0.011037, 0.003004, 0.003004, 0.003004
214, 0.026799, 0.0042897, 0.012575, 0.88061, 0.71423, 0.8204, 0.68118, 0.01894, 0.0032887, 0.011005, 0.002971, 0.002971, 0.002971
215, 0.026789, 0.0042323, 0.01162, 0.88217, 0.71481, 0.821, 0.68169, 0.018924, 0.0032853, 0.010981, 0.002938, 0.002938, 0.002938
216, 0.026271, 0.0042765, 0.011859, 0.88071, 0.71599, 0.82138, 0.68209, 0.018914, 0.003282, 0.010951, 0.002905, 0.002905, 0.002905
217, 0.026506, 0.0042575, 0.011895, 0.89548, 0.70758, 0.82185, 0.68266, 0.018899, 0.0032787, 0.010915, 0.002872, 0.002872, 0.002872
218, 0.026573, 0.0042308, 0.011324, 0.8798, 0.71738, 0.82225, 0.68307, 0.018888, 0.003275, 0.010867, 0.002839, 0.002839, 0.002839
219, 0.026802, 0.0042771, 0.011784, 0.90278, 0.70469, 0.82267, 0.68341, 0.018875, 0.0032717, 0.010832, 0.002806, 0.002806, 0.002806
220, 0.026294, 0.0042496, 0.012278, 0.88689, 0.71446, 0.82299, 0.68382, 0.018861, 0.0032689, 0.010801, 0.002773, 0.002773, 0.002773
221, 0.026162, 0.0041922, 0.011727, 0.88652, 0.71544, 0.82347, 0.68423, 0.018849, 0.0032651, 0.010765, 0.00274, 0.00274, 0.00274
222, 0.026085, 0.0041818, 0.011185, 0.87998, 0.72014, 0.82422, 0.68488, 0.018834, 0.0032612, 0.010727, 0.002707, 0.002707, 0.002707
223, 0.025764, 0.0042888, 0.012347, 0.88198, 0.71947, 0.82454, 0.68545, 0.018818, 0.003259, 0.010698, 0.002674, 0.002674, 0.002674
224, 0.026048, 0.0042901, 0.011907, 0.88291, 0.71991, 0.82483, 0.68547, 0.018802, 0.0032568, 0.010663, 0.002641, 0.002641, 0.002641
225, 0.025829, 0.0042861, 0.011404, 0.88415, 0.7199, 0.82507, 0.686, 0.018789, 0.0032535, 0.01063, 0.002608, 0.002608, 0.002608
226, 0.026066, 0.0042921, 0.012395, 0.88502, 0.71965, 0.82533, 0.68627, 0.018781, 0.0032511, 0.010617, 0.002575, 0.002575, 0.002575
227, 0.025661, 0.0042114, 0.011332, 0.88562, 0.71944, 0.8256, 0.68681, 0.018774, 0.0032485, 0.01061, 0.002542, 0.002542, 0.002542
228, 0.025668, 0.0042967, 0.011914, 0.88481, 0.71983, 0.82577, 0.68685, 0.018762, 0.0032454, 0.010596, 0.002509, 0.002509, 0.002509
229, 0.025457, 0.0041623, 0.01098, 0.90693, 0.7071, 0.82607, 0.68704, 0.018757, 0.003242, 0.010577, 0.002476, 0.002476, 0.002476
230, 0.025143, 0.0041684, 0.010599, 0.88612, 0.71991, 0.82641, 0.6873, 0.018744, 0.0032381, 0.010558, 0.002443, 0.002443, 0.002443
231, 0.025387, 0.0041142, 0.011331, 0.90645, 0.70892, 0.82669, 0.68776, 0.018737, 0.0032354, 0.010545, 0.00241, 0.00241, 0.00241
232, 0.025271, 0.0041676, 0.010882, 0.90885, 0.70837, 0.82711, 0.68817, 0.018728, 0.0032321, 0.010513, 0.002377, 0.002377, 0.002377
233, 0.025422, 0.0041519, 0.010758, 0.91081, 0.70795, 0.8274, 0.68849, 0.018716, 0.0032283, 0.010493, 0.002344, 0.002344, 0.002344
234, 0.025042, 0.0041172, 0.010893, 0.90996, 0.70908, 0.82769, 0.68903, 0.018705, 0.0032258, 0.010481, 0.002311, 0.002311, 0.002311
235, 0.025003, 0.0041943, 0.011081, 0.91139, 0.70889, 0.82817, 0.68926, 0.01869, 0.0032234, 0.010455, 0.002278, 0.002278, 0.002278
236, 0.025495, 0.0041886, 0.011161, 0.90653, 0.71192, 0.82845, 0.68968, 0.018679, 0.0032202, 0.010438, 0.002245, 0.002245, 0.002245
237, 0.024853, 0.0041379, 0.01109, 0.90219, 0.71449, 0.82858, 0.68999, 0.018668, 0.0032175, 0.010422, 0.002212, 0.002212, 0.002212
238, 0.025359, 0.0042637, 0.011631, 0.89763, 0.71717, 0.82876, 0.69032, 0.018664, 0.0032149, 0.010398, 0.002179, 0.002179, 0.002179
239, 0.025138, 0.0041634, 0.010866, 0.89976, 0.71686, 0.82914, 0.69072, 0.018658, 0.0032125, 0.010367, 0.002146, 0.002146, 0.002146
240, 0.025191, 0.0042017, 0.01147, 0.90105, 0.71653, 0.82931, 0.69081, 0.018654, 0.0032102, 0.010338, 0.002113, 0.002113, 0.002113
241, 0.024415, 0.0040783, 0.010546, 0.90208, 0.71607, 0.82966, 0.69108, 0.018647, 0.0032065, 0.010307, 0.00208, 0.00208, 0.00208
242, 0.024771, 0.0041411, 0.010643, 0.90229, 0.71697, 0.82998, 0.69137, 0.018636, 0.0032033, 0.010276, 0.002047, 0.002047, 0.002047
243, 0.024879, 0.0041102, 0.010742, 0.90345, 0.7157, 0.83009, 0.69113, 0.018626, 0.0032001, 0.010245, 0.002014, 0.002014, 0.002014
244, 0.024687, 0.0040893, 0.010514, 0.90102, 0.71857, 0.83059, 0.69153, 0.018616, 0.0031977, 0.010224, 0.001981, 0.001981, 0.001981
245, 0.024453, 0.0040245, 0.009946, 0.90133, 0.71889, 0.83091, 0.69212, 0.018608, 0.0031937, 0.010202, 0.001948, 0.001948, 0.001948
246, 0.024697, 0.004068, 0.010138, 0.90193, 0.71892, 0.83113, 0.6926, 0.018597, 0.00319, 0.01018, 0.001915, 0.001915, 0.001915
247, 0.024104, 0.0040036, 0.0098895, 0.9028, 0.7194, 0.83159, 0.69295, 0.018585, 0.0031867, 0.010157, 0.001882, 0.001882, 0.001882
248, 0.024311, 0.0041231, 0.010796, 0.90309, 0.71971, 0.83193, 0.69327, 0.01858, 0.0031838, 0.010132, 0.001849, 0.001849, 0.001849
249, 0.024564, 0.0041038, 0.010224, 0.90306, 0.71991, 0.83241, 0.69372, 0.018572, 0.0031802, 0.010103, 0.001816, 0.001816, 0.001816
250, 0.023878, 0.0040245, 0.010154, 0.90286, 0.72076, 0.83287, 0.69407, 0.018562, 0.0031758, 0.010073, 0.001783, 0.001783, 0.001783
251, 0.024395, 0.0040523, 0.010766, 0.90464, 0.71976, 0.8331, 0.69441, 0.018554, 0.0031725, 0.01007, 0.00175, 0.00175, 0.00175
252, 0.023987, 0.0040441, 0.009975, 0.89771, 0.72504, 0.83346, 0.69491, 0.018548, 0.0031685, 0.010077, 0.001717, 0.001717, 0.001717
253, 0.023958, 0.0039825, 0.0099026, 0.90173, 0.72389, 0.83378, 0.69496, 0.018537, 0.0031654, 0.010077, 0.001684, 0.001684, 0.001684
254, 0.023851, 0.0039409, 0.0098038, 0.90892, 0.72011, 0.83425, 0.69554, 0.018527, 0.0031616, 0.010087, 0.001651, 0.001651, 0.001651
255, 0.023708, 0.0040062, 0.010849, 0.90224, 0.7249, 0.83458, 0.69571, 0.018517, 0.0031589, 0.010091, 0.001618, 0.001618, 0.001618
256, 0.023637, 0.0039904, 0.010232, 0.90757, 0.72244, 0.83481, 0.69606, 0.01851, 0.0031562, 0.010077, 0.001585, 0.001585, 0.001585
257, 0.023538, 0.0039196, 0.0096139, 0.90594, 0.72435, 0.83514, 0.69628, 0.0185, 0.0031535, 0.010053, 0.001552, 0.001552, 0.001552
258, 0.023372, 0.0040115, 0.0098075, 0.90904, 0.72332, 0.83556, 0.69681, 0.01849, 0.0031515, 0.010032, 0.001519, 0.001519, 0.001519
259, 0.023071, 0.0039129, 0.0094614, 0.90798, 0.72446, 0.8358, 0.6973, 0.018476, 0.0031483, 0.010016, 0.001486, 0.001486, 0.001486
260, 0.023335, 0.0039356, 0.0098765, 0.90549, 0.72739, 0.8363, 0.69757, 0.018465, 0.0031453, 0.010006, 0.001453, 0.001453, 0.001453
261, 0.023355, 0.0039533, 0.0097373, 0.90543, 0.72807, 0.83655, 0.69784, 0.018457, 0.0031423, 0.0099884, 0.00142, 0.00142, 0.00142
262, 0.023173, 0.0038814, 0.0094556, 0.9022, 0.73081, 0.83708, 0.69852, 0.018449, 0.0031393, 0.0099691, 0.001387, 0.001387, 0.001387
263, 0.022969, 0.0039175, 0.0094699, 0.90072, 0.73256, 0.83735, 0.6989, 0.018439, 0.0031365, 0.0099656, 0.001354, 0.001354, 0.001354
264, 0.022732, 0.0038617, 0.0093456, 0.90198, 0.73203, 0.83767, 0.6992, 0.018434, 0.0031332, 0.0099503, 0.001321, 0.001321, 0.001321
265, 0.022798, 0.0037935, 0.0092447, 0.90197, 0.73286, 0.83808, 0.69939, 0.018426, 0.0031298, 0.0099482, 0.001288, 0.001288, 0.001288
266, 0.022469, 0.0038351, 0.0090556, 0.90257, 0.73256, 0.83836, 0.69983, 0.018418, 0.003126, 0.0099335, 0.001255, 0.001255, 0.001255
267, 0.022526, 0.0038402, 0.0090142, 0.90367, 0.73213, 0.83863, 0.70025, 0.01841, 0.003123, 0.0099296, 0.001222, 0.001222, 0.001222
268, 0.022521, 0.0037604, 0.0088468, 0.90334, 0.73282, 0.83898, 0.70062, 0.018395, 0.0031187, 0.009916, 0.001189, 0.001189, 0.001189
269, 0.022164, 0.0037553, 0.0087953, 0.90895, 0.73013, 0.83948, 0.70106, 0.018383, 0.0031149, 0.0099, 0.001156, 0.001156, 0.001156
270, 0.022302, 0.0038188, 0.0088961, 0.90912, 0.73083, 0.83986, 0.70125, 0.018371, 0.0031116, 0.0098842, 0.001123, 0.001123, 0.001123
271, 0.022217, 0.003799, 0.0088319, 0.90955, 0.73091, 0.84019, 0.7019, 0.01836, 0.0031077, 0.0098568, 0.00109, 0.00109, 0.00109
272, 0.022204, 0.0037048, 0.0087159, 0.91319, 0.72971, 0.84054, 0.7021, 0.018351, 0.0031049, 0.0098304, 0.001057, 0.001057, 0.001057
273, 0.021826, 0.0037491, 0.0085838, 0.91365, 0.7296, 0.84098, 0.70252, 0.018339, 0.0031017, 0.009798, 0.001024, 0.001024, 0.001024
274, 0.021465, 0.0037103, 0.0085357, 0.914, 0.72963, 0.84141, 0.70293, 0.018326, 0.0030983, 0.0097775, 0.000991, 0.000991, 0.000991
275, 0.021561, 0.0037047, 0.0086047, 0.90844, 0.73335, 0.84168, 0.7033, 0.018322, 0.0030957, 0.0097627, 0.000958, 0.000958, 0.000958
276, 0.02168, 0.0036804, 0.0084067, 0.90803, 0.73425, 0.84206, 0.70398, 0.018313, 0.003092, 0.0097405, 0.000925, 0.000925, 0.000925
277, 0.021412, 0.0036595, 0.0085608, 0.89973, 0.74041, 0.84237, 0.70436, 0.018303, 0.0030894, 0.0097202, 0.000892, 0.000892, 0.000892
278, 0.021379, 0.0036755, 0.0085213, 0.90422, 0.73851, 0.84279, 0.70488, 0.018291, 0.0030864, 0.0096985, 0.000859, 0.000859, 0.000859
279, 0.021257, 0.0036746, 0.0083755, 0.90334, 0.73957, 0.84305, 0.70511, 0.01828, 0.0030838, 0.0096725, 0.000826, 0.000826, 0.000826
280, 0.020995, 0.0036793, 0.0082731, 0.90475, 0.73979, 0.84334, 0.70558, 0.01827, 0.0030806, 0.0096496, 0.000793, 0.000793, 0.000793
281, 0.02095, 0.0036471, 0.0082025, 0.90763, 0.73882, 0.84368, 0.70591, 0.018263, 0.0030777, 0.009621, 0.00076, 0.00076, 0.00076
282, 0.02075, 0.0035969, 0.0083073, 0.90675, 0.74035, 0.84416, 0.70597, 0.018255, 0.0030745, 0.0095917, 0.000727, 0.000727, 0.000727
283, 0.020712, 0.0036154, 0.0081972, 0.90728, 0.74112, 0.84444, 0.70644, 0.018248, 0.003072, 0.0095615, 0.000694, 0.000694, 0.000694
284, 0.020743, 0.0036614, 0.008176, 0.91021, 0.7395, 0.84478, 0.70657, 0.018241, 0.0030696, 0.0095361, 0.000661, 0.000661, 0.000661
285, 0.020447, 0.0035952, 0.0081307, 0.90565, 0.74237, 0.8451, 0.70688, 0.018235, 0.0030672, 0.0095091, 0.000628, 0.000628, 0.000628
286, 0.020352, 0.0036225, 0.0079415, 0.90728, 0.74148, 0.84542, 0.7073, 0.018227, 0.0030641, 0.0094885, 0.000595, 0.000595, 0.000595
287, 0.020107, 0.0035801, 0.0079622, 0.90978, 0.73999, 0.84558, 0.70771, 0.018224, 0.0030618, 0.009461, 0.000562, 0.000562, 0.000562
288, 0.020113, 0.0035625, 0.007827, 0.91324, 0.73873, 0.84588, 0.70797, 0.018216, 0.0030584, 0.0094275, 0.000529, 0.000529, 0.000529
289, 0.020102, 0.0035462, 0.0078679, 0.91347, 0.73902, 0.84617, 0.70843, 0.018211, 0.0030563, 0.0093967, 0.000496, 0.000496, 0.000496
290, 0.019723, 0.0035561, 0.007874, 0.91413, 0.73879, 0.84636, 0.7087, 0.018206, 0.0030537, 0.0093633, 0.000463, 0.000463, 0.000463
291, 0.019586, 0.003598, 0.0077383, 0.90863, 0.74279, 0.8466, 0.70924, 0.018201, 0.0030511, 0.0093388, 0.00043, 0.00043, 0.00043
292, 0.019334, 0.003496, 0.007661, 0.91775, 0.73778, 0.84683, 0.70941, 0.018191, 0.0030489, 0.0093137, 0.000397, 0.000397, 0.000397
293, 0.019356, 0.0035508, 0.0077956, 0.91807, 0.73799, 0.84707, 0.70967, 0.018188, 0.0030469, 0.0092924, 0.000364, 0.000364, 0.000364
294, 0.019264, 0.0035092, 0.0077458, 0.91742, 0.73873, 0.84727, 0.70975, 0.018183, 0.0030443, 0.0092659, 0.000331, 0.000331, 0.000331
295, 0.019174, 0.0034744, 0.0074608, 0.91612, 0.73933, 0.84755, 0.70996, 0.018176, 0.0030418, 0.009244, 0.000298, 0.000298, 0.000298
296, 0.01921, 0.003498, 0.0075991, 0.91606, 0.73941, 0.84774, 0.71024, 0.01817, 0.0030398, 0.0092163, 0.000265, 0.000265, 0.000265
297, 0.018812, 0.0034964, 0.007491, 0.90979, 0.74374, 0.84804, 0.71049, 0.018164, 0.0030372, 0.0091905, 0.000232, 0.000232, 0.000232
298, 0.018673, 0.0034746, 0.0074683, 0.91274, 0.74207, 0.84819, 0.71087, 0.018159, 0.0030349, 0.0091677, 0.000199, 0.000199, 0.000199
299, 0.01854, 0.0034561, 0.0072729, 0.91246, 0.74268, 0.84839, 0.71101, 0.018156, 0.0030323, 0.0091422, 0.000166, 0.000166, 0.000166
训练曲线如下
训练混淆矩阵如下
准确率曲线
召回率曲线
2.4.7 TT100K中国交通标志验证测试
class CPcbDefectCnnModel(object):
def __init__(self, model_path):
self.weights= model_path
self.data='data/tt100k.yaml'
self.imgsz=(640, 640)
self.conf_thres=0.5
self.iou_thres=0.45
# Load model
self.device = select_device()
print(self.device)
self.model = DetectMultiBackend(self.weights, device=self.device, dnn=self.dnn, data=self.data, fp16=self.half)
stride, self.names, pt = self.model.stride, self.model.names, self.model.pt
imgsz = check_img_size(self.imgsz, s=stride) # check image size
def predict(self, image_numpy_data):
# Padded resize
img = letterbox(image_numpy_data, 640, 32, True)[0]
print(img.shape)
# Convert
img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
img = np.ascontiguousarray(img)
pred = self.model(im, augment=self.augment, visualize=self.visualize)
pred = non_max_suppression(pred, self.conf_thres, self.iou_thres, self.classes)
detect_results = []
# Process predictions
for i, det in enumerate(pred): # per image
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(im.shape[2:], det[:, :4], image_numpy_data.shape).round()
detections = det.cpu().numpy()
for v in detections:
detect_results.append(v)
bboxes = []
scores = []
classIds = []
# [x,y,w,h,p,class]
for detection in detect_results:
print(detection)
score = detection[4]
classId = detection[5]
(x1, y1, x2, y2) = detection[:4]
bboxes.append([int(x1), int(y1), \
int(x2 - x1), int(y2 - y1)])
scores.append(float(score))
classIds.append(classId)
print(detect_results)
for *xyxy, conf, cls in reversed(det):
c = int(cls) # integer class
label = None if self.hide_labels else (self.names[c] if self.hide_conf else f'{self.names[c]} {conf:.2f}')
annotator.box_label(xyxy, label, color=colors(c, True))
return im0
模型验证结果如下：
觉得不错的小伙伴，感谢点赞、关注加收藏哦！更多干货内容持续更新…
代码下载链接
关注博主的G Z H【小蜜蜂视觉】，回复【TT100K】即可获取下载方式
参考文献
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[2] 改进YOLOv7的无人机图像小目标检测算法. 王晓宇;张丽辉;赵辉;张丽娟.电光与控制
[3] 基于改进YOLOv7的针织品表面小目标缺陷检测. 蒋立泉;邓宇轩;涂文章;余豪;何儒汉.棉纺织技术
[4] 改进YOLOv7的小目标检测方法. 冯泰梾;张雪松;宋存利;李光宇;金花.计算机工程与应用
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[7] 基于改进YOLOv7的风机叶片缺陷检测研究. 汤占军;张朝杰;王健;陆鹏;刘汇塬;蹇洪.北京航空航天大学学报
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